dataesr/scientific-paragraphs-categorization huggingface dataset:
S. Integrated Ocean Observing System's Animal Telemetry Network (IOOS ATN [2] ). These programs aim to provide near real-time ocean measurements via the World Meteorological Organization's Global Telecommunication System for assimilation in operational ocean and atmospheric forecast models. In all these cases, near real-time telemetry data provision requires rapid and therefore automated, reliable quality control processes, including the error-prone Argos location data that are essential for understanding animal movements and distribution, and for providing geospatial context to ocean measurements. Here we present a continuous-time state-space model for rapid filtering of any Argos location data. This model is now used as part of the IMOS ATF's quality control/quality assurance process for animal-borne ocean observations.
Argos Kalman filter and Kalman smoother data Locations measured using CLS' Kalman filter (KF) or Kalman smoother (KS) algorithms have their estimated uncertainties provided to users as error ellipses  [11]. Ellipses are defined by three variables: semi-major axis, semi-minor axis and semimajor axis orientation from north. Building on McClintock et al. [14] , the error variance-covariance matrix is: Ω i = τ 2 x,i τ xy,i τ xy,i τ 2 y,i (5) with the elements being derived from the Argos error ellipse components: τ 2 x,i = M i √ 2 2 sin 2 c i + m i ψ √ 2 2 cos 2 c i (6) τ 2 y,i = M i √ 2 2 cos 2 c i + m i ψ √ 2 2 sin 2 c i (7) and τ xy,i = M 2 i -m 2 i ψ 2 2 cos c i sin c i (8) where M i is the ellipse semi-major axis length of the i-th observation, m i is the semi-minor axis length and c i is the semi-major axis orientation  [11, 14].
Estimation We used the R package TMB (Template Model Builder,  [23] ) to fit the state-space model, using maximum likelihood to estimate model parameters and the Laplace approximation to rapidly estimate the random effects -the unobserved location and velocity states, x and v  [5, 24]. Using this estimation approach, uncertainty in x and v estimates are obtained using a generalised delta method (see  [23]  for details). All model and associated general data preparation code are available in the foieGras R package  [25]. The latest version can be downloaded from the lead author's GitHub site (https://github. com/ianjonsen/foieGras).
Data and pre-processing We model all three types of Argos satellite location data: LS, KF, and KS. The data are comprised of four pinnipeds, one seabird and two sea turtle species (Table  1 ); with deployment locations ranging between polar, temperate, and tropical marine regions (Figure  S1 ). The number of individual data sets by species and data type range from 6 to 13 with all having locations measured by GPS and at least one Argos type (Table  1 ). All data collected after 2008 were reprocessed by CLS to obtain the three Argos data types (4 species; Table  1 ). We used an automated pre-filtering step to identify outlier observations to be ignored by the state-space model. This pre-filtering used the argosfilter R package  [21]  to identify locations implying travel rates > 3 ms -1 for all pinnipeds and sea turtles and travel rates > 17 ms -1 for northern gannets.
Empirical validation We examined the accuracy of model-predicted locations, assuming GPS data represent truth. Although GPS data have higher spatial accuracy and precision, and typically have higher sampling rates than Argos data, they are nonetheless discrete measurements of a continuous-time process. As a consequence, they are also likely to misrepresent animals' true movement paths but to a far smaller extent (10's of m;  [26] ) than Argos data. For all validations presented, we compared GPS locations to model-fitted locations (hereafter model-estimated locations), which are location states estimated at the times of the Argos-measured locations. By focusing on model-estimated locations and not predicted locations that occur at regular time intervals, we reduce the degree to which model accuracy is confounded with data sampling rates that are known to vary across species and Argos data types (see Discussion).
