Jean Goedert
email: jean.goedert@protonmail.com

David Cochard

Arnaud Lenoble

Olivier Lorvelec

Benoît Pisanu

Aurélien Royer

Seasonal demography of different black rat (Rattus rattus) populations under contrasting natural habitats in Guadeloupe (Lesser Antilles, Caribbean)

Keywords: Black rat, Lesser Antilles, Ecology, Population, Precipitation Compliance with ethical standards

The black rat (Rattus rattus) is one of the most widespread rodents on islands worldwide, introduced over the last five centuries. However, reliable information concerning how biotic or abiotic factors influence key parameters of black rat population biology in insular contexts is currently unavailable. Here we aim to document the relative abundance of rat populations and evaluate how the age structure and the bodymass of adult individual vary seasonally in different forest environments under contrasting climatic conditions. Rats were captured during wet and dry seasons in 2017-2018 at one or two sites in each of the four natural forested environments of Guadeloupe, all of which experience widely different annual rainfall (semi-deciduous dry forest, seasonal evergreen forest, mountain rainforest and Pterocarpus officinalis swamp forest). A total of 171 black rats were captured during a 1018 trap-night effort. Overall capture results confirm this species to thrive in all the natural forested environments we investigated. With the exception of the P. officinalis swamp forest, black rat populations reach higher relative abundances during the wet season due to juvenile and sub-adult recruitment at the end of the dry season. In contrast, in the P. officinalis swamp forest, breeding activity continues during both seasons and relative rat abundance appears to fluctuate less seasonally. The relative abundance of adult black rats is also higher in the seasonal semi-evergreen and rainforests that experience little or no water stress. These contexts therefore appear the most favourable for sustaining black rat populations, a pattern that is most likely connected to a combination of climatic and/or edaphic parameters that condition the year-round availability and abundance of food resources.

Introduction

Three commensal rodents arrived on European ships and subsequently colonized Guadeloupe: the house mouse (Mus musculus), the Norwegian rat or brown rat (Rattus norvegicus) and the black rat or roof rat (Rattus rattus), the latter most likely arriving as early as the seventeenth if not the sixteenth century [START_REF] Pinchon | Late Quaternary vertebrate faunas of the Lesser Antilles: historical components of Caribbean biogeography[END_REF][START_REF] Pinchon | Late Quaternary vertebrate faunas of the Lesser Antilles: historical components of Caribbean biogeography[END_REF][START_REF] Lorvelec | Les mammifères terrestres non volants des Antilles françaises et l'introduction récente d'un écureuil[END_REF]). The black rat, also known as the ship rat or roof rat, is a medium-sized commensal rodent native to the Indian subcontinent that was introduced almost worldwide, including numerous islands, by European ships [START_REF] Atkinson | The spread of commensal species of Rattus to oceanic islands and their effects on island avifaunas[END_REF][START_REF] Pascal | Invasions Biologiques et Extinctions : 11 000 Ans d'Histoire des Vertébrés en France[END_REF][START_REF] Aplin | Multiple geographic origins of commensalism and complex dispersal history of black rats[END_REF]. Today, 80% of the world's archipelagos host black rat populations in climates ranging from tropical to sub-polar [START_REF] Sastre | Plantes, milieux et paysages des Antilles françaises : écologie, biologie, identification, protection et usages[END_REF]. This remarkably adaptable species can establish non-commensal populations in various environments, especially forests where it lives as a semi-arboreal species [START_REF] Quéré | Les rongeurs de France : Faunistique et biologie-3e édition revue et augmentée[END_REF]. The black rat is a highly opportunistic forager, which feeds primarily on fruits, seeds, arthropods, as well as numerous invertebrates and vertebrates [START_REF] Sastre | Plantes, milieux et paysages des Antilles françaises : écologie, biologie, identification, protection et usages[END_REF]. Black rats reproduce multiple times each year, with a relatively short gestation period (between 20 and 29 days; [START_REF] Ewer | The biology and behaviour of a free-living population of black rats (Rattus rattus). Animal behaviour monographs[END_REF][START_REF] Quéré | Les rongeurs de France : Faunistique et biologie-3e édition revue et augmentée[END_REF] and a high number of young per litter (up to ten; [START_REF] Quéré | Les rongeurs de France : Faunistique et biologie-3e édition revue et augmentée[END_REF]. These characteristics highlight the remarkable adaptability of black rats that allow them to successfully inhabit a broad range of environments.

Although previous research has primarily focused on its social behaviour in captive colonies [START_REF] Barnett | An analysis of social behaviour in wild rats[END_REF][START_REF] Ewer | The biology and behaviour of a free-living population of black rats (Rattus rattus). Animal behaviour monographs[END_REF]) and biology in commensal environments, particularly its role as a vector of diseases (e.g. [START_REF] Meerburg | Rodent-borne diseases and their risks for public health[END_REF][START_REF] Himsworth | The characteristics of wild rat (Rattus spp.) populations from an inner-city neighborhood with a focus on factors critical to the understanding of rat-associated zoonoses[END_REF], in crop destruction (e.g. [START_REF] Pimentel | Environmental and economic costs of nonindigenous species in the United States[END_REF][START_REF] Pimentel | Economic and environmental threats of alien plant, animal, and microbe invasions[END_REF][START_REF] Singleton | Impacts of rodents on rice production in Asia. IRRI, Los Baños Stapp P, Polis GA (2003a) Marine resources subsidize insular rodent populations in the Gulf of California, Mexico[END_REF] or as invasive species (e.g. [START_REF] Borroto-Páez | Invasive mammals in Cuba: an overview[END_REF][START_REF] Nowak | Walker's Mammals of the World[END_REF], relatively little has been reported on non-commensal populations in natural habitats (although see Harper and Bunbury 2015). Human dominated environments are less prone to food resource fluctuations than are natural ones, where the availability of food can vary substantially according to climatic conditions and seasonality. This explains why black rats in urban environments have a more rapid growth rate, reach sexual maturity quicker and are able to reproduce year-round compared with populations in natural environments [START_REF] Himsworth | The characteristics of wild rat (Rattus spp.) populations from an inner-city neighborhood with a focus on factors critical to the understanding of rat-associated zoonoses[END_REF]. In these latter contexts, the climate has been shown to be an important driver of rodent population dynamics (e.g. [START_REF] Ernest | Rodents, plants, and precipitation: spatial and temporal dynamics of consumers and resources[END_REF][START_REF] Rousteau | The influence of spatio-temporal resource fluctuations on insular rat population dynamics[END_REF]. More specifically, several studies have highlighted the impact of rain fall on vegetation and food resources indirectly regulate black rat biology on tropical islands (e.g. [START_REF] Tamarin | The population biology of Hawaiian rodents: demographic parameters[END_REF][START_REF] Clark | Population ecology of Rattus rattus across a desert montane forest gradient in the Galapagos Islands[END_REF]Harper and Bunbury 2015). These studies have, however, mainly focused on Indian and Pacific island contexts, with little data available for the Caribbean. In fact, information concerning the biology of black rat populations in natural environments of the Caribbean derives primarily from research conducted in the early 1980s at two sites on Guadeloupe (Delattre andLe Louarn 1980, 1981).

Here we investigate the influence of rainfall regime and differing forested habitats on the demographic patterns of this invasive rodent on Guadeloupe during the wet and dry seasons based on data collected from trapped black rats. We hypothesize that forested habitats with marked hydric seasonality should support more variable black rat populations whose size varies seasonally compared with the climatically more stable forested habitats.

Material and methods

Studied sites

Guadeloupe is an island in the Lesser Antilles, this archipelago forming one of the world's biodiversity hotspots [START_REF] Myers | Biodiversity hotspots for conservation priorities[END_REF]. The main island, Guadeloupe, comprises two adjoining islands: the volcanic and mountainous Basse-Terre, to the west, and the carbonated (limestone) lowlying Grande-Terre, to the east (Fig. 1).

Prior to European colonisation, Guadeloupe was likely covered almost entirely by forest [START_REF] Rollet | Structures, flores, dynamiques : réponses des forêts pluviales des Petites Antilles aux milieux montagnards. Phytogéographie tropicale : réalités et perspectives[END_REF], an important part of which has since been deforested, especially in densely populated low-lying areas [START_REF] Lugo | Tropical forests in the Caribbean[END_REF], including selectively harvested old-growth forests. Natural forests do still remain in Guadeloupe and vary according to soil conditions and climatic factors (for details, see [START_REF] Lasserre | La Guadeloupe, étude géographique[END_REF][START_REF] Rousteau | The influence of spatio-temporal resource fluctuations on insular rat population dynamics[END_REF][START_REF] Sastre | Plantes, milieux et paysages des Antilles françaises : écologie, biologie, identification, protection et usages[END_REF][START_REF] Rollet | Structures, flores, dynamiques : réponses des forêts pluviales des Petites Antilles aux milieux montagnards. Phytogéographie tropicale : réalités et perspectives[END_REF]. Today, five main forest formations are present on Guadeloupe [START_REF] Rousteau | The influence of spatio-temporal resource fluctuations on insular rat population dynamics[END_REF] and Fig. 1). Rats were captured from 7 sites within these five formations during two capture sessions. [START_REF] Rousteau | The influence of spatio-temporal resource fluctuations on insular rat population dynamics[END_REF] and [START_REF] Sastre | Plantes, milieux et paysages des Antilles françaises : écologie, biologie, identification, protection et usages[END_REF] [START_REF] Imbert | Floristics and structure of the Pterocarpus officinalis swamp forest in Guadeloupe, Lesser Antilles[END_REF]. Moderate seasonal variations mean that P. officinalis flowers and fruits throughout the year (two extensive blooming periods generally occur from July to September and from November to January). The swamp forest is the most productive forest in terms of fine litterfall [START_REF] Migeot | Phenology and production of litter in a Pterocarpus officinalis (Jacq.) swamp forest of Guadeloupe (Lesser Antilles)[END_REF], which sustains a rich and diversefauna [START_REF] Feagin | Species composition and differences in diversity among the Pterocarpus officinalis forested wetland Feng AY, Himsworth CG (2014) The secret life of the city rat: a review of the ecology of urban Norway and black rats (Rattus norvegicus and Rattus rattus)[END_REF].

