dataesr/scientific-paragraphs-categorization huggingface dataset:
Both for targeted and general LC mapping, methods should be adapted to various environments or at least should be easily transferable (genericity of the method). Additionally, they have to be locally relevant and at the same time globally consistent in order to achieve an accurate description of the national landscapes (genericity of the final product). Upscaling. Country-wide classifications are now required for VHR maps in order to become a reliable substitute to topographic databases and current workflows based on visual inspection of images. A specific focus should be made on the best trade-off between acceptable processing times/simplicity, and satisfactory spatial and semantic accuracy. Selection/evaluation of the remote sensing data sources. The first assessment challenge is related to the availability of remote sensing data at a regular basis through existing archives or dedicated Spatial Data Infrastructures.
Many national mapping and cadastral agencies were used to process a specific kind of data with a given time lapse between two acquisitions. New datasets with increasing temporal and spectral resolutions can be beneficial. Secondly, experimental sensors may also provide observations of different kinds with ad hoc surveys (e. g. , oblique imagery, hyperspectral images, or full-waveform lidar signals). Their relevance should be evaluated in order to assess whether they could be integrated in the existing workflows. Optimal exploitation of existing LC data. Existing landcover geodatabases are an invaluable input even if (i) they may not be up-to-date or accurate enough and (ii) the existing nomenclature may not fit with the current one  (Gressin et al. , 2014). Nevertheless, they should be exploited since they may offer a reliable replacement solution to human operators as reference data and for parameter tuning and learning tasks.
Duality. LC databases can be created for mapping and statistical purposes (e. g. , spatial indicators, or forest statistical inventory), that are not easily compatible: exhaustivity and representativeness are privileged, or conversely spatial accuracy. In a dual perspective, operational effectiveness should be able to deal with both objectives with few changes in the methodology. Many public policies, in particular related to urban areas (imperviousness), require both evolution metrics and accurate location of the underlying phenomena  (Costa et al. , 2018).
In parallel, open-access data has arrived  (Wulder, Coops, 2015). Satellite sensor archives are released for free, giving access to 30-40 years of collection of Earth Imagery (Landsat, SPOT (Ultré-Guerard, Boissin, 2015,  Wulder et al. , 2019) ). Initiatives at local scales exist, allowing to tackle issues for specific environments and to envisage knowledge transfer. New sensors also provide data for free, such as the Sentinel program for the European Space Agency (ESA). The other significant advantage is the global coverage of such datasets, allowing to collect observations almost wherever around the globe. Many national mapping agencies also release their own data, giving access to older images with higher spatial resolutions. Free and open data policy is expected to foster data reuse  (Zhu, Woodcock, 2014) , and the development of new services in Earth Observation.
The variety of passive and active sensors proliferates in terms of spatial, spectral, temporal resolutions  (Toth, Jóźków, 2016) : hyperspectral sensors, time-series of images, video sequences from small satellites or Unmanned Aerial Systems, full-waveform and multispectral lidar, radar with full polarimetric capability. The larger agility of spaceborne optical sensors (Pléiades, MISR, CHRIS, Worldview-2/3) allows to give access to multi-view multi-angular datasets. Digital cameras for airborne acquisitions are getting finer spatial resolutions with an increasing number of spectral bands. Oblique imagery is more widespread. Technological evolution has been driven by experience collected after first decades of remote sensing and geodatabase use. Subsequently, remote sensing is now inherently multi-modal  (Gomez-Chova et al.
, 2015) : complementary observations can be exploited and can mitigate limitations of a particular sensor  (Joshi et al. , 2016). This is the concept of virtual constellations  (Wulder et al. , 2019). Many national mapping agencies and local public bodies release reference topographic, land-cover and land-use databases for free at many levels (e. g. , Globeland30, ESA Copernicus program, US NLCD). The need for updating, diagnostic, and refinement has emerged since initial DBs are now established, leading to clearer requirements (see Section 2. 2). An increasing number of free and open-source processing softwares for image and 3D point cloud exploitation becomes available  (Christophe, Inglada, 2009). This allows to give access to core processing techniques (calibration, registration, classification, segmentation).