We compared model-estimated locations from fits to all three Argos data types, where available, with GPS data. In all cases, the times of GPS observations do not match the times of Argos observations or the corresponding model-estimated locations. To account for this mismatch, we initially considered three approaches for comparing between GPS and modelled locations. First, using a linear interpolation of GPS locations to model-estimated location times  [27]. Second, using the temporally closest GPS observation if any occurred within ± 10 min. Third, using the model to predict locations at the GPS observation times. In several cases, it was not feasible to predict model locations for each GPS observation time as the typically higher frequency of GPS observations resulted either in implausible artefacts in the model fits to the Argos data or in convergence failures of the optimiser used to fit the model.
Spatio-temporal resolution and spatial accuracy It is important to note that when comparing GPS locations with those from models fitted to Argos-measured locations, accuracy is interlinked with the temporal resolution (sampling rate) of Argos relative to GPS locations. As GPS resolution is typically greater than Argos, comparisons to determine spatial accuracy of estimated locations are confounded by this difference. No model fit to Argos-measured locations alone can resolve all the nuances of a movement path that are present in higher resolution GPS data. This discrepancy will be reflected in measures of spatial accuracy, unless GPS data are suitably sub-sampled or interpolated. We interpolated GPS locations to the times of the Argos-measured locations to which the state-space model was fitted.
Our reasoning was that interpolation of the generally higher resolution GPS data should be less corrupted by spatial error than a similar interpolation of the lower resolution and irregularly occurring model-estimated locations. Sub-sampling GPS locations by matching them with the temporally closest model-estimated location, commonly used elsewhere  [12, 30, 31] , resulted in lower RMSD or greater (apparent) accuracy than comparison with the linearly interpolated GPS locations. These lower RMSD values, however, were based on fewer (n < 10) temporally matched pairs of model-estimated and GPS locations for some species/individuals (Fig. S5 ); using a 20-min window. Although sample sizes could be increased by choosing a wider time window, the potential for biased comparisons would increase differently across species due to their different spatiotemporal scales of movement.
This case-dependency is likely tied to typical surface time intervals of diving species, and, for those species spending the majority of time in air, on the magnitude of their travel rates. On different issue of scale, many ecological analyses of animal tracking data consider remotely sensed or other environmental data at spatial resolutions (2 -10 km; e. g. ,  [32] ) approaching the state-space model accuracy limits found here. This highlights the need for researchers to consider the appropriate resolution of their environmental data given their specific questions and the limitations of their location estimates. Fitting a state-space model to Argos tracking data is not a panacea. Researchers should consider carrying location uncertainty estimates provided by statespace models through to subsequent ecological analyses.
Conclusions The state-space model developed and validated here can be used to obtain qualitycontrolled animal locations from Argos Least-Squares or Kalman filter data in near real-time, with median accuracy comparable to or marginally better than CLS' reprocessed Kalman Smoother data. Our model also accounts for apparent northsouth bias in Kalman filter-and Kalman smoother-derived error ellipses. The model's near real-time capability provides the best estimates of location, given the available data, that can be continually updated as new data arrive via the Argos system. This rapid, continual quality control of animal tracking data is necessary as near real-time monitoring and forecasting of ocean states increasingly incorporates oceanographic data from animal-borne sensors, and as the need for dynamic ocean management grows in our increasingly exploited and rapidly changing oceans.
Although the model was developed for fast, automated quality control processes, its simplicity and ease of use also make it suitable for manual use by researchers wishing to conduct quality control of historical or otherwise less immediate Argos data. Table  1  Number of individual data sets by species and data type. Argos data types are: Least-Squares (LS); Kalman filter (KF); Kalman smoother (KS). Mean track durations were calculated from the data after removing periods of prolonged data gaps. Tag programming details were not available for all deployments, so Argos and GPS sampling rates were calculated from the unfiltered data.