Figure 1. Vegetation maps of Guadeloupe (French West Indies) modified after

All capture sites were located one to several hundred meters away from all human activities (e.g. habitations, crops, picnic areas) in order to best reflect the diversity of natural environmental conditions on Guadeloupe. During the second capture session in March 2018 (see below), several traps were set in agricultural areas close to the selected sites: two large fields dedicated to the large-scale production of sugarcane near Piton Sainte-Rose and Barre de Cadoue and a smaller sugarcane farm next to a Creole garden, near Espérance. For Barre de Cadoue, a supplementary test was carried out not far from "La Porte d'Enfer", at the edges of a marshy area.

Capture session and climatic conditions

Two capture sessions were conducted in order to record potential seasonal changes in black rat population biology and dynamic. The first one was carried out during the first 2 weeks of October 2017 (04/10-13/10), which corresponds to near the end of the wet season. The second was carried out 5 months later during the middle of the dry season in March 2018 (14/03-27/03). The cyclone season in Guadeloupe usually falls just before the beginning of the dry season that spans from June to November. In 2017, the region experienced significant cyclone activity, with the annual average rainfall exceeding the norm (see Fig. 1). Records from Météo-France weather stations in Guadeloupe indicates that rainfall from November 2017 to January 2018 was slightly lower than expected and was accompanied by slightly warmer conditions compared with normal temperatures for the season (from+ 0.1 to + 0.4 °C, Fig. 1).

Capture protocol

Sixty rocker rat traps (Manufrance™; wire mesh; 26 × 10 × 10 cm) were set along 1 to 3 parallel rows depending on the characteristics of each site (accessibility, slope). Traps were placed every 15 m along rows separated by 20 m. GPS data was recorded for each trap. During each capture session, traps were set in the same locations according to the same configuration. Traps were initially set at the end of afternoon and baited with a mixture of organic oats, peanut butter and sardine oil. Each trap was visited the next morning and recorded either as open (with or without bait) or sprung (rat, mongoose (Urva auropunctata), hermit crab (Coenobita clypeatus) or empty with or without bait). Rats were systematically killed immediately after being removed from the trap, with the traps subsequently rebaited and reset the same morning. Depending on the site and session of capture, the trap-night effort varied from one to two nights, except for Gros Morne de Deshaies and Barre de Cadoue during the second session, when traps were set for 3 and 5 nights, respectively (SI 1). In the latter case, baits were resupplied every two nights.

Biological data

The length (nose to anus and anus to tip of tail) and weight (total and eviscerated) were recorded for each captured rat, except for five who escaped. Although it has no taxonomic value, morphological information was also recorded (rattus, alexandrinus or frugivorous) to evaluate potential correlations between pelage colouration and the environment. All individuals were sexed, and the length of the testicles and number of placental scars or the number of embryos were recorded in order to determine the reproductive maturity for male and female specimens, respectively.

Both eyes were removed to determine the relative age of the cohort within each population [START_REF] Lord | The lens as an indicator of age in cottontail rabbits[END_REF][START_REF] Augusteyn | Growth of the lens: in vitro observations[END_REF][START_REF] Augusteyn | Growth of the eye lens: I Weight accumulation in multiple species[END_REF]. Eye lenses were recovered through dissection, then fixed in formalin (12%) for at least 2 months and subsequently dried at 80 °C for 24 h. Eppendorf vials were hermetically sealed in an oven following desiccation to avoid the lenses rehydrating prior to be weighed. Both lenses were weighed with a precision of 10-5 g and their mean weight calculated.

The skeleton of each individual was cleaned and inventoried with a specimen number as part of the comparative anatomy collection of the PACEA laboratory (UMR-5199 of the CNRS) at the University of Bordeaux.

Trap success index

To describe the relative rat abundance between site and season of capture, we used the trap success index described by [START_REF] Nelson | Correction for sprung traps in catch/effort calculations of trapping results[END_REF]; the total number of captured black rats was divided by the total trap-night effort (trap-night effort = number of traps set × number of night), multiplied by 100 and corrected by half of traps, which became ineffective in the total trapping effort (i.e. sprung for any reason, including rat capture) [START_REF] Himsworth | The characteristics of wild rat (Rattus spp.) populations from an inner-city neighborhood with a focus on factors critical to the understanding of rat-associated zoonoses[END_REF].

A confidence interval (I) for a p value of 0.95% was calculated for each trap success index as:

I = 1,96 * *100
where p is the trap success index value divided by 100 and n is the total trap-night effort.

Statistical analyses

All analyses were performed using simple or mixed modelling methods [START_REF] Zuur | Mixed effects models and extensions in ecology with R[END_REF]) run in a Bayesian framework [START_REF] Zuur | A beginner's guide to GLM and GLMM with R: a Frequentist and Bayesian perspective for ecologists[END_REF] using the "brms" package [START_REF] Bürkner | brms: An R package for Bayesian multilevel models using Stan[END_REF][START_REF] Bürkner | Advanced Bayesian multilevel modeling with the R package brms[END_REF] for the R software version 3.5.3 (R Core Team 2019; see Online resource 2). Apart from the analysis of agerelated variation in corrected trapping rates by habitat and season that had too few replicated observations within sites, all models included a site random intercept to overcome heterogeneity in sample variance within trapping sites that may have biased coefficient estimations when comparing habitat or the influence of season across sites.

To define cohorts and separate juvenile, sub-adult and adult, we compared the reproductive state of each individual with their mean eye lens weight. Females with placental scars and/or embryos were considered sexually mature and coded as 1, while females without any evidence of reproduction were coded as 0. Males with testicular lengths greater than or equal to 2 cm were considered sexually mature and were coded as 1, while those with lengths less than 2 cm long were coded to 0. A generalized linear mixed model (GLMM) was generated for both female and male populations and adjusted with a binomial error (using a logit link function) adapted to presence/absence data.

A linear mixed model was used to test the significance of variations in adult rat mass (eviscerated weight) and to investigate the impact of habitat and season on this parameter and their interaction. The adjusted repeatability [START_REF] Nakagawa | Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists[END_REF] of the model was calculated to quantify the consistency in eviscerated body mass variation within sites when compared between habitats or season across sites.

A generalized linear mixed model adjusted with a betabinomial error (with a logit link function) adapted for modelling proportions based on a varying trial effort was employed to fit trapping rates corrected for sprung traps separating adult and juvenile plus sub-adult individuals according to habitat and season. No a priori probability was integrated in the statistical analyses. Gaussian priors were used to estimate the posterior distribution of model coefficients for the fixed part, and a half-Cauchy test (0, 25) was used for the random intercept estimates [START_REF] Zuur | A beginner's guide to GLM and GLMM with R: a Frequentist and Bayesian perspective for ecologists[END_REF]). Three chains were run for 20,000 iterations after a burn-in of 10,000 iterations and a thinning rate of 10, yielding a 3000 posterior sample of estimated coefficients. Model convergence was graphically and numerically checked for all analyses (Rhat < 1.001; Online resource 2). For the binomial and beta-binomial GLMM, the dispersion parameter was checked to be close to 1.00, ensuring an absence of heterogeneity in residual variance distribution, as well as for simple linear models that were graphically checked for homogeneity using Pearson residuals to fitted values relationship [START_REF] Zuur | Mixed effects models and extensions in ecology with R[END_REF][START_REF] Zuur | A beginner's guide to GLM and GLMM with R: a Frequentist and Bayesian perspective for ecologists[END_REF]. Detailed raw data used for statistical analyses along with step-by-step R codes are provided as online resources (Online resource 2).

Results

Capture data

The black rat was the only rodent species trapped during this study. Non-target species (mongoose and hermit crabs) also triggered traps in some instances, notably in the dry semi-deciduous forest sites. A total of 172 rats were captured during a 1018 trap-night effort. A 327 trap-night effort during the wet season (October 2017) resulted in the capture of 102 black rats, while a 691 trap-night effort during the dry season (March 2018) captured 69 black rats. SI 1 summarizes capture numbers along with the trap-night effort for each capture site and season as well as the relative age of individuals. Black rats were caught in the four natural environments; none was captured in a large industrial sugarcane field (trap-night effort = 80), whereas seven rats were caught in the sugarcane field forming part of the creole garden (trap-night effort = 20). Individuals from the semi-deciduous dry forests were mainly represented by the frugivorous morph; those from swamp forest were almost all alexandrinus, whereas those from the mountain rainforest tend to be mainly represented by the rattus morph.

