Fabien Paquet
email: f.paquet@brgm.fr

Isabelle Thinon

Eric Lasseur

Justine Briais

Olivier Dugue

Bernadette Tessier

Virginie Gaullier

One network may hide otherstoward a comprehensive scenario for neogene superimposed valley networks along the English Channel

The English Channel has been an area of intense geological investigation for decades, spanning various subjects such as structural and basin evolution between variscan and alpine orogenic cycles or sediment transport over a wide platform under tidal and storm currents. Among these subjects, one specific and noticeable feature of the English Channel is the occurrence of a complex network of channels with clear morphological expression at the seabed. Since the beginning of the 20th century, and more pragmatically since the 1970s and the development of high-resolution seismic acquisition, this network has been the focus of several studies that proposed various scenarii for its origin and age. The existence of two superimposed networks had already been proposed in the late 70's by Alduc. Since 2007, in the framework of the geological mapping of the French continental shelf, BRGM and collaborators (University of Caen Basse-Normandie/UMR M2C; University of Lille 1/UMR LOG,…) acquired a dense grid of very high resolution marine seismic data. Interpretation of these data (i) confirms that several networks are superimposed, and (ii) details the real complexity and discontinuity of the older network(s) associated to the "Fosses de la Manche" system. Finally, thanks to high quality oil exploration seismic data, we were able to reveal a local unexpected increase of bedrock incision and associated sedimentary fill up to c. 350 m. These observations and subsequent implications for networks origin (fluvial or tidal) and development are discussed with respect to regional geological settings and controlling parameters. Their significance in terms of source-to-sink features is a key aspect as the English Channel is certainly an important zone of sediment transfer during Neogene, collecting from a large catchment and delivering to the Western Approaches, the Celtic Sea and the Bay of Biscay abyssal plains. Drilling selected targets to provide groundtruthing sounds now like the obvious next step.


Ismaïla Seck

Khouloud Dahmane

Pierre Duthon

Gaëlle Loosli

Baselines and a datasheet for the Cerema AWP dataset

Keywords: Datasheet for dataset, Cerema AWP, classification, regression, auto-encoder, GAN

This paper presents the recently published Cerema AWP (Adverse Weather Pedestrian) dataset for various machine learning tasks and its exports in machine learning friendly format. We explain why this dataset can be interesting (mainly because it is a greatly controlled and fully annotated image dataset) and present baseline results for various tasks. Moreover, we decided to follow the very recent suggestions of datasheets for dataset, trying to standardize all the available information of the dataset, with a transparency objective.

Introduction

Deep learning techniques have shown impressive results during the past years, in particular when dealing with image processing. However there remains a lot of open questions, for instance in terms of quality evaluation and method comparisons.

The introduction of new datasets and of new algorithms leads to new baselines. That raises some questions about how those baselines are established, about the fairness in the comparison between the newly introduced algorithm and the ones previously known.This problem is not new [START_REF] Armstrong | Relative significance is insufficient: Baselines matter too[END_REF] but it may be increased by the huge amount of publications in the field nowadays. Let us take the case of Generative Adversarial Networks [GPAM + 14], there are many variants of GAN1 , often with the claim to outperform previous versions and so with no objective metric to back that claim. 2Another aspect linked to new datasets is the need for transparency and good understanding of its content. The recent work-in-progress [GMV + 18] discusses the need for standardization of datasets we use. While the cited paper is only a work-in-progress proposal to a standard datasheet content that will likely change in the coming months/years, we decided to make an attempt of description of the Cerema AWP dataset according to this proposal.

The Cerema AWP (Adverse Weather Pedestrian) dataset [DEBAD + 17][Dut17] was released recently3 . It is a very particular annotated image dataset since all images are obtained from a fully controlled environment. Each image shows exactly the same scene, as illustrated on figure 1, but under various controlled weather conditions. The interest of such a dataset relies on the precise knowledge of its content. Of course it might not be useful for real life machine learning applications, but we believe that it could help in the evaluation of machine learning methods, such as controlled image generation.

