dataesr/scientific-paragraphs-categorization huggingface dataset:
Self-Supervised Learning Approach Analysis We train the ship detector on the S2-SHIPS dataset in the leave-one-out setting: out of N images we choose N -1 for training and one for testing. For certain geographical regions there are several images in the dataset while for others only one. Therefore, by varying the testing and training images we measure the transferability of the learned detector, for different levels of domain difference between training and testing sets. We do not perform cross-validation, the hyperparameters for U-Net are chosen a priori and not optimized. We obtain 16 folds with 15 training images and one testing image. The training set consists of patches that match the ship presence criterion described in Section 2. 4, extracted from the original images. This sampling produced about 1800 patches on average per fold.
We report precision, recall, and F1-score averaged over the 16 folds, averaged over 5 runs of the experiments. We do not report standard deviations as they were always insignificant. Our initial aim is to evaluate the overall performance of the detector, irrespective of land cover in the images. Thus, we first test without the filtering stage, and the precision results reflect false positives both on land and at sea. Table  2  presents the results of this comparison. First, we can observe that there is a strong relationship between the dataset size and the performance attained. To see this in more detail, in Figure  6  we show graphically the difference in F1 score depending on the size of the pre-training dataset, under the (A)ugmentation pre-text task, using transfer learning (TL). Using the large SEN12MS dataset we obtain four percentage points of F1 score more than with EuroSAT for the same pretext task.
As the threshold of the NDWI is chosen to maximize performance over the whole dataset, it is suboptimal on individual images. For deep learning methods, such as SEN12MS-RA, generalizing problems are evidenced on images Suez 2, Panama, and Rotterdam 2, but this is mainly due to the challenging conditions induced by those test images, rather than domain difference : large land cover (Suez canal), clouds (Rotterdam, Panama), and turbulent sea (Panama). Our SSL based pipeline achieves similar results to ImageNet pre-training in the transfer learning setting and two percentage points higher F1 score in the fine-tuning setting. It is worth noticing that training from scratch achieved better F1 score in the fine-tuning setting. When training on 15 images, learning from scratch seems a better choice due to simplicity and better performance.
Few-Shot Performance of the Methods under Study To evaluate the performance dynamics of the proposed method in the few-shot learning setting we split the dataset into two parts. The training set contains 13 images and the test set three: one from the Suez canal, a second from Brest and the last one from Rotterdam. We vary the number of training images from 1 to 13, which corresponds to a variation in the number of distinct ships from 42 to 742. This experiment aims to highlight the network's robustness towards training dataset size and diversity (sun glint, water color, etc. ). Varying the number of training samples shows that SSL methods trained on large datasets, especially with the region based pretext task, achieve competitive and even better results than ImageNet pretrained networks fine-tuned on a small amount of data.
We notice also that performance increases with the size of the pre-training datasets. Since these are not annotated it is easy to build such datasets. The ones chosen here have no relation to the ship detection problem at hand, thus no significant effort is needed to select the images in these datasets. The pretext task needs to be chosen according to the downstream task in order to learn the needed invariances. The region based pretext task looks promising probably because it helps the encoder to better cluster together similar elements, such as water, agriculture crops, or residential areas, while a simple pretext task data-augmentation only focuses on color or noise invariances. The benefit of such pretext task can be seen in our case as it lowers the number of false positives over land and near the shore.
In terms of computational complexity, the difference between all deep learning methods lies in the way we pre-train the weights. Compared to the supervised pipelines that can be used to train a ResNet-50 on ImageNet dataset, the self-supervised pipeline has a similar complexity but requires much larger batch size. This is particularly problematic for multi-spectral images, and a GPU with at least 8 Go of memory is necessary. The pretext task training is time-consuming: it took us nearly two days to train it on SEN12MS dataset using a multi-GPU machine (4 GPU with a total of 64 Go of memory). Training on a single desktop GPU with at least 8 Go of memory is feasible, but lasts longer.
The SSL pipeline can be applied to networks where no ImageNet pretraining is available, such as custom architectures specific to remote sensing. Thus, an interesting research goal would be training, through SSL, a feature extractor designed for remotesensing applications, that improves upon learning from scratch or ImageNet pretraining by a large margin. For ship detection, our image-based approach is still two orders of magnitude away from the false alert performance of methods applied on SAR images  [1]. To improve image-based ship detection, a better network backbone could be studied, more adapted to small objects. Furthermore, it would be better to include cloud filtering and land/water classification explicitly in the network. Although adding more data is always a good idea, the few-shot setting is more challenging and can bring about more methodological improvements in deep learning for remote sensing.