The calculated trap success indices vary from 1.8% for Barre de Cadoue during the dry season to 73.2% for Bras David during the wet season (Fig. 2). Except for the swamp forest, the trap success index is consistently higher for the wet season than during the dry season, suggesting a greater population density for the former. Only Gros Morne de Deshaies and Maison de la Mangrove sites show trap success indices lower than 40% during the wet season. During the dry season, trap success indices are below 40%, especially for the semi-deciduous dry forest (< 7%), although the capture effort is greater for these two localities; seasonal evergreen forest sites show values ranging between 12 and 20%, while mountain rainforest sites show trap success index closer to 30%. [START_REF] Himsworth | The characteristics of wild rat (Rattus spp.) populations from an inner-city neighborhood with a focus on factors critical to the understanding of rat-associated zoonoses[END_REF] Relative age of black rats Complete biological information was recorded for 166 individuals. Figure 3 presents growth models for male and female rats based on eye lens weights. In both cases, significant relationships exist between reproductive maturity indices and eye lens weight (Online resource 3). Based on these two models, we graphically defined the sub-adult class as specimens with mean eye lens weights of between 20 and 30 mg (Fig. 3), while those with mean eye lens weight less than 20 mg or greater than 30 mg were attributed, respectively, to juvenile or adult age classes.

Variations in adult body weight

Eviscerated body weight of adult black rats does not differ between sexes (Online resource 3) and ranges between 80 g and 173 g (mean ± 1 SE: n = 57, 118 ± 3 g). Although eviscerated body weight of adults captured in the semi-deciduousdry forest tends to be lower than individuals from the other sites (supplementary material), no significant variation is evident according to habitat or season (Online resource 3). Eviscerated body weight was consistent between sites, with the site random intercept comprising on average 19.6% of the total variance estimated by the linear mixed model (Online resource 3, cf. Nakagawa and Schielzeth 2010).

Figure 3. Logistic curve for captured male and female individuals calculated from eye lens weight (mg), testicle length (for males) and reproductive state (for females) based on placental scars

Variations in age-related relative abundance according to environment Although variations in trap success indices are observable between seasons (Fig. 2), these variations should be assessed according to age class. During the wet season, corrected trapping rates for adult black rats ranged between 0.6 and 31.8%, while during the dry season rates ranged between 0.3 and 11.9% (Online resource 1). While this difference between seasons is evident visually, the associated probability is almost significant but still falls within the credible interval (Online resource 3). However, trapping rates for adult black rats were significantly lower in the semi-deciduous dry forest compared with the mountain rainforest (Online resource 3; Fig. 4).

Including juveniles and sub-adults, hereafter referred to as young black rats, the corrected trapping rates were strikingly different, being null during the dry season and ranging between 7.1 and 42.9% during the wet season (Online resource 1; Fig. 4), except in the swamp forest, where young black rats were trapped in equal numbers independent of the season.

Discussion

Numerous mammals occupy environments with seasonal variations that condition food availability, the photoperiod, climatic parameters (i.e. temperature, humidity) and energetic demands, which in turn influence several biological traits, including reproductive development and population density or growth (e.g. [START_REF] Bronson | Mammalian reproductive biology[END_REF][START_REF] Bronson | Climate change and seasonal reproduction in mammals[END_REF]. In Guadeloupe, seasonal contrasts in rainfall between dry and wet seasons, combined with the effects of local topography, shape the distribution of vegetation on the island and consequently local rat population biology and dynamics. Our results are in agreement with previous research on black rats in the Lesser Antilles (Delattre andLe Louarn 1980, 1981) and show that the density and breeding activity of black rat populations on Guadeloupe are largely controlled by seasonal rainfall. Moreover, our data provides new insights concerning the impact of environmental conditions on both relative population abundance and structure.

Population relative abundance

The black rat is a commensal species that thrives alongside humans and in natural environments throughout the world. Previous research [START_REF] Pascal | Rodent community structures in agricultural and natural ecosystems of Guadeloupe and Martinique (French West Indies)[END_REF]) described an uneven distribution of these species in Guadeloupe, with the black rat being less often associated with human activity than either the house mouse or Norwegian rat, both of which were found to be more abundant in urban and agricultural ecosystems, a pattern typical of tropical island contexts (e.g. Harper and Bunbury 2015). They also found that Norwegian rat was less abundant than black rat and virtually absent in natural environments where human pressure is low. Our capture data confirm this spatial heterogeneity in Guadeloupe; no Norwegian rats were captured despite an important trap-night effort of capture. This is consistent with the fact that black rats are the superior competitors in forest habitats [START_REF] Harper | Habitat use by three rat species (Rattus spp.) on an island without other mammalian predators[END_REF]. It is also worth noting that no black (or Norwegian) rats were captured in the two industrial sugarcane fields (Online resource 1). This is consistent with a previous study [START_REF] Pascal | Rodent community structures in agricultural and natural ecosystems of Guadeloupe and Martinique (French West Indies)[END_REF]) that equally reported a low capture success in this kind of crop. Several hypotheses may explain the absence of black rats in these contexts: the use of rat poison, sugarcane varieties resistant to rat attacks or our trapping strategy. For instance, our traps were positioned at the edges of the sugarcane fields, an area which has been previously reported to support a lower density of rats [START_REF] Lefebvre | Relationship of roof rat population indices with damage to sugarcane[END_REF]). We did, however, capture several black rats in a small sugarcane field within traditional creole gardens surrounded by forest near the capture site of Espérance. Therefore, another explanation is that pure sugarcane plantations present less suitable habitats for black rats. The limited number of trees compared with forest contexts would therefore account for their relative abundance in sugarcane fields next to forests. Similarly, Norway rats are highly vulnerable to predation by mongoose in this habitat as there are no refuges for them, unlike commensal areas where they are abundant. Mice may be present in forests with black rats but are unlikely to be detected without first removing black rats [START_REF] Harper | Response of mice (Mus musculus) to the removal of black rats (Rattus rattus) in arid forest on Santa Cruz Island, Galapagos[END_REF].

Previous work concerning the presence of the black rat in natural environments of Guadeloupe recorded it as occurring uniquely in deciduous dry and seasonal evergreen forests [START_REF] Delattre | Cycle de reproduction du rat noir (Rattus rattus) et du surmulot (Rattus norvegicus) dans différents milieux de la Guadeloupe (Antilles françaises)[END_REF]. The wide distribution of rats in the natural and anthropized environments in Guadeloupe was confirmed by subsequent capture sessions [START_REF] Lorvelec | Inventaire et statut des Mammifères des Antilles françaises (hors Chiroptères et Cétacés)[END_REF][START_REF] Lorvelec | Impacts des mammifères allochtones sur quelques espèces autochtones de l'îlet Fajou (réserve naturelle du Grand cul-de-sac marin, Guadeloupe), établis à l'issue d'une tentative d'éradication[END_REF]. Our data complements this analysis by further demonstrating the presence of the black rat in all the main types of forests on Guadeloupe, while also providing an estimate of its relative abundance for each forest context during both wet and dry seasons. Up until now, the only available trapping data for Guadeloupe was available in [START_REF] Pascal | Rodent community structures in agricultural and natural ecosystems of Guadeloupe and Martinique (French West Indies)[END_REF], who reported an uncorrected trap success index of 12.5% for a site located at the lower limits of the rainforest during the early dry season. This station is located along a forest track in a former logging area, and therefore, an environment that was likely to have been biologically depleted by the selective exploitation of tree species as well as soil erosion induced by deforestation. Nevertheless, this capture rate is comparable to what was recorded for the seasonal evergreen forest during the peak dry season. Our results for both seasons, which includes sites that better reflect the different forest environments on the island, provide a more reliable picture of the black rat abundance in the natural environments of Guadeloupe.

Furthermore, our data clearly demonstrate the success index to vary according to both season and environment, a pattern previously observed by [START_REF] Delattre | Cycle de reproduction du rat noir (Rattus rattus) et du surmulot (Rattus norvegicus) dans différents milieux de la Guadeloupe (Antilles françaises)[END_REF] for seasonal forests (dry deciduous and seasonal evergreen). Only Maison de la Mangrove and Gros Morne de Deshaies do not follow this trend; both sites are, in fact, characterized by lower and less variable trap success index for both seasons.

More generally, our trap success indices appear to be relatively similar to other tropical islands, such as Seychelles, and considerably higher than those reported from the temperate environment, such New Zealand (e.g. Harper and Bunbury 2015). For instance, [START_REF] Innes | Population biology of the ship rat and Norway rat in Pureora Forest Park, 1983-87[END_REF] reported trap success indices for black rats ranging from 0 to 9.2% for the different environments of New Zealand, while [START_REF] Efford | Population dynamics of the ship rat Rattus rattus L. in the Orongorongo Valley, New Zealand[END_REF] found a similar trap success index when estimating the density of black rat populations in the Orongorongo Valley, New Zealand, over a 15-year period. In contrast, [START_REF] Hill | The morphology, distribution and conservation implications of introduced rats, Rattus spp. in the granitic Seychelles[END_REF] reported much more variable trap success values from eight islands of the Seychelles archipelago, ranging from 17.5 to 99.5% and from 3.8 to 63.2% for the wet and dry seasons, respectively. The trap success values we calculated for the different Guadeloupe environments vary between 1.8 and 73.2% and are much more comparable to those obtained from Seychelles than those from New Zealand. In fact, except for the two low trap success values obtained for the semi-deciduous tropical dry forest, all others are greater than 10% (Fig. 2).

