dataesr/scientific-paragraphs-categorization huggingface dataset:
The current concept is static in a way that it builds on pre-defined contour datasets. These pre-defined contour datasets are, however, globally defined although it would be a much nicer approach to define contour spacing based on the local relief and map scale (zoom level) as suggested in the literature (e. g. ,  Hake et al. , 2001). This would provide optimum results for highrelief areas (fewer contours) and lowlands plains where the contour spacing can be reduced (cf. Figure  1a-d ). While the implementation of the topography concept is straightforward in terms of incorporation of data using the CARTO platform, its realization in terms of preprocessing is challenging due to an abundance of data and interpolation artefacts in the archived raster datasets. Unfortunately, the MOLA dataset cannot be processed from raw data levels with the same quality as it was manually corrected and visually improved by the MOLA science team without being documented.
Readouts of the original track data and application of an improved interpolation is possible but the results will contain all erroneous tracks. Still, the manually improved raster dataset has flaws as seen by numerous jagged lines in NS direction and generally considerable jumps in the level of detail when comparing along track and across track space. By vectorizing and applying generalization and smoothing of contour lines it is easy to create topological errors that are hard to eliminate once they are introduced. The amount of required smoothing to eliminate these errors causes a deterioration of resolution and will still not provide satisfactory results. Subsequent hypsometric tinting has been carried out using 50x50 pixel average sampling window over the global MOC color mosaic. Low-lightness values were assigned to topographically low area, and brighter areas were assigned to higher elevations in order to create a similar effect as commonly applied for physiographic maps of the Earth.
fossa, fossae), volcanic structures (lat. tholus, tholi; mons, montes and patera. paterae) due to their areal dominance, and finally, features with a larger areal extent such as plains (lat. planitia, planitiae as well as planum, plana and terra, terrae features). This selection will transport as much details as needed, but it is not too overloaded for a global map. The revised dataset provided by USGS does not only provide the areal extent of each feature but also a rotation angle allowing to better place and distribute features on the map. At this moment, however, more advanced placement options, such as alignment to curved objects and paths are lacking which, however, will be hard to implement given the fact that dynamic zoom levels require updates along object paths whenever the user changes the zoom level.
Summary and Outlook Using CARTO as our geospatial datasets storage and visualization platform and CartoCSS as styling language, we have been able to implement key elements of our cartographic concept and to produce the first vector-based basemap for Mars. At this stage we have not been able to implement all our conceptual elements on a mature level as the vectorization of global raster data poses a number of challenges on itself. This is mainly related to relatively higher levels of noise, in particular in topographic and spectral data. However, once an appropriate filtering has been applied and deterioration of the original data resolution can be accepted the conversion and implementation is possible as tested for a number of regionally confined cases. Our aim to provide a vector-rendered basemap could accomplice in the first implementation by using CARTO.
However, since then, even today's state-of-art planetary web mapping applications still use traditional global mosaicked raster data as basemap. Zooming in and out such basemaps allows a viewer to see planetary surfaces at different scales and levels of detail; but it does not provide additional contextual information (derived or interpreted), and makes it difficult to overlay other datasets and create readable visualizations. Geosciences ODE, REACT-QuickMap, iMars), but later developed and used for scientific research purpose (PlanetServer, MARSWEB), for landing site selection and science operations (JMARS), and more recently for outreach and education (NASAs Solar System Trek Suite, Google Maps/Earth, JMARS). In collaboration with NASA researchers at Arizona State University and USGS's ASC, Google contributed to make planetary maps popular on the web with their Google Mars/Moon interfaces in 2011.
In the context of underwater robotics, the visual degradation induced by the medium properties make difficult the exclusive use of cameras for localization purpose. Hence, many underwater localization methods are based on expensive navigation sensors associated with acoustic positioning. On the other hand, pure visual localization methods have shown great potential in underwater localization but the challenging conditions, such as the presence of turbidity and dynamism, remain complex to tackle. In this paper we propose a new visual odometry method designed to be robust to these visual perturbations. The proposed algorithm has been assessed on both simulated and real underwater datasets and outperforms state-of-the-art terrestrial visual SLAM methods under many of the most challenging conditions.
The back-end relies on Bundle Adjustment  [16]  to ensure minimal drift and consistency in the reconstructed trajectory. The paper contributions are the following: • A thorough evaluation of visual features tracking methods on underwater images. • The development of UW-VO: a monocular VO method robust to turbidity and to short occlusions caused by the environment dynamism (animals, algae. ). • An evaluation of state-of-the-art open-source monocular VO and VSLAM algorithms on underwater datasets and a comparison to UW-VO, highlighting its robustness to underwater visual degradation. • The release of a dataset consisting of video sequences taken on an underwater archaeological site  1. The paper is then organized as follows. First, in section 2, we review works related to our contributions. In section 3, we present an evaluation of features tracking methods on two underwater sets of images and we show that optical flow based tracking performs better than methods based on the matching of descriptors.