It permits to build higher level processing chains that can be easily evaluated with the growing availability of contest and benchmark datasets  (Rottensteiner et al. , 2014 , Braun et al. , 2015 , Maggiori et al. , 2017 , Demir et al. , 2018 , Azimi et al. , 2019 , IEEE GRSS Data Fusion Contest, 2020). However, such initiatives are not sufficiently spread and are still of limited scope: sensor-specific, few efficient computational capacities, limited interfacing with high-level programming languages etc. The situation is steadily improving with the great availability of deep neural network (DNN) frameworks  (Robinson et al. , 2019a) , within well-documented libraries  (Zhu et al. , 2017). It is accompanied by the set up of multiple High Performance Computing infrastructures (HPC) and libraries, mandatory so as to upscale proposed workflows, swallow the amount of geospatial images, and provide maps in decent times  (Chi et al.
Efficient classifiers The two expected features of classifiers are accuracy and scalability. Higher accuracies can be obtained with automatic or manual knowledge integration as well as a better use of existing land-cover and remote sensing datasets (next section). A better training stage? All workflows are based on the supervised classification of remote sensing image(s). Therefore, they heavily rely on existing reference data (Section 4. 2) and on efficient training procedures. Most fail today in dealing with imbalanced datasets and rare classes: at the data level, standard under/oversampling often lead to over-fitting or redundancy. Cost-sensitive learning is better suited. Continuously learning class costs leads to intractable optimization with large scale Criterion (↓) / Scenario (→) Experimental Operational mapping mapping HR VHR VHR today Spatial accuracy - - - - Semantic accuracy - - - - Updateness & change detection - - - - Automation - - - - Simplicity - - - - Versatility - - - - Upscaling - - - - RS data evaluation/fusion - - - - Exploitation of existing databases - - - - Table  1.
Main challenges in land-cover mapping. For operational cases, we distinguish global/High Resolution (HR), national/Very High Resolution (VHR), and VHR with current paradigm change (VHR today). and indicate the importance and the complexity of each criterion, respectively. The color quantifies each of them (low -medium -high). datasets  (Huang et al. , 2016). Authors prefer defining ad hoc losses, e. g. , with hard negative mining  (Dong et al. , 2017) , if possible supported by joint clustering  (Hayat et al. , 2019). Similarly, unseen classes are not handled when applying existing classifiers on new areas, preventing their upscaling ability. Fewshot learning and weakly supervised solutions  (Gidaris, Komodakis, 2019)  can cope with such issues, handle tail classes  (Liu et al. , 2019) , can help in domain adaptation tasks, and in reducing the required amount of training in DNN, in particular when integrating unlabelled yet useful data.
Exploitation of existing geospatial databases These are invaluable sources for training classifiers. They are both available at large scales with limited nomenclatures or over limited areas with fine-grained categorization. In the latter case, if one aims to adopt such set of classes, it becomes a weaklysupervised or a transfer learning issue. Most of the literature focuses in transferring existing knowledge to new sensors; additional effort should be put on the complementary task  (Redko et al. , 2019) , especially foreseeing the cases when reference data will no longer be available  (Tardy et al. , 2019). Furthermore, most LC DBs are organized in a hierarchical way (forests coniferous/deciduous/other). Such taxonomy has not been yet exploited in order to constraint the classification problem  (Verma et al.
Fostering applications Towards land-use mapping. This remains a remarkably ignored domain while it has been shown to be the most consuming and challenging task in geodatabase generation. Many reference data exist, allowing a suitable training step. The main bottleneck is not a correct local discrimination but an efficient partitioning of the space, especially in urban areas. This is again a semantic segmentation task. Finding borders between landuse classes is not trivial, which explains why researchers often complement their discriminative workflow with ground-based images  (Srivastava et al. , 2019). Land-cover dynamics monitoring. Mono-date classification is now longer sufficient and should be accompanied with change detection  (Wulder et al. , 2018). For robust multi-date comparisons, the challenges consist first in integrating into the (structured) classifier the knowledge about the conceivable trajectory of changes  (Bailly et al.