Population dynamics and seasonal cycle

Although the black rat can breed year-round (Gomez 1960;[START_REF] Innes | Population biology of the ship rat and Norway rat in Pureora Forest Park, 1983-87[END_REF]), population densities have been observed to fluctuate seasonally in many environments (e.g. Gomez 1960; [START_REF] Tamarin | The population biology of Hawaiian rodents: demographic parameters[END_REF][START_REF] Innes | Diet and reproduction of ship rats in the northern Tararuas[END_REF][START_REF] Clark | Population ecology of Rattus rattus across a desert montane forest gradient in the Galapagos Islands[END_REF][START_REF] Innes | Population biology of the ship rat and Norway rat in Pureora Forest Park, 1983-87[END_REF][START_REF] Hill | The morphology, distribution and conservation implications of introduced rats, Rattus spp. in the granitic Seychelles[END_REF][START_REF] Harper | Habitat use by three rat species (Rattus spp.) on Stewart Island/Rakiura, New Zealand[END_REF]Stapp and Polis 2003a;[START_REF] Efford | Population dynamics of the ship rat Rattus rattus L. in the Orongorongo Valley, New Zealand[END_REF][START_REF] Rousteau | The influence of spatio-temporal resource fluctuations on insular rat population dynamics[END_REF][START_REF] Russell | The origin of Rattus rattus on the Îles Éparses,Western Indian Ocean[END_REF][START_REF] Harper | Black rats in mangroves: successful and intractable[END_REF][START_REF] Ringler | Invasive rat space use on tropical islands: implications for bait broadcast[END_REF]Harper et al. 2015;[START_REF] Harper | Home range and population density of black rats (Rattus rattus) on a seabird island: a case for a marine subsidised effect?[END_REF]. This decline in breeding activity is generally interpreted as related to harsher climatic conditions and lower food availability in the environment (e.g. [START_REF] Clark | Population ecology of Rattus rattus across a desert montane forest gradient in the Galapagos Islands[END_REF]. Black rat population dynamics, however, appears to be no more stable on tropical islands, where seasonal climate conditions are neither too cold nor too dry compared with temperate islands such as those typical of New Zealand. On the contrary, black rat populations on tropical islands can highly fluctuate and reach higher densities than those typical of temperate islands. This is probably due to higher primary productivity and food availability that permit higher production, recruitment and survival of offspring (Harper and Bunbury 2015). Moreover, demographic patterns vary between populations inhabiting neighbouring islands [START_REF] Hill | The morphology, distribution and conservation implications of introduced rats, Rattus spp. in the granitic Seychelles[END_REF][START_REF] Russell | The origin of Rattus rattus on the Îles Éparses,Western Indian Ocean[END_REF] or even the same island, if it is sufficiently large enough to host different climatic conditions and environments [START_REF] Delattre | Cycle de reproduction du rat noir (Rattus rattus) et du surmulot (Rattus norvegicus) dans différents milieux de la Guadeloupe (Antilles françaises)[END_REF][START_REF] Clark | Population ecology of Rattus rattus across a desert montane forest gradient in the Galapagos Islands[END_REF]Harper et al. 2015). Nonetheless, in most cases, seasonal fluctuations in black rat populations are principally due to the recruitment of young individuals after the breeding period. On tropical islands, breeding patterns are mostly seasonal and controlled by precipitation [START_REF] Tamarin | The population biology of Hawaiian rodents: demographic parameters[END_REF]Malecha 1971, Clark 1980;[START_REF] Hill | The morphology, distribution and conservation implications of introduced rats, Rattus spp. in the granitic Seychelles[END_REF]Stapp and Polis 2003b;[START_REF] Russell | The origin of Rattus rattus on the Îles Éparses,Western Indian Ocean[END_REF][START_REF] Rousteau | The influence of spatio-temporal resource fluctuations on insular rat population dynamics[END_REF][START_REF] Harper | Black rats in mangroves: successful and intractable[END_REF]Harper et al. , 2015;;[START_REF] Ringler | Invasive rat space use on tropical islands: implications for bait broadcast[END_REF]. In these contexts, substantial seasonal and altitudinal variations in rainfall produce contrasting environments on the same island [START_REF] Beard | The natural vegetation of the Windward and Leeward Island[END_REF]. This is equally the case on Guadeloupe, where mean annual precipitation varies from less than 1500 mm in the semi-deciduous dry forest on the eastern coast of Grande-Terre Island to more than 5000 mm in the mountain rainforest of Basse-Terre Island. [START_REF] Delattre | Cycle de reproduction du rat noir (Rattus rattus) et du surmulot (Rattus norvegicus) dans différents milieux de la Guadeloupe (Antilles françaises)[END_REF] suggested that these contrasting annual precipitation rates controlled the breeding patterns of the rat population in the different Guadeloupe environments.

Our data clearly show the impact of seasonality on the relative abundance of captured young rats. During the dry season, no juvenile rats were caught, except in the swamp forest, while a higher number of juveniles were captured during the wet season, with proportions ranging from 25 to 70% according to the locality (Fig. 2). On average, the highest proportions of trapped rats come from the mountain and evergreen forest habitats and the lowest in the semi-deciduous dry forest and P. officinalis swamp forest. This pattern is in good agreement with data from [START_REF] Delattre | Cycle de reproduction du rat noir (Rattus rattus) et du surmulot (Rattus norvegicus) dans différents milieux de la Guadeloupe (Antilles françaises)[END_REF], who showed that the highest density of black rats occurred during September-October, a period which also corresponds to the maximum number of juveniles in the population during the year (26%).Moreover, in our study, few pregnant females with embryos were captured in all forest environments during the dry season. This observation, combined with the fact that no juveniles were caught during the dry season (except for the P. officinalis swamp forest), suggests that the onset of the reproductive cycle coincides with the arrival of the summer rains [START_REF] Delattre | Cycle de reproduction du rat noir (Rattus rattus) et du surmulot (Rattus norvegicus) dans différents milieux de la Guadeloupe (Antilles françaises)[END_REF], even in habitats with substantial annual rainfall.

These seasonal changes in both rat density and proportions of juveniles clearly separate the P. officinalis swamp forest from the other environmental contexts. Capture data from the swamp forest show that rats breed in October (wet season) as well as in March (dry season; one female had embryos). The low number of individuals and juveniles captured during both seasons in the swamp forest may therefore suggest that rats breed continuously in this specific environment. This contrasts with [START_REF] Delattre | Dynamique des populations du rat noir, Rattus rattus, en mangrove lacustre[END_REF] observations of a decrease in reproduction during the middle of the dry season (February to March) based on regular captures over a 2-year period. This apparently continuous breeding activity reflected in our data could thus be an artefact of a smaller number of capture sessions (only 2), suggesting some caution in their interpretation. Nevertheless, the different patterns in relative rat abundance and the proportion of juveniles observed during our study between the P. officinalis swamp forest and the other forested environments strongly suggest a more subtle, or at least less marked, seasonal effect. In addition, the interpretation of a continuous reproductive cycle in the P. officinalis swamp forest is supported by black rat behavior in comparable mangrove environments (Harper et al. 2015). [START_REF] Delattre | Dynamique des populations du rat noir, Rattus rattus, en mangrove lacustre[END_REF] proposed that the relative stability and lower density of black rat populations in the P. officinalis swamp forests are constrained by food availability in this particular environmental context. Our data are consistent with this interpretation when the higher trap success index and higher seasonal variations of young individuals trapped in low or no-water-stressed forests (seasonal evergreen and mountainous forests, respectively) in comparison to swamp forests are taken into account. Although limited, resources in this area are available throughout the year. In fact, the monodominant tree species P. officinalis produces two massive flowering blooms, from May to December, followed later by massive fruiting [START_REF] Migeot | Phenology and production of litter in a Pterocarpus officinalis (Jacq.) swamp forest of Guadeloupe (Lesser Antilles)[END_REF]. Furthermore, even if the Maison de la Mangrove is located in a freshwater environment, it is situated close to the canal of "Belle-Plaine", where aquatic fauna (fish and crustaceans) may assimilate marine-derived food resources from the nearby mangrove environment [START_REF] François-Lubin | Les peuplements de poissons des canaux et des rivières de mangroves : le cas du canal Belle-Plaine:(Grand Cul-de-Sac Marin, Guadeloupe)[END_REF]. The incorporation of marine resources in the diet of black rats inhabiting the P. officinalis swamp forest has been hinted at by the carbon isotope analysis of the carbonate component of tooth enamel (Goedert et al. in press). Greater availability of food resources throughout the year in swamp forest habitats therefore potentially underlies the more favourable conditions for black rat populations in these habitats compared with the semi-deciduous dry forest, a likelihood supported by the comparison of trap success rates between these two environments. These results are similar to those obtained in the Seychelles, where mangrove forests provide food sources for rat populations throughout the year and thus constitute much more favourable environments compared with surrounding dry forests [START_REF] Harper | Black rats in mangroves: successful and intractable[END_REF](Harper et al. , 2015)), although stress due to limited freshwater availability can play a more important role in the latter.

It is also worth noting that data from Bras David and Col des Mamelles included no young individuals captured during the dry season and thus suggests a seasonal pattern in breeding activity. This result is in itself surprising, as these two sites are located in the mountain rainforest typified by no marked seasonal fluctuations in precipitation or local vegetation.