Cerema AWP datasheet

The idea of creating datasheets for datasets [GMV + 18] comes from several observations of major failures of machine learning systems due to biases or internal flaws in the dataset that were used to train them. Most of times, the misuse of datasets are unintentional and due to the fact that very few factual information is known about publicly available datasets. Such a document, with standard questions, would have at least two targets : on the one hand, the consumers of the data, who would be informed of how and what for were the data collected, and on the other hand, the providers who would have the opportunity to improve their dataset quality and maybe focus on point that would have been ignored otherwise (such as legal aspects or fairness).

We present the datasheet for Cerema AWP, as it is suggested in [GMV + 18] as an attempt to follow their guidelines and hopefully participate to this very interesting initiative aiming at bringing more fairness in machine learning. The datasheet is organized exactly as proposed and is placed at the end of the paper on page 7 for readability issues.

To sum-up the content of the dataset, it was originally designed to train more robust pedestrian detectors, starting from the observation that most of the existing datasets were containing favorable weather conditions. In a special installation (the Cerema Fog and Rain R&D platform ), a tunnel in which rain, fog and night can be artificially created, it was possible to take photos of 5 persons walking back and forth in the tunnel, wearing different clothes and under different weather conditions. Using classical detectors on those images, it was observed that indeed they were failing much more under adversarial weather conditions.

In this paper we are not interested directly on the pedestrian detection but we rather want to exhibit other interests of this particular dataset.

Tasks and baseline results

In this section, we present several identified tasks for the Cerema AWP and some baseline results. All presented baselines are convolutional neural networks, which are well adapted to image processing and large datasets. For each proposed model, we tried to produce the best models we could, using only standards layers.

In the following, we first present how we pre-processed and split the dataset.

Pre-processing

The dataset provided on the original website consists of raw images stored in a hierarchy of folders, which gives some of the labels:

• Level 1 : Weather conditions (5 with daylight, labels beginning with 'J'), 5 during night (labels beginning with 'N'). The 5 are good weather (labels 'JCN' and 'NCN'), low rain ('JP1','NP1'), heavy rain ('JP2','NP2'), little fog ('JB1','NB1'), heavy fog ('JB2','NB2')). • Level 2 : Pedestrian (5 different persons)

• Level 3 : Clothes (2 different jackets colors)
The other labels and information (such as the pedestrian direction, the bounding box or the exposure time) are provided in a separated csv file. This file also gives a redundant information concerning pedestrian and clothes, in the form of a new label that separates the dataset into 10 classes (combination of pedestrian identification and clothes).

We decided to export all the information (images and labels) in a single format in order to simplify the information management. The proposed script4 also provides an option for image downsizing. Here is a final format of the hdf5 file for Python: attribute 'width' : width of stored images attribute 'height' : height of stored images dataset 'train' : stores 85% of the dataset dataset 'test' : stores the remaining 15% of the dataset The split between train and test is done such that both sets have similar proportion of each class (10 classes weather, 10 classes pedestrian, 6 classes direction). On average, the test set contains about 12 example of each of the 600 combinations of classes. The information inside train and test is stored as follows:

weather : list of labels from 0 to 9 (order compared to the original dataset : ['JB1', 'JB2', 'JCN', 'JP1', 'JP2', 'NB1', 'NB2', 'NCN', 'NP1', 'NP2']) pedestrian : list of labels from 0 to 4 clothes : list of labels from 0 to 1 pedestrianClothes : list of labels from 0 to 9 (combination of the two previous ones) direction : list of labels from 0 to 5 exposition : list of reals boundingBox : list of size (nbElements, 4), [x,y,width,height] in proportion of height and width (originally this information is given in pixels, but since we propose to extract images at different scales of downsizing, we preferred proportions)

images : Numpy matrix (nbElements ×width×height×3) Unless mentioned otherwise, results are provided on images of size 1/4 of the original size (256x158). For all experiments, the train set is split randomly in order to have results on a 20% validation set.

Classification tasks

We applied standard convolutional networks, using Keras on Tensorflow, to each of the classification tasks. The summary of the architectures can be found in the annexe, the optimization algorithm is Adam with defaults parameters, except for the learning rate set at 10 -5. We expect the results to be very high on most of the classes: due to the large similarity between images, we can foresee that some sort of over-fitting will do well.