ISO is the most used solvent for CMD as it is able to interact with hydrophobic protein sites  [23]. DMSO and ETOH are chosen for their hydrogen-bond acceptor and donor capabilities respectively  [24, 25]. The cosolvents were also chosen to be fully water miscible to avoid molecules aggregations phenomena and the concentration was fixed to avoid protein denaturation  [26]. The models of the inactive and active EPAC1 conformations were generated by homology from the EPAC2 crystal structure (inactive state: PDB code 2BYV  [27] ; active state PDB code 3CF6  [6] ). To evaluate that the unfolding phenomena did not affect the protein during the simulations the RMSD of the backbone Cα was computed  [28]. The RMSD values showed that cosolvents molecules did not disturb the protein folding (Figs. S1, S2 ).
EPAC structures The EPAC structure was downloaded from the PDB data bank (http://www. rcsb. org/), inactive state: PDB code 2BYV;  [27]  active state PDB code 3CF6  [6]. Homology model were carried out by homology model PRIME  [29]  of MAESTRO  [30]. The obtained models were examined to avoid steric clashes, bond length deviations and angles, etc. , by using the Ramachandran plot and the Protein Preparation Wizard. [31]  The missing loops were obtained by Raptor X  [32, 33]. The primary sequence of EPAC1 and EPAC2 was downloaded by UniProtKB (https ://www. unipr ot. org) code: O95398NCBI and UniProtKB code: Q8WZA2 respectively.
PCA The PCA analyses were carried out by cpptraj program  [40]. The trajectory was firstly superimposed to an average structure obtained by cpptraj. Then, the trajectory of the a-carbon was extracted and the covariance matrix calculated. The diagonalization of the covariance matrix generated a set of eigenvectors that gave a vectorial description of the motion. The computed eigenvectors and eigenvalues were used to draw the porcupine plot by means of the Normal Mode Wizard module (NMWiz)  [41, 42]  of the VMD gui  [43].
Molecular docking Ligand structures were built with MAESTRO  [30]  and minimized using the OPLS3 force field until a rmsd gradient of 0. 05 kcal/(mol Å) was reached. The docking simulations were performed using Gold,  [44]  Plants  [45]  and Autodock  [46]. We set a binding lattice of radius or side large enough to cover the whole studied maps, then all default settings were used. The Docking of the AM-001 analogues were carried out by Plants  [45] , using as receptor a representative structure obtained by the trajectory of the EPAC1/posa3_6. The representative structure was the average structure extracted by cpptraj  [40]  after a stable rmsd was reached. The poses binding free energy was calculated by MAESTRO prime MMGB-SA module  [47]. The pictures reported in the manuscript were done with Pymol  [48].
Analyses of active conformation occupancy maps The active conformation of the enzyme was mainly studied to access the quality of the method. The inhibition of EPAC1 by AM-001 was previously determined by BRET assays  [17]. Indeed, the experiment measured the level of activation of the EPAC1, namely the shift from inactive to active form of the enzyme after the antagonist binding. The cosolvent occupancy maps were computed with trajectories of 100 ns (Fig. S3 ) and 250 ns (data not shown). The occupancy maps analyses for the EPAC1 active conformation led to the identification of four interesting zones signed as areas from 1 to 4 (Fig. 2 ) regardless of the trajectories length. The areas 1 and 2 were located at the CDC25-HD domain and corresponded to the Rap binding site  [6]. Indeed, the superimposition of the EPAC2 crystal structure (PDB code: 4MGI)  [49] , bearing Rap interacting domain, with the EPAC1 model clearly showed that the areas 1 and 2 were filled by this domain.
These data were used to reject the area located at the CDC25-HD and the RA domains. Indeed, the areas appear to be too far from both the lid and hinge helix to influence the inactive to active conformation state. This observation led us to managed 7 areas (Fig. 3 ). Area 1 was the cAMP binding pocket  [6]. The identification of the cAMP site further validated the reliability of the method. The remaining areas were inspected by docking and molecular dynamics. Area 2 was located at the CNBD close to the catalytic site and it encompassed CNDB α3 and the following loop (245-250), α4 (251-260) and α6 (344-350). Area 3 was at the interface between CNBD and REM domains. This site is delimited by REM β1 (387-392) and α1 (398-402) together with the CNBD β9 (353-359) and β10 (363-368). Area 4 involved the HP loop of CDC25-HD and the REM α2 (423-430).