Consequently, no restrictions on food source availability should be expected in these contexts. Other factors, such as higher altitudes, humidity, wind exposure and nebulosity associated with temperature changes, solar input and reduced soil drainage, which are also known to produce less luxuriant forest conditions, lower growth rate and tree height, all likely contribute to seasonal reproductive patterns amongst black rats (e.g. [START_REF] Beard | The natural vegetation of the Windward and Leeward Island[END_REF][START_REF] Weaver | Forest structure and productivity in Puerto Rico's Luquillo Mountains[END_REF]Rousteau 1996;[START_REF] Sastre | Plantes, milieux et paysages des Antilles françaises : écologie, biologie, identification, protection et usages[END_REF]). Several population-specific characteristics also likely underlie such differences, as is evident by the relatively high consistency in site-specific variation in eviscerated bodymass, indicating that local or intrinsic factors influence rat body condition or growth status. With that said, better understanding the factors that control differences in rat body mass and their significance requires a larger dataset than the one devised for this study, including both more captured specimens and a higher number of capture sites. Notably, a larger dataset would facilitate testing the trend towards lower body weights observed for black rats living in the semi-deciduous dry forest, as well any possible correlation with less intense breeding activity reflecting an adaptive response developed by these populations to this particular environmental context in order to cope with harsher seasonal climatic conditions. Ultimately, such a study would provide insights into how black rats adapted their population biology to be highly successful across a variety of habitats on a single tropical island.

Conclusion

The black rat is a successful invasive species capable of reproducing in a wide variety of environments. Our results show its demography to be primarily impacted by seasonal climatic factors and habitat features. Rainfall regimes, which vary both seasonally and in intensity, condition the primary production and phenology of flowering and fruiting and consequently the entire food web. These constraints directly influence black rat populations, leading to the adoption of the distinct reproductive dynamics observed in our study. Despite this high degree of variability, our capture data not only demonstrates black rats to thrive in all the natural environment contexts of Guadeloupe but also highlights the remarkable adaptability of this introduced mammalian species.

Figure 2 .

 2 Figure 2. Trap success index by site and season with associated confidence intervals and proportions of captured juveniles. Trap success indices are corrected by half of the traps that were ineffective in the total trapping effort (i.e. sprung for any cause, including rat capture)[START_REF] Himsworth | The characteristics of wild rat (Rattus spp.) populations from an inner-city neighborhood with a focus on factors critical to the understanding of rat-associated zoonoses[END_REF]

Figure 4 .

 4 Figure 4. Variations in age-related corrected trapping rates for black rats (Rattus rattus) in Guadeloupe according to habitat and season

  Semi-deciduous dry forest (capture sites: Gros Morne de Deshaies Basse-Terre GPS WGS 84 coordinates 16.32° N, -61.8° O; alt: 200 m and Barre de Cadoue Grande-Terre 16.48° N; 61.44° O; alt: 40 m) on the western coast of Basse-Terre and in the east and north of Grande-Terre. These forests are found below 300m(a.s.l.) on the western side of Basse-Terre and cover most of the lower (less than 250 m a.s.l.) island of Grande-Terre. These forests receive less than 1500-2000 mm of annual rainfall and are marked by seasonal climatic variations, with a dry season extending from January to April/May, and a wet season extending from August to December. Barre de Cadoue is in a more xeric environment with a mean annual precipitation of less than 1400 mm.

	the semi-deciduous dry forest at altitudes between 300 and 400 m (a.s.l.) on the western side
	and 100 m on the eastern side of Basse-Terre, in addition to semi-deciduous wet formations
	of western Grande-Terre, all of which receive between 1500 and 2000 and 3000 mm of annual
	precipitation. This is significantly more than what is recorded for the dry semi-deciduous forest
	between the wet and dry seasons.
	3 Mountainous and sub-mountainous rainforest (capture sites: Bras David 16.17° N; 61.69° O;
	alt: 250 m) at mid-elevations of the central ridge of the mountainous massif that receives an
	annual rainfall in excess of 2500-3000 mm, with no sizeable seasonal fluctuations impacting
	vegetation phenology. These forests are found between 300 and 400 and 800 m (a.s.l.) and
	between 100 and 600 m (a.s.l.) to the west and east of the mountainous massif of Basse-Terre.
	4 Altimontane formations (capture sites: Col des Mamelles 16.18° N; 61.73° O; alt: 550 m) group
	together low bushes and altimontane rainforests on the upper portions of the Basse-Terre
	mountain range. These zones receive more than 2500 mm of annual rainfall in the north of the
	island and more than 4500 mm in the south. These forests develop above the mountainous
	and sub-mountainous rainforest at altitudes between 1000 and 1100 m (a.s.l.).
	5 Wetlands (capture sites: Maison de la Mangrove located on the plains between Basse-Terre
	and Grande-Terre 16.29° N; -61.53° O; alt: 7 m) that include mangroves, swamp forest,
	swampy plains and valley bottoms. Located in the low-lying areas (< 10 m (a.s.l.)) of the plains
	between Basse-Terre and Grande-Terre, the swamp forest is the most extensive freshwater
	wetland forest and is largely dominated by Pterocarpus officinalis

with the location of the capture sites. Graphs illustrating monthly rainfall from April 2017 to March 2018 recorded at three Météo-France weather stations in different environmental settings. CA, Barre de Cadoue; MANG, Maison de la mangrove; PR, Piton Sainte-Rose; GM, Gros Morne de Deshaies; ESP, Espérance; MM, Mamelles; BD, Bras David 1 2 Seasonal evergreen forest (capture sites: Espérance western Basse-Terre 16.23° N; 61.74° O; alt: 300 m and Piton Sainte-Rose northern Basse-Terre 16.33° N, -61.76° O; alt: 320 m) comprises evergreens mixed with a limited number of deciduous species and develops above

Acknowledgements

The authors would like to thank the Guadeloupe National Park agents who provided the rat traps used during the capture sessions. A special thanks go to Daniel Imbert (University of Antilles), who helped us identify the capture sites and lent us some equipment. The authors are also grateful to "Météo-France" for providing data concerning weather conditions in Guadeloupe. The authors would also like to thank Zoé Thalaud for her help in preparing the rat skeletal material and the acquisition of biological data.

Funding information

This study was conducted as a part of the CNRS ECSIT Project: « Écosystèmes insulaires tropicaux, réponse de la faune indigène terrestre de Guadeloupe à 6000 ans d'anthropisation du milieu », with financial support from the European PO-FEDER program (grant n° 2016-FED-503), the Guadeloupe Regional Council, and the DAC of Guadeloupe.

Availability of data and material

The authors declare that the data supporting the findings of this study are available within the manuscript and its supplementary information.

Author's contributions

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

Trapping operations were reported to the ONCFS regional office and authorizations were provided by the Guadeloupe National Park (permits PNG-2017-81 andPNG-2018-15). This study did not involve any endangered or protected species. None of the captured black rats were subjected to experiments involving extended stress or suffering. All specimens were killed following recommendations (regulation UE 2010/63) for the small mammals weighing less than 1 kg (Annex IV).


Duy Huynh
email: duy.huynh@irit.fr

Nathalie Neptune
email: nathalie.neptune@irit.fr

Josiane Mothe

Annotation automatique d'images: le cas de la déforestation

Keywords: Information retrieval, image annotation, CNN, convolutional neural networks, deforestation detection

Cet article correspond à un état de l'art sur le thème de l'annotation automatique d'images d'observation de la terre pour la détection de la déforestation. Nous nous intéressons aux différents challenges que recouvre le domaine et nous présentons les méthodes de l'état de l'art puis les pistes de recherche que nous envisageons.

Introduction

According to the National Geographic, forest covers about 30% of the planet. Forest ecosystems play an essential role in supporting life on the earth such as supplying wood, water regulation, preventing storms and soil erosion and forests store rare genetic resources for our planet. Deforestation affects the environment in a multitude of ways. The most obvious effects are global warming and loss of biodiversity. From the photosynthetic function of trees, forests release oxygen and absorb carbon dioxide. The fewer forests, the more carbon dioxide entering the atmosphere, increasing the speed of global warming. In addition, earth's forests are home to over 80% of plants and animals but deforestation destroys these habitats, diminishing biodiversity and causing the extinction of four to six thousand rainforest species every year [START_REF] Geographic | Climate 101 : Deforestation[END_REF]. Direct causes of deforestation are agricultural expansion, logging and wood extraction, bio fuels from palm oil, infrastructural expansion such as road and urbanization, and mining [START_REF] Tariq | An overview of deforestation causes and its environmental hazards in khyber pukhtunkhwa[END_REF] (see also http://www. theworldcounts.com/stories/deforestation-facts-and-statistics).

Satellite remote sensing makes it possible to observe the earth and help detect deforestation in a faster way than ever. For example, the Copernicus program provides images from any region every 5 days 1 . Moreover, with the advances of computing power and machine learning techniques, it becomes possible to detect deforestation automatically from earth observation (EO) images [START_REF] Achard | Determination of deforestation rates of the world's humid tropical forests[END_REF][START_REF] Secades | Earth observation as a tool for tracking progress towards the aichi biodiversity targets[END_REF]. This paper reviews the state of the art of the methods that are used in the domain of automatic deforestation detection. When using remote sensing, deforestation detection is an application of change detection. It generally consists in two steps : the annotation of satellite images in order to identify the land cover and the change detection on zones that are identified as forest. In these two steps, convolution neural networks (CNN) were proven successful. This is because CNN allow for a large learning capacity [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF] ; moreover their automatic feature extraction capability is very useful in image analysis [START_REF] Witten | Data Mining : Practical machine learning tools and techniques[END_REF]. Figure 1 shows the basic workflow of automatic deforestation detection using two images of the same area captured at different times. The first step is linked to information retrieval and can be paralleled with the tasks developed in ImageCLEF for automatic association of concepts to images in medicine [START_REF] Ionescu | Overview of imageclef 2017 : Information extraction from images[END_REF], although it goes a step further since image segmentation is usually considered to annotate the images in the case of deforestation detection while it is usually an index only in information retrieval. The remainder of this paper is as follows : sections 2 and 3 present the state of the art of the two steps of the automatic deforestation detection problem. In section 4, we present the main current challenges which also correspond to the ones that we would like to tackle in our future work. Section 5 concludes this paper. Additionally, a brief explanation of how convolutional neural networks work is presented in the appendix.