Arabesque is an application for the exploration and geovisualisation of origin-destination flows (or spatial networks), developed within the framework of the Univ. Gustave Eiffel (ex. IFSTTAR)-funded research project geographic flow visualisation (gflowiz) geoflowiz, in collaboration with the CNRS. It allows both the exploration and the filtering of OD data and their representation, with a strong emphasis on geographic information layering and features' semiology. The key-objective is to propose an easy way to produce a modern cartography (a geovisualisation) of thematic flows (e. g. bilateral flow volume), at several geographic scales, even from your own datasets. The objective of this article is to position Arabesque in the range of geoweb applications for producing flow maps, by comparing its functionalities with those of similar web applications-Magrit, Kepler.
gl, flowmap. blue -pointing out their respective advantages and limitations. The analysis of its functionalities is compared on the same flow dataset -MOBSCO, i. e. a dataset describing the school mobility of French pupils and students on a given year -for a practical and empirical "validation" of its contributions. We demonstrate that the configurations and appearances of these tools' visual output depend largely on the culture of their developers, and on the use and audiences for which they have been developed.
To provide a practical and empirical "validation" of Arabesque contribution and specificity, we first select a small number of flow mapping tools sharing common characteristics with Arabesque and second, compare these tools using the same flow dataset: MOBSCO, i. e. a dataset describing the school mobility of French pupils and students on a given year. In doing so, we highlight the complementary effects between these tools which, for the same set of data, make it possible to obtain different types of representations. Those configurations and appearances depend largely on the culture of the developers of these tools, and on the use and audiences for which they have been developed. The differences between these tools bring out two different cultures: that of thematic mapping and statistical analysis of information and the more 'dataviz' oriented culture of information exploration.
The development of online web mapping tools There are currently a growing number of computer solutions for mapping flows and origindestination (OD) matrix with one's own data. According to their environment, these tools can be classified into 3 main families: 1) desktop GIS softwares (e. g. QGIS, ArcGIS); 2) programming languages with (geo)data analysis capabilities (e. g. Python/R); 3) online tools with user-friendly interfaces. As demonstrated in a recently published paper  (Bahoken et al. , 2020) , the latter category has tended to develop further in recent years. This dynamics is concomitant with and part of a wider phenomenon: the rise of the geoweb  (Hackley et al. , 2008; Mericksay and Roche, 2011). But among online mapping tools, flow mapping tools tended to emerge in greater numbers from the mid-2010s onwards, taking advantage of the opportunities offered by the release of feature libraries such as D3  (Botstock et al.
, 2011)  and new technologies such as WebGL or vector tiles that opened up a range of new possibilities for online graphics and mapping. The growing attraction to open source tools and replicable approaches in the world of mapping and geomatics also fostered this dynamic  (Giraud and Lambert, 2017). In a recent contribution, Bahoken (2020) provided a review of about 80 flow-mapping web applications resulting from a digital curation. Among all these applications, only a minority are offering users the possibility of importing, analysing, exploring and mapping their own dataset. Author's manuscript. Proceedings of the 2021 edition of the FOSS4G 2021 -Academic Track, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS). ________________________________________________________________________________________________________________ The majority of retrieved applications only allows the exploration of a preselected dataset on a certain thematic (i.
The MOBSCO dataset To perform our comparison of Kepler. gl, Magrit and Flowmap. blue with Arabesque, we selected a flow dataset that will be visualized in each software. For this purpose, we choose a dataset which remains little known. It is the detailed file of the French population census describing the school mobility of individuals and, more specifically, commuting to and from the place of study (MOBSCO). In spite of its great thematic interest for mobility researchers in France, it has not been the object of much exploitation and valorisation until now. It is a product of the French National Institute for Statistics and Economic Studies (INSEE) and is available online. It comprises two sorting variables: the municipality of residence and that of the place of study (including in bordering countries).
Its scope encompasses individuals enrolled in an education establishment and aged 2 years or over. The source file contains 4,730,521 records and 22 variables. For each statistical individual, it lists its municipality of residence, its place of study, its age group, and highest diploma, the socio-professional category of the households he belongs to, the number of persons and cars of the households. The size of the dataset being too large for online flow mapping tools, we preformatted and aggregated it at the departmental level (higher administrative level than municipalities). We also filtered the data to only keep records for students only (higher diploma is at least the baccalaureat), because they are more likely to move outside their municipality of residence to study. The final dataset contains 27090 records.