Easy classification tasks

We summarize here results for Weather, Pedestrian, Clothes, and Pedestri-anClothes, since each of them can be easily solved with validation performance higher than 99%. From the simplest (the weather, since the relevant information is mainly contained in the color range) for which 1 epoch is enough, to the least simple (Pedestrian-Clothes) which requires 10 epochs, we observe that there is no real challenge.

Classification on the Direction labels The direction label indicates how the pedestrian is shown on the image : face, back, left, right (close or far), diagonal from far right to close left. This classification task is less obvious since the algorithm has to focus on details on the pedestrian only. However it is possible that it can infer results from the position in the image because of the regularity of the path. After 10 epochs, the accuracy on the validation set is quite stable and stays at around 80%. We observe the confusion matrix (table 1) and see that some distinctions are not easy (like between 3 and 4 or between 5 and 6), which make sense since the positions in the image are similar for these couple of classes, but also between 1 and 2, which is more surprising. On the contrary, directions 1 and 2 are almost never mismatched with the other directions. From this we conclude that indeed the position in the image is used to detect the direction, but not only (5 and 6 are exactly at the same locations but are rather well distinguished overall).

Multi-label classification

We decided for this experiment to build an architecture that will share the convolutional part but have separate output for each class, in order to obtain multilabel classification. Labels that are taken here are weather, pedestrian, clothes, direction. For pedestrian and clothes, we decided to add a common layer as shown in the annexe. Based on the individual performance and number of required epochs of each classification tasks, the losses are weighted as follows :

• weather : 0.2 • pedestrian : 0.2 • clothes : 0.5

• direction : 1 We also trained the model without weighting losses to compare. From table 2, we observe that easy classes are perfectly detected in this setting, but also that the Direction detection seems to be improved. We suspect that sharing the feature extraction part with tasks that are directly related to the pedestrian characteristics (such as clothes) helps the model to focus on the interesting part of the image, which is the pedestrian. We also observe that the loss weighting has a positive impact on the Direction detection.

Regression

The bounding box information comes as a vector of size 4, containing (x,y) of the top left corner and (w,h), the width and height of the box. All values are between 0 and 1 and represent a percentage of image width and height. For this experiment we treat those vectors as a multi-regressor problem. The architecture is the similar to the classification tasks, with an output shape of 4. We achieve a mean square error (MSE) on the validation set of 0.0020 and a mean absolute error (MAE) of 0.0327. From those results it's hard to conclude on the interest of this tasks using only regression : it's not obvious that the machine actually learns to detect the pedestrian. In order to have a more precise idea, we decided to make another experiment in which we train on a selected subsample of the dataset, such that pedestrian in the validation set will appear at places that were not presented in the train set. To achieve this, we use the direction information and simulate a covariate shift problem [START_REF] Quionero-Candela | Dataset Shift in Machine Learning[END_REF]. Indeed, as illustrated on figure6, the directions are linked to the positions. We decided to exclude from the training set all images with directions 1 and 2 (labels 0 and 1). For the evaluation, we present only the images with directions 1 and 2, or images with any directions. This experiment confirms that the method can actually detect a pedestrian at an unusual place but with twice the amount of errors.

Image reconstruction and generation

Auto-encoders

From now on, we focus on image reconstruction and generation, starting with auto-encoders [START_REF] Hinton | Autoencoders, Minimum Description Length and Helmholtz Free Energy[END_REF]. The idea of autoencoders is to train a neural network that learns to reproduce its input as output, with a smaller size intermediate layer. The representation inside the 3 shows the obtained results, based on convolutional layers. We see that it is quite easy to make the autoencoder catch the information on the background and weather conditions. However, we were not able to make it print the pedestrians.

Figure 3: Examples of reconstructed images using auto-encoders. On the left columns are true images, on the right column are the generated images. We observe that no pedestrian is generated, only the recurrent background is reconstructed.

GAN

Generative adversarials Networks,GANs, were introduced in 2014 [GPAM + 14] and since then dragged the interest of many. The idea is to train simultaneously two networks the generator G and the discriminator D. Working for example with an image dataset, the aim of the discriminator D is predict whether or not an image presented to it is from the dataset or from the generator G. This simultaneous training of those networks can be seen as a two-player minimax game which has a unique solution. At that solution, neither the discriminator nor the generator can improve anymore. Hopefully, by the time the training get that optimal point, images generated can not be told apart by the discriminator, and that stands for humans too. Since the introduction of GANs many versions have been introduced but [LKM + 17] states that all those variants are at some points equivalent, we use a DCGAN-like [START_REF] Radford | Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks[END_REF] which is suitable in our experiments. It is well known that GANs are difficult to train, and this difficulty increase with the variability of the input images, and also their size.