In section 4, our monocular visual localization system UW-VO is described. Finally, in section 5, we compare UW-VO to state-of-the-art open-source monocular VO and VSLAM on both a synthetic dataset and on a real one that we made publicly available for the community. We show that UW-VO competes with these methods in terms of accuracy and surpasses them in terms of robustness.
Before considering the coupling of such complementary low-cost sensors for localization, the first step is to be able to rely on an accurate VO method. Hence, contrarily to most of the approaches in underwater localization, we propose here a keyframe-based VO method, solely based on visual data coming from a high frame monocular camera. Inspired by aerial-terrestrial SLAM, we choose to rely on bundle adjustment to optimize the estimated trajectories, thus avoiding the integration of linearization errors of filtered approaches. Furthermore, our method do not use any environment specific assumption and can hence run in any kind of environment (planar or not). We show that our method outperforms state-of-the-art visual SLAM algorithms on underwater datasets.
Features Tracking Methods Evaluation As discussed in the introduction, underwater images are mainly degraded by turbidity. Moreover, underwater scenes do not provide many discriminant features and often show repetitive patterns like coral branches, holes made by animals in the sand or simply algae or sand ripples in shallow waters. In order to develop a VO system robust to these visual degradation, we have evaluated the performance of different combinations of detectors and descriptors along with the optical flow based Kanade-Lucas-Tomasi (KLT) method  [49]  on two sets of underwater images. Underwater sets of images. Two different sets of images are used here (Fig. 2 ). The first one is the TURBID dataset  [24] , which consists of series of static pictures of a printed seabed taken in a pool.
As we will show that the KLT performs best, we also evaluate all the previous descriptors in conjunction with this Harris corner detector for a fair comparison. Evaluation protocol. The TURBID dataset is used to evaluate robustness to turbidity. The employed evaluation protocol on this set is the following: • we divide each image into 500 cells and try to extract one feature per cell • we track the features extracted in one image into the following one (i. e. the image shot right after an adding of milk) • before each tracking, the second image is virtually translated of 10 pixels to avoid initializing the KLT at the right spot Note that the KLT method uses a local window, limiting the search space around the previous feature. As the tracking is here performed between images slightly translated one from the other, the search space for matching descriptors is limited to a 40x40 pixels window around the previous features.
20GHz -8 Gb RAM. To the best of our knowledge there is no underwater method able to estimate localization from monocular images available open-source. Furthermore, no publicly available datasets were released with these methods, so we cannot compare with them. Hence, UW-VO has been evaluated along with ORB-SLAM 2  , LSD-SLAM  3  and SVO  4  on different datasets which are all available online, allowing future methods to compare to our results. All algorithms are evaluated on real underwater datasets. UW-VO and ORB-SLAM are also evaluated on a simulated dataset, whose frame rate (10 Hz) is too low for SVO and LSD-SLAM to work. Indeed, SVO and LSD-SLAM are direct methods which require very high overlap between two successive images in order to work. Note that ORB-SLAM and SVO have been fine-tuned in order to work properly.
For ORB-SLAM, the features detection threshold was set at the lowest possible value and the number of points was set to 2000. For SVO, the features detection threshold was also set at the lowest possible value and the number of tracked features required for initialization was lowered to 50. For each method, every results presented are the averaged results over five runs. A simulated dataset created from real underwater pictures has been made available to the community by Duarte et al. [62]. Four monocular videos of a triangle-shaped trajectory are provided with four different levels of noise in order to synthetically degrade the images with turbidity-like noise (Fig  6 ). The images resolution of these videos is 320x240 pixels. In each sequence, the triangle-shaped trajectory is performed twice and it starts and ends at the same place.
Results on a Simulated Underwater Dataset Table  1  presents the final drift at the end of the trajectory for each method. On the first three sequences, ORB-SLAM is able to close the loops and therefore has the lowest drift values, as the detection of the loop closures allows to reduce the drift accumulated in-between. On the same sequences, V. O. ORB-SLAM has the highest level of drifts. Note that ORB-SLAM and its V. O. alternative fail half the time on the third level of noise sequence and have been run many times before getting five good trajectories. It is worth noting that the localization drift increases significantly for V. O. ORB-SLAM when the turbidity level gets higher. This is mostly due to the increased inaccuracy in its tracking of ORB features. On the last sequence, the turbidity level is such that ORB descriptors get too ambiguous and leads to failure in ORB-SLAM tracking.
We publicly released the underwater datasets used in this paper along with the camera calibration parameters and the trajectories computed with Colmap to allow future methods to compare to our results. The good results obtained on these sequences highlight the effectiveness of the developed method for localizing ROVs navigating in deep underwater archaeological sites. The computed localization could be used by the pilot as a driving assistance and could further serves as a feedback information for navigation if scale is recovered. Future work will study the implementation of this localization algorithm on an embedded computing unit in order to fulfill these tasks. The development of a monocular visual odometer was a first step towards a robust underwater localization method from low-cost sensors.