Image annotation is a general problem which is not specific to forest detection but rather that covers a large variety of domains such as information extraction from medical images for disease detection [START_REF] Ionescu | Overview of imageclef 2017 : Information extraction from images[END_REF], [START_REF] Mothe | IRIT & MISA at Image CLEF 2017 -Multi label classification[END_REF], and image retrieval [START_REF] Babenko | Neural codes for image retrieval[END_REF]. In the case of deforestation detection, what we need is land cover information. The automatic annotation of earth observation images to associate land cover types with areas is typically done with classification methods. Our intention is not to provide a state of the art of image classification methods but rather to detail how these methods have been used in the case of land cover annotation with an emphasis on deforestation detection.

As in many fields, deep learning has revolutionised the domain of image annotation for deforestation detection in remote sensing. Without aiming to be exhaustive, in this section, we first present some of the approaches that have been developed for land cover annotation of satellite imagery before the deep neural networks arose, then we present how deep learning is used in this domain.

Methods used before deep learning

Classification-based methods are very commonly used when having to annotate satellite images for deforestation detection. [START_REF] Shimabukuro | Using shade fraction image segmentation to evaluate deforestation in landsat thematic mapper images of the amazon region[END_REF] and [START_REF] Müller | Long-term deforestation dynamics in the brazilian amazon-uncovering historic frontier development along the cuiabá-santarém highway[END_REF] are two examples of such methods, the first one opting for an unsupervised classification approach by segment while the second one is using a supervised pixel-by-pixel approach.

To generate deforestation maps and provide related information on areas experiencing deforestation, [START_REF] Shimabukuro | Using shade fraction image segmentation to evaluate deforestation in landsat thematic mapper images of the amazon region[END_REF] proposed an approach based on shade fraction image generated by a spectral mixture model, then "region growing" segmentation and unsupervised classification (clustering) of fields were applied. At the time, the state of the art was visual interpretation of satellite imagery or classification based on pixel-by-pixel analysis without contextual information. However, visual interpretation is laborious and thus costly especially when dealing with a large number of small surfaces to label in the same area. Using shade fraction images instead of working with the Landsat images directly allowed for reduced processing time for classification and post-processing time for manual removal of undetermined classes. Their results were validated against results from conventional techniques in use at the time on images from Rondônia, in Brazil.

Yearly deforestation patterns for an area of the Amazon forest in Mato Grosso and Pará were derived from Landsat TM 2 and ETM+ 3 images captured between 1985 and 2012, by [START_REF] Müller | Long-term deforestation dynamics in the brazilian amazon-uncovering historic frontier development along the cuiabá-santarém highway[END_REF]. The proposed approach uses a random forest classifier on the training data made of labelled samples of the values of reflectance images and minimum tasseled cap wetness (TCW) observations, for each pixel. An overall accuracy of 85% was reached with 95% confidence interval margin of ±2%. Classification error tended slightly towards late detections. Higher deforestation rates were found compared to the state of the art deforestation datasets for the same region and over the same time 2. Landsat Thematic Mapper (TM) is a an advanced multi-spectral scanning sensor carried by Landsat 4 and 5 and featuring seven spectral bands. With band 6 being a thermal infrared radiation sensor. The TM sensor has a spatial resolution of 30 m X 30 m (120 m for band 6) and a temporal resolution of 16 days. https://landsat.gsfc.nasa.gov/ the-thematic-mapper/ 3. Landsat Enhanced Thematic Mapper Plus (ETM+) is an enhanced version of Landsat TM carried by Landsat 7. ETM+ is a multi-spectral scanning radiometer with eight bands. It has a spatial resolution of 30 m X 30 m (60 m for the thermal band, 15 m for the panchromatic band). https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus/ period.

The next subsections review the approaches in the literature that use convolutional neural networks (CNN) to annotate remote sensor data, in particular for the detection and analysis of deforestation.

Methods using convolutional neural networks

Several approaches using neural networks, including convolutional neural networks have been proposed for annotating satellite images, such as [START_REF] Kussul | Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data[END_REF] and [START_REF] Zhang | A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification[END_REF]. In the first approach the annotation is done by segment while it is done by pixel in the second approach. [START_REF] Kussul | Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data[END_REF] proposed a deep learning approach for the classification of multi-source satellite images. They used a combination of supervised and unsupervised neural networks to segment and classify satellite imagery from Landsat 8 and Sentinel 1A. Testing was done with data from the Joint Experiment of Crop Assessment and Monitoring (JECAM) test site in Ukraine. While this study focused mostly on crop identification, the area was labelled with eleven land cover types among which the "forest" type. This approach was tested for overall accuracy against the following approaches : random forest (RF), and an ensemble of neural networks (ENN) made of multilayer perceptrons. The proposed ensemble of 2D CNN outperformed these two other methods in overall accuracy. However, for the forest class, the difference is only a few decimal points because the random forest and ENN had already reached over 99% accuracy.

The approach from [START_REF] Zhang | A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification[END_REF] classifies fine resolution images from remote sensors with a model integrating a CNN and a multilayer perceptron (MLP). This model was compared to standalone standard pixel-based CNN and MLP classifiers and a pixel-based texture MLP based on the standard Grey Level Co-occurrence Matrix (GLCM-MLP). The authors tested the method on data from Southampton and its surroundings, in the UK. A total of eight land cover classes were detected and large patches of forest in rural areas. While they did not have a forest class, forest patches were put in the "Trees" class which is described as "large patches of deciduous trees" and "patches of tree species". The overall accuracy of the MLP-CNN approach, including for the Trees class, on the two test sites was found to be higher than that of the other three methods.

In the next subsection we discuss a Kaggle4 competition aimed at automatically classifying forest images for deforestation detection and the prevalence of CNN models.

The Kaggle competition for deforestation detection

Deforestation can also be detected in a single image by detecting known deforestation patterns in a forested area. The "Planet Deforestation Detection" Kaggle competition launched by Planet Labs5 in April 2017, proposed such task.

The competition aimed to find effective methods to track forest changes by using high resolution satellite imagery. The images were provided as image chips and the task was to automatically annotate each image chip with its corresponding labels for atmospheric condition and land cover. A total of 17 possible labels were defined. The competition started on April 20, 2017 and ended on July 20, 2017 [START_REF] Kaggle | Planet : Understanding the amazon from space | kaggle[END_REF].

The chips are extracted from satellite images and represent approximately one square kilometre [START_REF] Scott | Forest recognition : Planet launches kaggle competition[END_REF]. These chips are from Planet's full-frame analytic scene products and were provided in GeoTiff formats with four bands of data and in JPG format. The scenes included were exclusively from the Amazon basin (?). The training set contained over 40000 images while the test set contained over 20000 images. The F2-score, which is a weighted average of precision and recall, was used to rank the submissions.

The formula for calculating the F2-score was given as follows [START_REF] Kaggle | Planet : Understanding the amazon from space | kaggle[END_REF] :

(1 + β 2 ) P.R
β 2 P +R where P = T P T P +F P , R = T P T P +F N , β = 2.

P is the precision and R is the recall, T P stands for true positives, F P for false positives and F N for false negatives, β is a float which in this case is equal to 2 to indicate that a higher weight is given to the recall than to the precision.

There were some data quality issues with TIFF images not matching their corresponding JPG images in the test set and there were labels incorrectly assigned as well. Ambiguous labelling was in several cases due to the fact that from the image alone certain classes could not be told apart by visual interpretation. The winning model used exclusively the JPG images with several data augmentation techniques to pre-process the images such as image rotation and haze removal [START_REF] He | Single image haze removal using dark channel prior[END_REF]. A model made of 11 CNN was used with existing CNN architectures such as Inception, Resnet and Densenet. Then ridge regression was used on the probabilities obtained for each label. The CNN were then combined with a ridge regression model. Finally, the loss function was designed to take into account the F2-score used for evaluating the submissions 6 [START_REF] Kaggle | Planet : Understanding the amazon from space | kaggle[END_REF].

A total of 938 submissions were made for this competition [START_REF] Kaggle | Planet : Understanding the amazon from space | kaggle[END_REF]. Of the top 16 teams from the private leader-board, 7 reported using CNN models 7 . [START_REF] Lagrange | Benchmarking classification of earth-observation data : From learning explicit features to convolutional networks[END_REF] had found that for semantic labelling of earth observation images, the best performance is obtained when using deep convolutional neural networks compared to expert classifiers and spectral support-vector classification.

Image annotation is the first step in change detection, and deep learning can be used for automatically learning features and annotating the images. The result of the image annotation task is images that are semantically labelled and can be compared to detect changes [START_REF] Hirschmugl | Methods for mapping forest disturbance and degradation from optical earth observation data : a review[END_REF]. In the next section we will go over the methods that are commonly used for change detection in remote sensing imagery.