Benchmarking/Use test In this section, we are testing 4 flowmap geoweb tools for OD flows that are interfaced on the web, with the same dataset, in order to compare them, both in terms of ergonomics and rendering. Author's manuscript. Proceedings of the 2021 edition of the FOSS4G 2021 -Academic Track, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS). ________________________________________________________________________________________________________________ The objective is to position Arabesque in the range of geoweb applications for producing flow maps, by comparing its functionalities with those of comparable applications, pointing out their respective advantages and limitations. Another interest of this comparative approach is to show the dependence of the rendering: from the type and style of the map to the tool.
This dependence is also linked to the audience for which the application is intended, depending on whether its interest will be focused on the exploration and analysis of cartographic data or on cartographic editing. In the first subsection, we introduce the 4 applications, their specificities, the audience and use type for which they were developed. In the second subsection, we present the result of the comparative test we performed with the MOBSCO dataset, and in the third subsection we present a summary diagram showing the specificities of each application at each step of the data processing chain, from data pre-formatting to exporting, including the different analysis options available.
Kepler. gl Launched in 2018, Kepler. gl is an open source geovisualisation tool designed for complex datasets (large, spatio-temporal, 3D). Built with Deck. gl  (Wang, 2019) , Kepler. gl uses WebGL to display large data sets in multiple forms quickly and efficiently. For example, it provides dot maps, choropleth maps, heatmaps, clusters or even 3D grids  (He, 2020). Developed by the technology company Uber, this application allows both exploring and mapping large and complex geographic datasets thanks to client-side rendering. All layer geometry calculations are GPU accelerated, smoothly rendering millions of points and making kepler. gl a much more powerful web tool than traditional mapping software and other web mapping solutions. The target audience is that of data journalists and data scientists whose objective is above all to explore large data sets and not to correctly represent flow logics.
Magrit This thematic web mapping application was launched in 2017  (Viry et al. , 2018). It is free under the CeCILL licence and compatible with the GNU GPL. Mainly developed in Python and modern JS (ES6) Javascript using notably the d3. js library, its code is already open. Magrit is mainly intended for social scientists and thematic cartographers as it is firstly designed to teach statistical cartography, respecting the rules of cartographic semiology  (Bertin, 1967). To do so, it offers 12 types of maps (e. g. choropleth and smoothed choropleth, proportional symbols, waffle, cartogram, discontinuity) according to different projections. One of them allows to represent flows, but in a reduced version: only in the form of hub lines with weighted symmetrical links.
Use test of the flow mapping web applications As said before, we loaded the dataset we subsetted from the MOBSCO dataset. Then we perform some manipulations in order to test the filtering and rendering capabilities of each software. The figures from 1 to 7 show, for each application, its interface and the final result obtained. In order to highlight the capabilities of each software, we have chosen not to show the default displays, which are generally quite unreadable with OD data and have preferred to highlight maps that are more elaborate.
Arabesque Arabesque starts by loading the links data, and allows the user to specify ID columns, the flow (called volume) column and, if needed, an aggregation function. The next step consists in loading location data or to use a preset location dataset. When joining the two datasets, it will display a message indicating how many links were removed if the location of a node is not available (i. e. missing data). The layer panel offers functionalities to add, remove and organize layers. From there, you can change the map projection (default is webmercator but others are available) or the title of the map. For each layer, if available, you can access customisation panels (see Figure  2 ) to change the colour scale, the links shape and size, etc. The right panel is dedicated to the data. By default, a histogram represents the distribution of the flows.
Summary diagram We tried to present in a synthetic way the different steps involved by the flow mapping web-tools to explore a flow dataset. These steps are organized in five main tasks: data pre-processing, data processing, mapping, exploration and export. The diagram (Figure  8 ) therefore shows the workflow of a flow map from data to a finalized map and the functionalities offered by each application at each step. To complement this synthetic view, we detail in the next paragraphs the main differences between the functionalities offered by the tools to perform these five main tasks.
Data processing Once integrated, the flows and the spatial data are joined in an Origin-Destination matrix to be processed. Depending on the data, the resulting matrix can be asymmetric, the flows will be directed. If it is symmetrical, the flows will not be directed. Arabesque manages both types of flows. Flowmap. blue handles only directed flows while Magrit and Kepler. gl considers that the flows are not directed. This is an issue because only the most important outgoing flow between two nodes is represented. This limitation will prevent a fine analysis of bilateral flows. Some tools calculate new indicators. Arabesque automatically calculates the flow distances and common graph statistics for the nodes: inbound and outbound degrees and balance (weighted by the flows or not). Flowmap. blue automatically calculates a new indicator that is used for its clustering function, but these new values are not accessible to the user.