Acknowledgements We thank  M Weise  and  B Woodward  for motivating the validation study,  H Lourie  for assistance with CLS reprocessing, and  M Holland  and  K Wilson  for facilitating data access. IDJ supported by  Macquarie University 's co-Funded Fellowship Program and by external partners:  Office of Naval Research  grant  N00014-18-1-2405 ; the  Integrated Marine Observing System -Animal Tracking Facility ; the  Ocean Tracking Network ;  Taronga Conservation Society; Birds Canada ; and  Innovasea/Vemco. TAP supported by  CSIRO Oceans & Atmosphere  internal research funding scheme. CG thanks the  Institut Polaire Français Paul Emile Victor  (IPEV programs  109 ,  H. Weimerskirch  and  1201 ,  C. Gilbert ) and  Terres Australes et Antarctiques Françaises (TAAF)  for logistical and field support.
WJG and SCV thank  Greg & Lisa Morgan  for field assistance and were funded by  NERC   New Investigators Grant  ( NE/G001014/1 ), the  Peninsula Research Institute for Marine Renewable Energy  and  EU   INTERREG  Project  CHARM III. DPC  and  PWR thank National Oceanographic Partnership Program , the  Office of Naval Research, the Moore, Packard , and  Sloan Foundations , and  California Sea Grant Program. SSK supported by a  National Science Foundation   Office of Polar Projects research grant. XH and SW supported by the  Australian Government  under the  Caring for Country Initiative , the  Anindilyakwa Land Council , the  Northern Territory Government ,  Charles Darwin University , and the  ANZ Trustees Foundation -Holsworth Wildlife Research Endowment.
The deep learning model was trained and evaluated on a dataset composed of roads in the Alps extracted from IGN (the French national mapping agency) maps at 1:250,000 (output) and 1:25,000 (input) scale. The results are encouraging as the output image looks like a generalised version of the roads and the accuracy of pixel segmentation is around 65%. The model learns how to smooth the output roads, and that it needs to displace and enlarge symbols but does not always correctly achieve these operations. This article shows the ability of deep learning to understand and manage the geographic information for generalisation, but also highlights challenges to come.
g. , buildings aligned along a road); however, as in many other fields where machine learning is used, deep learning approaches might be able to overcome these problems  [8]. The first attempts to generalise buildings with a U-Net (a convolutional neural network for image segmentation)  [9]  showed very promising results  [10]. To go further than this seminal research, we want to test these new techniques in a classical but challenging use case of map generalisation: mountain road generalisation. Mountain road generalisation is more challenging than building simplification, because displacement, distortion, and sometimes schematisation operations are required in addition to simplification. Consequently, we decided explore how this knowledge could be learned by deep learning approaches. This alternative paradigm introduces novel research challenges, such as the creation of an adapted image training set from vector datasets, and mixes issues from computer science and spatial information science.
Research about the level of analysis, i. e. , defining what the generalised road exactly is (an object of the source database, a small part of the object, a group of objects) is also important in this generalisation task. For instance, ref. [20]  try to determine the appropriate stroke  [11]  to be the input of simplification algorithms, and such a process might be relevant to define the unit to learn from (i. e. , an entire road object, a part of an object, or groups of road objects). As mentioned in the introduction, as the goal of researchers in map generalisation was to automate the work of human cartographers, machine learning was explored to capture this specific knowledge  [4]. Machine learning can be used to learn the rules used by cartographers to make their generalisation decisions  [6] , or to learn the structural (geometric and semantic) transformation between the initial and the generalised map  [5].
Moreover, there is a real possibility of creating very large training datasets by using the existing generalised maps. Among the seminal research projects using deep learning for map generalisation, ref. [10]  proposes a segmentation network to generalise buildings at large scales. Image segmentation is the classification of each pixel in the image, e. g. , segmenting the pixels of the roads in aerial imagery. Feng et al. compare a U-Net, a residual U-net, and a generative adversarial network (GAN) architecture, the residual U-net architecture being the most accurate in their experiment. The work on transferring map styles at different scales can also be related to map generalisation. The GAN proposed by  [23]  to generate Google Maps-like maps from OpenStreetMap data is able to remove unimportant roads when the target is a small scale map.