Figure 4: Examples of generated images using a DC-GAN on images of size 256×158. The remarkable point is that the tunnel and car are well generated but the weather condition varies inside each image. As for the auto-encoder case, pedestrian are not easily generated.

Conditional GANs

For conditional GANs [START_REF] Mirza | Conditional Generative Adversarial Nets[END_REF], we use the same architecture as previously but with conditional inputs, and conditioning with the weather. As we can see on fig. 4, in the previous generated images, several weathers can be present in a single image, but that behaviour disappears when conditioning on the weather. We illustrate that using the CEREMA database which at first sight is a very simple database with a fixed background for each weather. We can also infer that the discriminator relies at first on the background to distinguish real images from generated ones. That can back the idea that the networks learn first the most general patterns before learning the particular ones [AJB + 17] . As we can see in the examples on fig. 5, while training our conditional DCGAN, the generator learn first to generate the background for the different weather conditions properly. And only after that, we can see the generator trying to print the pedestrian on the picture. This behavior is seen for all the settings we have tried so far.

Conclusion

To conclude this paper, we first summarize our baseline results for the Cerema AWP dataset. It is very likely that these score can be improved with more elaborated models or having the chance to run experiments on a bunch of GPU to practice a better model selection. However we think that establishing and publishing a fair baseline before applying fancy methods on this new dataset will produce higher quality results in time.

We summarize in table 4 the results on the test set for each supervised task. Those results were obtained once, for our best models selected on validation results. For results in the multi label setting, the first value in for unweighted costs and the second for the weighted costs.

The purpose of this paper was two fold :

• presenting a dataset that we believe can be useful for some systematic exploration of some algorithms properties, in an attempt of standardization,

• and establishing baseline results before any advanced exploitation, in order to reduce the bias in later results presentation.

This work was greatly influenced by current discussions inside and outside the community about fairness, results quality and comparability. While we know that publishing datasheets and baselines won't solve many issues, we believe that is at least a step in the good direction.

Our future work concerning this database is quite evidently to take advantage of its particularity to push generative models towards unexplored behaviors and to work on the quality evaluation of produced images, using a validation dataset, which is mainly impossible with existing standards dataset.

Datasheet for Cerema AWP

Motivation for Dataset Creation

Why was the dataset created? (e.g., was there a specific task in mind? was there a specific gap that needed to be filled?) The Cerema AWP dataset was created for the specific need of pedestrian detection under any weather and light condition. The argument was that most of the vision techniques work pretty well when the weather is good under the day light. This dataset aimed at evaluating the existing pedestrian detectors in various conditions, and at providing a dataset to train more efficient methods. What (other) tasks could the dataset be used for? By construction, there are many information included in the dataset : the weather condition, the pedestrian position, the pedestrian clothes color and the pedestrian direction. It could be then used for various classification, multi-label, regression tasks. Another application is the image generation field. Indeed, the dataset is large enough to train GANs or auto-encoders. With all the available labels, this dataset could be used to study different types of conditioning in image generation or information discovering.

Has the dataset been used for any tasks already? If so, where are the results so others can compare (e.g., links to published papers)? Currently the dataset was only used in the paper presenting it in the vision field [DEBAD + 17], to compare HOG and Haar detectors.

Who funded the creation of the dataset?

The Cerema organized the data collection. From the "README AND LICENCE" file available on the website: This work has been sponsored by the French government research program "Investissements d'Avenir" through the IMobS3 Laboratory of Excellence (ANR-10-LABX-16-01) and the RobotEx Equipment of Excellence (ANR-10-EQPX-44), by the European Union through the Regional Competitiveness and Employment program -2014-2020-(ERDF -AURA region) and by the AURA region. The Cerema also would like to thank Pascal Institute and the National Engineering School of Sousse. Any other comments? Available at https://ceremadlcfmds.wixsite.com/ cerema-databases.