Change detection in remote sensing images

In remote sensing, the use of satellite images to assess and then map deforestation is one of the applications of change detection. Two methods are applied for change detection with optical images : image to image change detection and time series analysis. Image-to-image change detection requires a minimum of two images captured at different times while time series analysis requires a series 6. http://blog.kaggle.com/2017/10/17/planet-understanding-the-amazon-from-space-1st-7. https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/ discussion/36732 of images captured over a period of time. Image-to-Image change detection is more commonly used. Performing change detection over long time periods may require the use of images captured by different remote sensing devices with different characteristics which then requires data fusion techniques to combine these data together.

Methods prior to deep learning

Deforestation detection is one of the major applications of satellite image change detection. The problem has been studied for decades and many techniques have been proposed. These techniques can be categorized into different approaches such as algebra, transformation, classification, advanced models, geographical information system (GIS) approaches, or visual analysis [START_REF] Lu | Change detection techniques[END_REF].

The very first algebra technique was univariate image differencing. This straightforward technique detects the change by applying a threshold to the difference in pixel value between first-date image and the second-date image [START_REF] Lu | Change detection techniques[END_REF]. This technique was widely used in change detection problems, particularly for detecting forest changes as in [START_REF] Miller | Analysis of the dynamics of shifting cultivation in the tropical forests of northern Thailand using landscape modeling and classification of Landsat imagery[END_REF] and others [START_REF] Singh | Review article digital change detection techniques using remotely-sensed data[END_REF]. Another well-known algebra technique was image regression. First, the method assumed that pixels in the same location are related by a linear function in time. Thus, the pixels values in the second-date image can be predicted according to the regression function. Finally, a threshold was applied to the difference between the true second-date value and the predicted second-date value. This technique showed better performance than the image differencing technique [START_REF] Singh | Review article digital change detection techniques using remotely-sensed data[END_REF].

The second group of techniques uses transformations such as Principal Component Analysis (PCA), Multitemporal Kauth-Thomas (MKT), Gramm-Schmidt (GS), and Chi-square transformations [START_REF] Lu | Change detection techniques[END_REF]. [START_REF] Collins | An assessment of several linear change detection techniques for mapping forest mortality using multitemporal landsat tm data[END_REF] examined PCA, MKT, GS methods to the problem of forest change due to conifer mortality and concluded that PCA and MKT give better results than GS.

The third group of change detection techniques is made of classification approaches which have been used for both image to image change detection and for time series change detection like in [START_REF] Mertens | Spatial modelling of deforestation in southern cameroon : spatial disaggregation of diverse deforestation processes[END_REF] and [START_REF] Olofsson | Time series analysis of satellite data reveals continuous deforestation of new england since the 1980s[END_REF] respectively. [START_REF] Mertens | Spatial modelling of deforestation in southern cameroon : spatial disaggregation of diverse deforestation processes[END_REF] proposed a model to detect deforestation in southern Cameroon based on remote sensing data from Landsat MSS sensor 8 for the years 1973 and 1986. Photographs, digitized topographic maps, aerial photos and other remote sensing data and population data were used as well. In addition, ground observation data were collected and used to validate the land cover and land cover change maps. A maximum likelihood classifier was used to generate land cover maps for 1973 and 1986, independently for the areas common to the two Landsat images. The classifier was trained on field observations and interpretations of aerial photos of unchanged areas for 1979. The accuracy was evaluated with field observation data and low-altitude aerial photos. For the classification task, 90% accuracy was reached for 1973 but only 85% for 1986 due to haze. Five classes were derived among which the "dense forest" which the authors define as "evergreen or moist deciduous forest zones, dominated by trees at least 5 m high and with a forest-cover proportion of 30 per cent or more", the four other classes were for different non-forested areas.

8. The Landsat MSS sensor is a multi spectral scanner carried by Landsat satellites one through five, with a spatial resolution of 68 m X 83 m and a temporal resolution of 18 days. https://landsat.gsfc.nasa.gov/ the-multispectral-scanner-system/ A time series analysis was performed by [START_REF] Olofsson | Time series analysis of satellite data reveals continuous deforestation of new england since the 1980s[END_REF] to reveal deforestations trends in the New England area in the North East of the United States with the goal of modelling the impact that forest changes have on the carbon balance of the planet. To map the land cover change, the Continuous Change Detection and Classification (CCDC) algorithm was used on pixel-level time series of Landsat data available for the area from 1985 to 2011. Areas of forest harvest were not added to the deforestation estimate. A random forest classifier was used on the training data with the attributes from the time series prediction model for each time series. To account for bias caused by classification errors, the mapped area is estimated from a random sample of reference observations then visual examination is performed to confirm the labels. This study detected the land cover changes but also capture the evolution of forest loss over time. Accelerated deforestation was found to have taken place in the 1990 then around 2007 the deforestation rate stabilized.

While change detection with optical imagery is fairly common, less work has been done with radar imagery especially with time series. However there have been some contributions on the subject such as in [START_REF] Barreto | Classification of Detected Changes From Multitemporal High-Res Xband SAR Images : Intensity and Texture Descriptors From SuperPixels[END_REF].

A change detection method based on object detection for high resolution Synthetic Aperture Radar (SAR) data was proposed by [START_REF] Barreto | Classification of Detected Changes From Multitemporal High-Res Xband SAR Images : Intensity and Texture Descriptors From SuperPixels[END_REF]. This method involves three steps. First, multitemporal Xband high resolution SAR image segmentation, followed by feature extraction and finally, area detection and classification. The image is segmented into superpixels with a simple linear iterative clustering algorithm (SLIC). The features are extracted with the object correlation images (OCI) framework and with gray-level cooccurrence matrix (GLCM). Areas are detected and classified into unchanged, deforestation and other changes classes with a multilayer perceptron. Experts manually annotated a set of multi-temporal Xband SAR images captured from August to November 2015 with 2,263 regions of interest. Results showed an improvement of 10% in accuracy, compared to the state of the art approaches, in change detection and classification for the deforested areas. The proposed approach was compared to the following state of the art object-based approaches : Optimum Path Forest (OPF) clustering for image segmentation, OCI alone for feature extraction, SVM and OPF-classifier for classification.

3.2 Method using deep learning [START_REF] Khan | Forest change detection in incomplete satellite images with deep neural networks[END_REF] proposed a new deep CNN model for object-based change detection in incomplete satellite imagery. Their approach performs two tasks, first data recovery to fill data that is missing due to limited camera aperture, cloud cover, and sensor artefacts, then change detection which is treated as a regions classification problem. For this second task, object-based change detection was used without domain knowledge to extract features, instead, all features were learned with a deep neural network.

The model was used to analyse satellite data on the north-east region of Melbourne, in Victoria, Australia. The authors were able to detect forest cover change on images taken from 1987 to 2015. This approach outperforms baseline techniques in temporal change detection and patch-wise classification tasks.

For start-time and end-time predictions for detected change events, this approach outperforms the state-of-the-art approaches, one based on hand-crafted features for classification and the second one based on bag of visual words for classification. To establish the baseline with these two state-of-the-art approaches, dense scale invariant feature transform (SIFT) descriptors were used as a baseline for change detection and linear SVM, kernel SVM and random forest (RF) were used for prediction.

Summary of state of the art approaches and cited papers

Table 1 shows the papers that were cited for annotation and change detection with earth observation images. The papers are categorized by the type of approach used. Among the papers cited, most have used a classification-based approach, including the ones using CNN. [START_REF] Miller | Analysis of the dynamics of shifting cultivation in the tropical forests of northern Thailand using landscape modeling and classification of Landsat imagery[END_REF] Classification based Prior to CNN [START_REF] Miller | Analysis of the dynamics of shifting cultivation in the tropical forests of northern Thailand using landscape modeling and classification of Landsat imagery[END_REF]) [START_REF] Müller | Long-term deforestation dynamics in the brazilian amazon-uncovering historic frontier development along the cuiabá-santarém highway[END_REF]) [START_REF] Mertens | Spatial modelling of deforestation in southern cameroon : spatial disaggregation of diverse deforestation processes[END_REF] [START_REF] Olofsson | Time series analysis of satellite data reveals continuous deforestation of new england since the 1980s[END_REF]) [START_REF] Shimabukuro | Using shade fraction image segmentation to evaluate deforestation in landsat thematic mapper images of the amazon region[END_REF] [START_REF] Barreto | Classification of Detected Changes From Multitemporal High-Res Xband SAR Images : Intensity and Texture Descriptors From SuperPixels[END_REF] [START_REF] Shimabukuro | Using shade fraction image segmentation to evaluate deforestation in landsat thematic mapper images of the amazon region[END_REF] Other methods [START_REF] Achard | Determination of deforestation rates of the world's humid tropical forests[END_REF]) [START_REF] Achard | Determination of deforestation rates of the world's humid tropical forests[END_REF] Deforestation detection using earth observation data implies mainly two complementary tasks : (a) detecting in a given image what is forest and what is not (i.e. image annotation) (b) detecting changes between images. While deep learning model have shown promising results in these two tasks, remote sensing suffers from a limited availability of annotated data for training such models. Moreover, it is likely that using different sources of evidence can help in the accuracy of the detection. The next section is related to these issues.

Research questions

From our readings and from related work, we elaborate what we think are the main research questions at this stage in the domain of automatic detection of deforestation using earth observation imagery.