Use Case We used two different datasets of roads covering a small part (2155 km 2 ) of the Alps in France (Figure  1 ). The first one is an extract of the database used to make the topographic maps at the 1:25,000 scale at IGN, the French national mapping agency (NMA), and it contains 30,455 roads for a total length of 5,253,248 km. The second dataset is an extract of the database used to make the 1:250,000 scale map at IGN, it contains 853 roads for a total length of 1,775,205 km. This second dataset represents only important roads and their shape is simplified, smoothed, and often schematised for a better visualisation of the sinuous bends. The goal of this use case was to generate the 1:250,000 scale map from the 1:25,000 scale map. These datasets are constructed and managed independently, therefore there is no direct link between corresponding roads and the road attributes are not similar.
The second dataset was generated from the first one by a generalisation process that combines automated algorithms and manual corrections by cartographers twenty years ago  [19] , and then manually updated by cartographers. The dataset does not contain only sinuous mountain roads, it contains all the roads of the area, so there were also some urban roads in towns and one highway across the map. We split our dataset in two subgroups according to sinuosity (Figure  2 ), in order to test different training methods, i. e. , to try a training with all types of roads and a training only with sinuous roads, the most difficult to generalise. We chose a simple measure of road sinuosity  [7] : for each road polyline, we divided the base length (distance between the first and the last vertices) by the total length of the road.
This measure is given as a percentage in Figure  2 : the value is close to 100 when the road is straight and close to 0 when it is very sinuous. As a first step, we do not want, in this paper, to learn both road simplification and selection. In order to identify the roads from the source dataset that were kept in the output dataset, a data matching pre-process between both datasets was necessary. There were very large gaps between the corresponding roads from both datasets (often several hundred meters), so the classical road matching method we used  [27]  was unsuccessful. As a consequence, we finally decided to manually match the roads that are present in both datasets. As a manual matching would not be a reasonable solution for larger areas, the use of more flexible multi-criteria data matching methods should be investigated  [28].
Creating an Adapted Learning Dataset The vector to raster transformation can generate some errors  [30] , as rasterization always causes a loss of information compared to vector data. Errors can occur on the area, the perimeter, the shape, the structure, the position and the attributes of the rasterized data, but only errors in shape, structure and position are relevant for us. Increasing the resolution of the raster image can minimize these errors, but a larger image has a consequence on the computation time of CNNs, and does not always improve final results. For instance, the classification results from  [31]  are better with a 256 × 256 size than with a 512 × 512 size (with a constant scale). Convolutional neural networks are designed to optimally work with squared images with a size which is a power of two (e.
g. , 128 × 128, 256 × 256, etc. ). Consequently we choose 256 × 256 images, which is empirically a good compromise between the resolution of the images and computation time. We can also note that the size of symbols is important to define during rasterization. Symbol size is a classical problem in raster generalisation  [32]. Consequently, we decided to generate our 256 × 256 images with red roads on a white background. Using a coloured image, even if only one colour is used for now, allows a further addition of information (e. g. , buildings or rivers) without transforming the network architecture for accepting coloured images as input. We can include some additional information by assigning width symbols relative to road importance. We matched the attribute values defining road importance in both datasets to a width in pixels, and this matching is summarised in Table  1.
Width in Pixel Symbolization at 1:25,000 Attribute Values Translation at 1:250,000 1 5 Irrelevant, forbidden, local narrow roads, lane 2 4 Regional roads and narrow regional roads 3 3 Regional roads with bike path 4 Ø Major roads 5 2 Highway Then, we propose two approaches (illustrated in Figure  3 ) to create the images by tiling the dataset: • to slide a fixed-size window over the study area. This method creates images with a fixed scale of the underlying data, but can generate irrelevant (only a very small portion of a road) or empty tiles. • to overcome the problem of irrelevant tiles, we can use the road objects as a basis to guide tile creation. In this method, the whole geometry of a road is included in the tile, and as roads objects have varying lengths, and geometry extents, this process makes tiles that have different scales.