Dataset Composition

What are the instances?(that is, examples; e.g., documents, images, people, countries) Are there multiple types of instances? (e.g., movies, users, ratings; people, interactions between them; nodes, edges) Instances are images produced in Fog and Rain R&D Platform. This platform is used for : reproduction and control of fog's particle size, of meteorological visibility, and of rain's particle size and intensity; physical characterization of natural and artificial fog and rain. The platform is used for various applications as development of algorithms for image analysis and processing in adverse weather conditions, application and development of new imaging technologies in the specific conditions of fog and rain (infrared, laser. . . ), performance testing of upcoming advance driver assistance systems (ADAS) including automatic obstacle detection features (lidar).5 . Are relationships between instances made explicit in the data? (e.g., social network links, user/movie ratings, etc.)?No How many instances are there? (of each type, if appropriate)? There are 51302 images. What data does each instance consist of ? "Raw" data (e.g., unprocessed text or images)?

Features/attributes? Is there a label/target associated with instances? If the instances related to people, are subpopulations identified (e.g., by age, gender, etc.) and what is their distribution? Images are raw images, in resolution 1024x631, which corresponds to standard resolution for cameras in cars. Concerning the image descriptions, some are available according to the folder organization (folder names give the classes)

• the weather : 10 classes, day and night, each with good weather, light rain, heavy rain, light fog, heavy fog

• the pedestrian : 5 groups, corresponding to 5 people who participated to the data collection : 3 women, 2 men.

• the clothes : 2 categories, each pedestrian appears with 2 outfits (one light and one dark)

Other labels are available in an external csv file:

• the bounding box containing the pedestrian (coordinates of the top left corner, height and width)

• the direction of the pedestrian : 6 directions, left close, right far and right close, forth, back and 1 diagonal. See figure 6 • pedestrian id : 10 groups, corresponding to each of the 5 persons with each of the 2 outfits. Be careful, some labels are set to 0 in the file (but it is easy to correct it from the pedestrian and clothes information.

Is

Data Collection Process

How was the data collected? (e.g., hardware apparatus/sensor, manual human curation, software program, software interface/API) The images were acquired by DFW-SX700 Sony camera at 7.5 fps and with a resolution of 1024x631 (aperture = 8.6 ; focus = 30m ; zoom = 8mm ; exposure time given for each image in the csv file).

Who was involved in the data collection process? (e.g., students, crowdworkers) and how were they compensated (e.g., how much were crowdworkers paid)? 3 permanent researchers, 2 permanent technicians and 1 trainee was involved for the technical preparation of data collection. 1 permanent technician, 1 PhD student, 1 postdoc and 2 trainees are pedestrians on the dataset. Labeling was performed by the permanent researcher and a trainee.

Over what time-frame was the data collected? Does the collection time-frame match the creation timeframe of the instances? It was collected between April 19th and 27th 2016. The real time-frame of the instance is given in the filename of each instance in format YYYY-MM-DD HH-MM-SS MS.

How was the data associated with each instance acquired? Was the data directly observable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/derived from other data (e.g., part of speech tags; model-based guesses for age or language)? If the latter two, were they validated/verified and if so how?

All information are directly observable and the collection process was done according to a scenario. There's no room for subjectivity. Does the dataset contain all possible instances? Or is it a sample (not necessarily random) of instances from a larger set?It contains all possible instances (all positions of the scenario for all pedestrian under all weather conditions).

If the dataset is a sample, then what is the population? What was the sampling strategy (e.g., deterministic, probabilistic with specific sampling probabilities)? Is the sample representative of the larger set (e.g., geographic coverage)? If not, why not (e.g., to cover a more diverse range of instances)? How does this affect possible uses? Not relevant.

Is there information missing from the dataset and why? (this does not include intentionally dropped instances; it might include, e.g., redacted text, withheld documents) Is this data missing because it was unavailable?No.

Any other comments?

Preprocessing

What preprocessing/cleaning was done? (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances) Was the "raw" data saved in addition to the preprocessed/cleaned data? (e.g., to support unanticipated future uses) Only raw data. Is the preprocessing software available? Not relevant. Does this dataset collection/processing procedure achieve the motivation for creating the dataset stated in the first section of this datasheet? If not, what are the limitations?Yes Any other comments?