Area 5, was the only one entirely located at the CDC25-HD comprising the α2 (689-692), α3 (704-708), α5 (761-765) and α9 (815-827). The area 6 was at the interface of CDCD25-HD and REM. The area was defined by the α8 (800-805), α9 (817-822) and α10 (844-851) of CDC25-HD and the loop between α2 and α3 (247-251) of the CNDB. Lastly, area 7 was located between the CDC25-HD and the CNDB domains involving the α8 (793-801) of the CDC25-HD and the long helix (206-216) linking CNDB to the Dep domain. (Fig. S7 ) Thus, AM-001 was docked to each of the listed areas. The docking studies were carried out with Gold,  [44]  Plants  [45]  and Autodock  [46]. For each area, at least one consistent AM-001 binding pose (RMSD < 1. 5 Å) among the docking software was selected and submitted to molecular dynamic simulations (10 ns).
More recently  7  , describe a stereo vision technique to measure the water surface topography. They used a conventional stereographic technique algorithms to survey geodetic surfaces and static objects. Based on that  8  , proposed a partially supervised technique (Wave Acquisition Stereo System, WASS) to estimate the 3-D shape of water waves using video image analysis with high spatial and temporal resolutions. Despite the basic principle remains more or less the same  9  , optimized the  8  technique and presented the first open source code version of WASS (http://www. dais. unive. it/wass/). Since the work by  8  , WASS and others similar stereo imaging systems have been widely used for different investigation purposes on ocean waves. For example  [10] [11] [12]  : used the foam footprint in the surface reflection to identified wave breaking in the video records, with that is possible observe the space and time evolution of breaking waves and investigate different properties of wave energy dissipation  [13] [14] [15] 16  used stereo data to study the shape and likelihood of the highest waves, including maximum and rogue waves  17, 18  ; used the 3-D spectrum to explore non-linear waves properties like bound waves, second-order and harmonics, wave-current interactions and bi-modality.
We considered the common pinhole camera model with a 5-degrees polynomial radial distortion, resulting in a total of 9 parameters, namely: the focal length (2 parameters, to account for non-square pixels), the position of the principal point (2 parameters) and the 5 parameters used to model the radial and tangential distortion. To estimate them, the intrinsic calibration was performed on each camera before installation consisting in the acquisition of a known chessboard target in dozens of different poses (Fig. 2 ). We used the Camera Calibration Toolbox for Matlab, provided by Jean-Yves Bouguet (www. vision. caltech. edu/bouguetj), implementing the method described in  23.
3-D reconstruction and Wave Field estimation. Each record of the data set is composed by a sequence of stereo frames and the intrinsic calibration data for each camera-lens system. The process used to reconstruct the spatio-temporal wave field is divided in three subsequent steps. First, the relative spatial position of the two cameras (ie. the extrinsic calibration) is recovered automatically by analyzing a subset of stereo frames. Second, each stereo pair is processed to reconstruct a dense 3-D point cloud of the observed sea surface. Finally, each point cloud is filtered, cleaned and interpolated on a regular grid to produce a data cube containing the spatial surface elevation over time. We used the WASS pipeline  9  for the first two-steps and Matlab ® scripts for the last one. The principle of stereo reconstruction is easy to understand.
e. valid) with respect to the estimated pose; (ii) the average epipolar error of the inlier matches (ie. how coherent are the inliers wrt. the epipolar constraint). In all our sequences we obtained hundreds of matches with an average epipolar error ranging from 0. 3 to 0. 5 pixels. Considered that the epipolar constraint is subject to just 5 degrees of freedom, we believe that such high number of correct matches is a good indication of an actual good calibration of the extrinsic parameters. After the extrinsic calibration, the wass_stereo program is executed for each stereo pair to produce a dense point cloud. The number of reconstructed points vary with the scene but is ~3 × 10 6 points (for 5 Megapixel cameras). A plane is fitted in a least-squares sense to each 3-D point cloud to produce an estimate of the mean sea plane position in the right camera reference frame.