It should noted that, in the initial database, a new road is collected at each intersection and at each change of attribute (e. g. , change in the number of lanes or in the width of the roadway). This is why roads do not always terminate at intersections. The first method needs two parameters: a window size (or resolution) and an overlapping rate that defines the window displacement over the dataset. The second method does not need any parameter. The problems raised by both methods are similar to classical problems in data partitioning for map generalisation  [33] , where there is no perfect solution. There are two important issues to consider here: first the scale of the images should be fixed to help the convolutional network to learn that scale changes from the input image to the output image.
Then, there is the classical issue of the best level of analysis for generalisation  [34] , which is particularly significant when dealing with mountain roads  [16]. For instance, working on the level of dataset road objects is arbitrary, as these objects can be very long or very short, and not always homogeneous in terms of shape. Working on road strokes  [11]  might be a better choice, and/or working on parts of roads that are homogeneous in terms of sinuosity or symbol coalescence  [16]. As none of the proposed methods is ideal, we decided to post-process the tiles generated. The first post-process consists in cleaning the tiles that lie at the limit of the use case area, when they do not contain roads from both datasets due to selection edge effects. Regarding the first method with fixed-size tiles, we removed the irrelevant tiles that contain no road or only a very small portion of roads.
As result of these processes, we obtain around 560 images with the first method and 690 with the second one. To increase the number of images for training, three common methods exist  [35]  that generate modified images from existing ones: crop, mirror, and rotation (see e. g. , Figure  5 ). In our case, using the crop augmentation (cut a part of the image) does not seem to be adapted: on object-based tiles, we lose a part of the context (around the object), and in case of fixed-scale tiles, images of the new dataset do not have a fixed scale. The rotation (90 • , 180 • , or 270 • ), and the mirroring (according to a horizontal or a vertical axis) augmentation methods have similar effects on the image and augment the training set with realistic new road images. It is also possible to augment the number of images by changing the tiling parameters.
In the object-based approach, considering smaller objects than initial roads, especially in the case of very long roads, would augment the size of the training dataset. In the fixed-sized method, choosing a smaller window or using a bigger overlap would augment the size of the training dataset. These alternative augmentations were tested and presented in Section 4 to assess their usefulness. But data augmentation does not solve problems of representativeness and homogeneity because it will augment much more the number of non sinuous roads if they outnumber the sinuous roads in the initial dataset, which is the case in our use case.
Implementation We conducted our experiment using Python 3 with the Keras library  [39]. The code was implemented in the Google Colaboratory platform with the available graphic processing unit (GPU) for standard licences. Our code and image data will be made available in future weeks as part of the DeepMapGen open platform https://github. com/umrlastig/DeepMapGen. The code to generate the images from the vector dataset was part of the CartAGen open source generalisation platform https://github. com/IGNF/CartAGen, developed at IGN  [40]. In deep learning, the data is separated in three sets. The training set is the set of images used to train the model. At each iteration (or epoch), additional data, not from the training set, called the test set, are used to assess and adjust the model. Finally, the evaluation set is not used in the training process and serves in the evaluation of the trained model on new images.
We chose to evaluate our model on 4% of our dataset. This is unusually low but our initial dataset was small (see Section 3. 1) and we wanted to keep it large enough for an effective training; however, this ratio allows for the representation of most situations. We randomly separated our dataset into a training and an evaluation set, and we added the constraint that evaluation tiles should not be spatial neighbours. Then, the training data was further randomly separated into the real training set and the test set. Consequently, 92% of our images were used for training. In this experimentation, we had to choose a loss function for the model. The loss function indicates the error of the model and the network tries to minimize this function. U-Net typically are implemented with a binary cross entropy loss (BCE).