Dataset Distribution

How will the dataset be distributed? (e.g., tarball on website, API, GitHub; does the data have a DOI and is it archived redundantly?) 3 rar files on the website.

Figure 1 :

 1 Figure 1: Examples of the Cerema AWP dataset. The same pedestrian appears roughly at the same place in different weather and light conditions.

Figure 2 :

 2 Figure 2: Examples of good and bad predicted bounding boxes. The red box is the true one, the blue box is the predicted one.

Figure 5 :

 5 Figure 5: Examples of generated images using a conditional DCGAN-like architecture on images of size 128 × 128. The weather is uniform on each image but the pedestrian is not well generated or ignored.

Figure 6 :

 6 Figure 6: Path taken by each of the pedestrian, indicating the directions.

Table 1 :

 1 Confusion matrix for the direction detection

	true/pred	1	2	3	4	5	6
	1	726	197	0	0	24	5
	2	145	1382	72	0	54	75
	3	8	155	754	118	11	9
	4	7	38	163	487	43	1
	5	6	9	2	28	1727	57
	6	0	14	0	4	182	1193

Table 2 :

 2 Results for different loss weights after 10 epochs

Table 3 :

 3 Results of the regression tasks when training with a subpart of the dataset, such that some pedes-

trian position do not appear in the training set. The two tests sets are composed by either only images of the remaining positions, or all images. We see from those results that the proposed method actually uses the position in the image to detect the pedestrian intermediate layer is a compression of the input. Autoencoders can be staked to obtain successive compressions of the input. Here we use autoencoders to evaluate how difficult it is for a neural network to generate our images. Figure

Table 4 :

 4 Final results of our baseline, on the test set.

cf https://github.com/hindupuravinash/the-gan-zoo

more detail about baselines and objective evaluation for GAN threadreaderapp.com/thread/978339478560415744.html

https://ceremadlcfmds.wixsite.com/cerema-databases

available on github https://github.com/gloosli/ ceremaAWP

More information -in Frenchhttps://www. cerema.fr/system/files/documents/technology2017/ Cerema-effi-sciences_Plateforme-9-10.pdf

Acknowledgment

This work has been partly supported by the grant ANR-16-CE23-0006 "Deep in France" and by the French National Research Agency within the "Investissement d'avenir" program via the LabEx IMobS3 (ANR-IO-LABX-16-01).

Dataset Maintenance

Who is supporting/hosting/maintaining the dataset? Cerema. Will the dataset be updated? If so, how often and by whom?No. How will updates be communicated? (e.g., mailing list, GitHub) Not relevant. Is there an erratum? No. If the dataset becomes obsolete how will this be communicated? Not relevant. Is there a repository to link to any/all papers/systems that use this dataset?No. If others want to extend/augment/build on this dataset, is there a mechanism for them to do so? If so, is there a process for tracking/assessing the quality of those contributions. What is the process for communicating/distributing these contributions to users?No. Any other comments?

Legal & Ethical Considerations

If the dataset relates to people (e.g., their attributes) or was generated by people, were they informed about the data collection? (e.g., datasets that collect writing, photos, interactions, transactions, etc.)Not relevant.

If it relates to people, were they told what the dataset would be used for and did they consent? If so, how? Were they provided with any mechanism to revoke their consent in the future or for certain uses?Not relevant.

If it relates to people, could this dataset expose people to harm or legal action? (e.g., financial social or otherwise) What was done to mitigate or reduce the potential for harm?Not relevant. If it relates to people, does it unfairly advantage or disadvantage a particular social group? In what ways? How was this mitigated? Not relevant. If it relates to people, were they provided with privacy guarantees? If so, what guarantees and how are these ensured?Not relevant. Does the dataset comply with the EU General Data Protection Regulation (GDPR)? Does it comply with any other standards, such as the US Equal Employment Opportunity Act?Not relevant. Does the dataset contain information that might be considered sensitive or confidential? (e.g., personally identifying information)No. Does the dataset contain information that might be considered inappropriate or offensive? No.

Any other comments?

A Architecture details

A.1 Classification and regression tasks

The provided architecture is for Weather and Pedestri-anClothes which are both 10 classes. For the other classification tasks, only the output is modified to match the number of classes or the number of regression values.