4.1 Data fusion : how to combine various sources of evidence ?

A current trend in remote sensing is data fusion [START_REF] Zhang | Multi-source remote sensing data fusion : status and trends[END_REF]. The main idea is to combine data from multiple sources in order to produce better quality results taking advantage of the information each source carries. While data fusion is not new in remote sensing [START_REF] Pohl | Review article multisensor image fusion in remote sensing : concepts, methods and applications[END_REF], the most recent research in the field focuses on high-level fusion in place of pixel-level fusion. [START_REF] Joshi | A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring[END_REF] reviewed 112 studies for various types of applications on fusing optical and radar data and concluded that the main methods used are pre-classification fusion followed by pixel-level inputs in traditional classification algorithms.

Various types of data can be fused. For example, [START_REF] Reiche | Fusing landsat and sar time series to detect deforestation in the tropics[END_REF] presented a fusion approach (MulTiFuse) that exploits the full observation density of optical and Synthetic Aperture Radar (SAR) time series to detect deforestation in the tropics. [START_REF] Schmidt | Multi-resolution time series imagery for forest disturbance and regrowth monitoring in queensland, australia[END_REF] used coarse spatial resolution MODIS data combined with finer spatial resolution Landsat data to map forest and agricultural elements of an area in central southeast Queensland, Australia. In [START_REF] Reiche | Improving near-real time deforestation monitoring in tropical dry forests by combining dense sentinel-1 time series with landsat and alos-2 palsar-2[END_REF], the authors show that spatial and temporal accuracies of the multi-sensor approach were higher than the singlesensor results for near real-time deforestation detection in tropical dry forests (the authors combined Sentinel-1 C-band SAR time series with ALSO-2 PALSAR-2 L-band SAR, and Landsat-7/ETM+ and 8/OLI).

Current data fusion techniques focus on fusing data at various levels of resolution, various number of layers for multi-spectral imagery but a few consider other sources of evidence such as statistics, scientific publications, etc. While satellite data is one source of evidence for deforestation detection, it may not be enough to explain the cause of deforestation and monitor its evolution as well as other related geo-phenomena. To get a step further we would like to combine earth observation imagery with other sources of evidence such as statistics related to wood exportation, type of manufacturing goods implying wood or derivatives, level of urbanisation and population growth. Another source of evidence we would like to use in cross analyses are reports and scientific papers. We believe that scientific papers on sustainable development contain relevant information about deforestation and the related geo-phenomena and that this information can be extracted and combined with data from earth observation imagery for analysis purposes. The main issue to fuse these sources is their high level of heterogeneity which is an open question.

Transfer learning with CNN : how effective is it ?

Transfer learning is the process of learning features by training a network on a large data set and then transferring these features to a different dataset. This approach has also been used in remote sensing and showed promising results by [START_REF] Hu | Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery[END_REF] and [START_REF] Salberg | Detection of seals in remote sensing images using features extracted from deep convolutional neural networks[END_REF]. The ability for learned features to be transferred across domains accounts for the success of transfer learning in deep learning and makes it a promising approach to cope with the problem of limited training data. Transfer learning is also used to overcome the problem of the lack of annotated data for training.

The annotation of remote sensing images is done either by human annotators or automatically by a computer program. In both cases, ground truths are the reference against which these annotations can be validated. However, going on the ground to collected theses ground truths may be costly and impractical. Consequently, the amount of labelled data with validated labels remains limited compared to the large amount of earth observation data that is available.

To overcome the problem of limited labelled data available for training, several approaches using transfer learning have been proposed in the remote sensing field. The effectiveness of deep learning approaches in remote sensing considering the insufficient training data is still an open question. So far, limited work has been done on change detection in forests with deep learning and CNN in particular. We aim to propose a new approach to detect and map deforestation in tropical forests using transfer learning.

Our goal is to propose a model to track the evolution of deforestation over time with time series analysis. By using transfer learning, we aim to find the best suited image dataset for this task by testing with various types of images.

We aim to provide a general model that can be reused in different countries and areas. This will require training our model on a large and diverse dataset. There is a massive amount of spatial data available though mostly unlabelled. We therefore aim to propose an unsupervised approach to annotate these images and validate the results with very high resolution images and ground truth data when available.

How to adapt CNN architectures to multi-spectral images ?

Satellite images are multi-spectral and pose the challenge of high dimensionality for machine learning.

We will propose a CNN model that is adapted to this particular type of data. We aim to propose a dimension reduction approach that is optimal for our problem of detecting and tracking deforestation in tropical forests.

How to get annotated satellite images for our areas of interest ?

Due to the fact that annotated satellite data for most tropical forests is scarce, another contribution we aim to make is provide a new data set of labelled earth observation data for one of our areas of interest. To achieve this, we will use very high resolution images and ground truth data from local organizations if available or from field missions.

Conclusion

This paper presented the state of the art related to automatic detection of deforestation using earth observation images from remote sensors. We present the methods that have been developed in the two main steps of automatic detection of deforestation : image annotation which includes image segmentation and classification, and change detection. For both steps, we chose to present first the methods that were developed before deep neural networks then the methods which use deep learning.

As in many other fields, neural networks are now very commonly used in remote sensing. Finally, we mentioned the research questions we would like to tackle in our future work and for two of them, namely data fusion and transfer learning, we detailed related work.

6 Appendix -Convolutional neural networks

Convolutional Neural Networks are one of the deep neural network classes that give the best current results in most computer vision problems such as classification and object recognition [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF], [START_REF] Witten | Data Mining : Practical machine learning tools and techniques[END_REF]. Basically, CNN is a straightforward Artificial Neural Network (ANN) in which the architecture consists of several layers connected together in a multi-tiered structure [START_REF] Lecun | Gradient-based learning applied to document recognition[END_REF]. There are three main types of layers : convolution layer,activation layer, pooling layer and fully connected layer [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF].

Before going into the basic components of CNN, we would like to look at an example describing how a feedforward ANN processes an input information. From that, we can draw on the effects of CNN components.

Suppose that we have a 200 x 200 image processed by a fully connected ANN. Each neuron needs 40,000 parameters to be trained which is costly. To reduce the number of parameters it is necessary to reduce the number of connections between layers ; this is the objective of the CNN convolution component. The idea is that each neural only needs to be connected to a local area of the image instead of the entire image. This feature enlarges CNN learning capacity [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF] which is the basic need to deal with large dataset problem such as earth observation images.

According to the literature of the domain (see for example [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF] there are 4 types of layers in a CNN which are defined as follows :

Convolution layer

The convolution layer plays the main role in the architecture of a CNN. Each neuron of the layer is formed by doing a convolution between a kernel and an image, applying convolution to the images aims at extracting important features such as edges, direction, color [START_REF] Witten | Data Mining : Practical machine learning tools and techniques[END_REF].

Activation layer

This layer is usually placed right after the convolutional layer. This layer uses an activation function such as sigmoid, tanh, softplus, rectified linear unit (ReLU). However, ReLU (f (x) = max(0, x)) is used most recently. The function converts all negative values in the result obtained from the convolutional layer to the value 0. The meaning of this setting is to make the model non-linear [START_REF] Witten | Data Mining : Practical machine learning tools and techniques[END_REF]. Using an activation layer also increases the learning rate [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF].

Pooling Layer

The goal of this layer is to reduce the matrix size but still highlight the features that are present in the input matrix. Max pooling is often used [START_REF] Guo | Deep learning for visual understanding : A review[END_REF]. In terms of meaning, Max-Pooling determines where the strongest signal is when applying a filter. It is done by taking the maximum value of the neurons within the pooling region [START_REF] Witten | Data Mining : Practical machine learning tools and techniques[END_REF].

Fully Connected Layer

This layer is similar to the feedforward ANN : all the nodes of the current layer are fully linked to the nodes from the next layer. After the image is processed by the previous layers, the image data will no longer be too large compared to the ANN model. This layer is placed at the end part of the CNN [START_REF] Witten | Data Mining : Practical machine learning tools and techniques[END_REF].

A convolution neural network is formed by putting the above layers together. The model always starts with the convolutional layer. The activation layer usually follows right after the convolutional layer or even merges both layers into a layer. The next layer can be convolutional or pooling. This pattern can be repeated depending on the architecture. The output of these layers then may feeded to fully connected layers. The final layer of CNN usually uses the softmax function (σ (z) j = ) which forces the output of the network represent a probability distribution across discrete alternatives [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF]. Figure 2 represents a general CNN which uses the ReLU function as an activation function.

FIGURE 2: A general CNN architecture which is merges the convolution layer and activation layer into one [START_REF] Mathworks | Convolutional neural network[END_REF]

FIGURE 1 :

 1 FIGURE 1: Basic workflow for annotation and change detection in satellite imagery of the same area taken at two different times.

TABLE 1 :

 1 Publications cited and the methods that they propose for each annotation and change detection tasks.

	METHOD	TASK Annotation	Change detection
	Algebra based approach

Kaggle is a data science and machine learning competition platform. https://www.kaggle.com/

Planet Labs is an earth observation satellite company based in San Francisco https://www.planet.com/

Acknowledgements

The work of Nathalie Neptune is supported by the Schlumberger Foundation through the Faculty for the Future fellowship. We would also like to thank the reviewers for their valuable comments. This work also benefits support from FabSpace 2.0 project ; FabSpace 2.0 received funding from the European Union's Horizon 2020 Research and Innovation programme under the Grant Agreement number 693210.