Mapping Once the Origin-Destination matrix has been created and the possible indicators calculated, the software will produce the representation. It will then be customizable or not. Arabesque and Kepler. gl allow for further styling of the map whereas Flowmap. blue allows only one type of representation. Magrit proposes several representation modes but only allows the link map for OD data. Arabesque and Magrit offer advanced graphic styles, i. e. it is possible with these tools to modify the style of the links (shape, colour, orientation). Background information can also be added to the map to provide context, either from web resources (Arabesque, Flowmap. blue, Kepler. gl) or from vector files provided by the user  (Arabesque, Kepler. gl, Magrit). Note that only Arabesque and Magrit allow to change the map projection.
Flowmap. blue and Kepler. gl are limited and can only Author's manuscript. Proceedings of the 2021 edition of the FOSS4G 2021 -Academic Track, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS). ________________________________________________________________________________________________________________ produce maps in Web Mercator projection. This may be useful for flow data analysis with global datasets but for local datasets a local projection might be wanted for mapping.
Exploration The data exploration possibilities are, along with the representation/styling possibilities, where the softwares differ the most. The four applications studied in this article offer data sorting and ordering functions. But some features are specific to certain applications. Flowmap. blue, Kepler. gl and Magrit offer functions to display labels and tooltips to provide information about the objects studied. This is a feature missing in Arabesque. Arabesque and Kepler. gl offer an interesting feature for data exploration: the graph query. By manipulating a window on a graph, this feature allows to hide part of the data in order to highlight the data included in the window. Kepler. gl also offers a brushing function (brush), which displays only the links present in a radius around the cursor, the other data being hidden.
Again, this feature can be useful in an exploratory context or during a demonstration. Finally, Flowmap. blue offers an original feature of spatial aggregation (clustering), that allows to easily explore the flow at different spatial scales. This aggregation is possible thanks to the calculation of an indicator, not accessible to the user, which allows to aggregate/disaggregate the flows when zooming. Everything is done automatically by the application, the user can activate or deactivate the functionality or choose one of the available aggregation levels. The number of aggregation levels is calculated according to the data by the application.
Export The export functionalities are also very diverse. The studied applications, with the exception of Flowmap. blue, offer the possibility to export the map in raster (PNG, JPEG) or vector (SVG) image formats. Kepler. gl and Flowmap. blue also offer an online hosting service in order to publish the map on the Web. Arabesque, Kepler. gl and Magrit allow you to save and export your project data and settings in order to be able to reload them afterwards. This is useful to be able to continue the analysis later or share it with a third party and is a great benefit for reproducibility.
Rigor vs design From the same type of data (OD), these 4 applications based on offer quite varied representation modes (hub lines, arcs, arrows, curved lines). The cartographic "rigor" is not supported by all the tools in the same way and from the same perspective. Arabesque and Magrit, developed by cartographers, are intended for "informed" audiences who are more "rigorous" in terms of graphic semiology. Flowmap. blue and Kepler. gl offer less rigorous but more aesthetic modes of representation, whose design is in line with current trends in online cartography and infoviz. Author's manuscript. Proceedings of the 2021 edition of the FOSS4G 2021 -Academic Track, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS). ________________________________________________________________________________________________________________.
Analysis, exploration or representation? Beyond the types of maps, these online applications mainly refer to three types of approaches around flow data: analysis, representation and exploration. Arabesque is the only application offering some flow data analysis functionalities (multivariate filtering, aggregation, selection on distances, network analysis indicators). The exploration dimension through dynamic data interaction (filtering, brushing, hovering) is at the heart of Kepler, Arabesque and Flowmap. blue. In these three interfaces, the user can move around, zoom in, and query elements. Magrit, on the other hand, is more focused on the production of static maps but do not allow navigation and exploration.
Interoperability and reproducibility About the data preparation, 3 out of 4 tools are using data formatted as two separate tables: one for the nodes (geographical points) and one for the links (thematic information Depending on the tools, the dissemination possibilities are very different. While some tools (Kepler. gl, Flowmap. blue) offer to host the results online, others (Arabesque, Magrit) offer to export the map as an image. The choice of the tool can therefore be based on the intended purpose of its use: a dynamic web map or a static illustration for a scientific article or report. The capability to export the parameters of a project can then constitute a real bonus to transmit the whole work to collaborators and contribute to ease the reproducibility of the analyses. In 2016, an experiment reproducibility issue has been identified  (Baker, 2016).