If a sequence of image pairs was collected, the above-mentioned operations are repeated at each instant and the final result will be a (2-D + time) burst of sea surface elevations. An examples of a typical stereo-image pair and the measured 3-D surface geometry can be seen in Fig. 4. experimental setup. Each wave data set presented in this study was acquired using an experiment setup ad-hoc designed to best represent its initial research purpose. At the time of this publication, this data set contains twelve records: seven recorded at the Marine Hydrophysical Institute platform in the Black Sea; three recorded from the Acqua Alta oceanographic research tower in the Adriatic Sea; one recorded at the Sogheongho Ocean Research Station in the Yellow Sea; one recorded at La Jument lighthouse in the Iroise Sea.
The cameras were installed on the eastern corner of the deck of the platform, point to a direction 82° clockwise of North. Additionally, in the other side of the platform the sea surface elevation was also measured at a 10 Hz by six restive strings arranged in a centered pentagon of radius 25 cm recorded sea surface elevations with an accuracy of 2 mm providing the data for frequency-directional spectrum estimations in the rage of 0. 1-1 Hz. A more detailed description of the platform instrumentation is given by  25. The wind speed and direction, air temperature, humidity, pressure and water temperature are measured at the center of the platform, 23 m above the sea level by a "Davis Vantage Pro 2" meteorological station. acqua alta (aa). The Acqua Alta stereo data set was collected using an imaging system installed on the north-east side of the Acqua Alta oceanographic research tower, located in the northern Adriatic Sea (Italy,   45.
Yellow Sea (YS). The Yellow Sea data were collected from the Socheongcho Oceanic Research Station (37°25′23. 28″N 124°44′16. 94″E), managed by the Korea Institute of Ocean Science and Technology. A couple of visible-range, synchronized digital cameras (8-bit grayscale, with 2456 × 2048 pixel resolution and 3. 45 μm square active elements) mounting 8 mm distortion lenses, were placed 5. 04 m apart and 33 m above the mean sea level on the east side of the station. The cameras set looking to east (90°N clockwise) and were rotated down-looking with an inclination angle of about 25°. At the station, the local depth is on average 50 m, the sea current spectrum is dominated by semi-diurnal tides, with a tidal ellipse having principal axis oriented northwest-southeast, and maximum speed along that direction in excess of 1 m/s.
La Jument (LJ). La Jument, lighthouse is located offshore of Ushant island, Western Brittany, Iroise Sea, France (48°25′20. 00″N 5°8′2. 28″W). This lighthouse is extreme exposition to North East Atlantic storms waves and strong tidal currents. The waves that shoal over the very steep up-wave bathymetry can violently break over the lighthouse and generate sea spray that can go up well over the lighthouse lantern. The stereo video data was collected there is part of the DiMe project dealing with the characterization of extreme breaking waves. This lighthouse lies over a rocky platform that drops abruptly on its western boundary. The upper deck of the lighthouse is located at about 45 m above the mean sea level, but it is very sensitive to a macro-tidal conditions that can range up to 8 m. This makes possible the observation of very high sea states in intermediate to shallow waters with a large field of view and a minimal shadowing effect.
AA stereo images portray space-time wave fields generated by prevailing east northeasterly winds producing, generally, fetch-limited waves in the northern Adriatic Sea. The AA 01 record was acquired during a cross-sea condition, being the dominant wave components aligned with the local wind (ENE) while a swell was produced by southeasterly winds blowing in the central Adriatic Sea. Breaking events at different scales and directions are visible in the stereo images. Space-time wave fields show the presence of second-order harmonics and a bimodal distribution of the short-wave energy. These data were used to investigate the benefit of using stereo observations to calibrate the X-band marine radar MTF  31. Wave elevations stored in the NetCDF4 file were low-pass filtered at 1. 5 Hz to remove the influence of high-frequency quantization noise.
AA 02 data were acquired during a ENE wind-forced condition. Wave breaking occur for dominant waves (along the peak direction) and for short waves (angled with respect the peak direction). Occasionally foam stripes aligned with the wind are detectable. AA 03 consists of easterly wave fields. Very few breakers are visible within the images. YS data include wind-generated 3-D wave fields observed during the passage of an atmospheric front, which led to a wide directional spreading of wave energy. Two independent wave systems were detected in the data, clearly identified in the 3-D wavenumber/frequency spectrum E k k f ( , , ) x y. Using this record  27  , examined the shape and the nonlinear properties of the wavenumber/frequency 3-D wave spectrum, and the characteristic spatial, temporal and spatio-temporal length scales of the wave field.