Open Broadcast Media Audio from TV (OpenBMAT) is an open, annotated dataset for the task of music detection that contains over 27 hours of TV broadcast audio from 4 countries distributed over 1647 one-minute long excerpts. It is designed to encompass several essential features for any music detection dataset and is the first one to include annotations about the loudness of music in relation to other simultaneous non-music sounds. OpenBMAT has been cross-annotated by 3 annotators obtaining high interannotator agreement percentages, which allows us to validate the annotation methodology and ensure the annotations reliability. In this work, we first review the current publicly available music detection datasets and state OpenBMAT's contributions. After that, we detail its building process: the selection of the audio and the annotation methodology.
Introduction Music detection refers to the task of finding music segments in an audio file. 1 Thus, the minimum requirement for a dataset to be suitable for this task is to include annotations about the presence of music. However, we find the following two features to be essential to any music detection dataset that aims to provide a certain level of generalization: first, music should appear both isolated and mixed with other type of non-music sounds, because, otherwise, the dataset may not be representative of many real-life scenarios such as broadcast audio; and second, a significant number of the audio files included in the dataset should be multi-class, i. e. , contain class changes so that it allows the evaluation of an algorithm's precision in detecting them. The two main applications of music detection algorithms are (1) the automatic indexing and retrieving of auditory information based on its audio content, and (2) the monitoring of music for copyright management (Zhu et al.
In this paper, we use the concept of loudness as it was defined by Moore  (2012): "that attribute of auditory sensation in terms of which sounds can be ordered on a scale extending from quiet to loud" (p. 133). Currently, there is no dataset with annotations about the relative loudness of music, and the only publicly available dataset including the aforementioned two features is the dataset published by Seyerlehner et al. (2007). 3 Unfortunately, despite containing both isolated music and music mixed with other type of sounds, its annotations do not reflect this information and specify only the presence of music. In the case of the dataset published by Scheirer and Slaney (1997),  4  which is the first open dataset that included annotations about the presence of music, the type of sounds that appear mixed with music are restricted to speech, and this is reflected in the chosen taxonomy: Music, speech, simultaneous music and speech and other.
Other publicly available datasets that include music presence annotations are MUSAN 5 (Snyder et al. , 2015) and GTZAN 6 datasets, but none of them include music mixed with other type of sounds and both of them consist of single-class instances, i. e. , audio files annotated as a single segment of a single class. For more information about these datasets see Section 2. In this paper, we present OpenBMAT, a dataset containing 27. 4 hours of audio sampled from 8 different TV program types that have been broadcast in the most popular TV channels of 4 different countries: France, Germany, Spain and the United Kingdom. It consists of 1647 one-minute multi-class audio files that include music and non-music sounds both mixed and isolated. OpenBMAT has been cross-annotated by 3 annotators using a taxonomy that mixes the presence of music with its loudness with respect to other type of simultaneous non-music sounds.
This taxonomy contains 6 classes (defined in Section 3. 2. 1): Music, Foreground Music, Similar, Background Music, Low  Background Music and No Music. In Table  1 , we show that, despite not being the longest dataset, OpenBMAT is the only one that brings together all the appropriate characteristics for the task of music detection and also for the estimation of the music's relative loudness. In Section 2, we describe the currently publicly available datasets that are suitable for the task of music detection. Then, in Section 3, we provide the details about the sampling process and the annotation methodology followed to create OpenBMAT. Section 4 is devoted to the analysis of the dataset's content as well as the validation of the annotations reliability. In Section 5, we perform a simple experiment where we evaluate a state-of-the-art algorithm using OpenBMAT to analyze the benefits of including agreement information in the annotations and the challenges that OpenBMAT poses for current methods.
In Section 6, we specify the exact content and format of the annotations and the platform where it is available. Finally, Section 7 provides our main conclusions. Scheirer and Slaney (1997) published the first open dataset that included annotations about the presence of music. The taxonomy, however, was designed for the task of discriminating music and speech and is composed of four classes: Music, Speech, Simultaneous Music and Speech and Other. The dataset contains 245 manually annotated 15-seconds 16-bit monophonic wav audio files at a sampling rate of 22050 Hz digitally sampled from an FM tuner from the San Francisco Bay area. All audio files are single-class and they are separated in a training part of 184 files and a testing part of 61 files. The training part is divided in 60 files of isolated music, 60 files of simultaneous music and speech, 60 files of isolated speech and four files with other type of sounds.
The testing part is divided in 20 files of isolated music with vocals, 21 files of isolated music without vocals and 20 files of isolated speech. Seyerlehner et al. (2007) presented the first and only open dataset specific to music detection until now, i. e. , with a taxonomy including only the classes Music and No Music. It has a total duration of nine hours unevenly distributed over 13 manually annotated mp3 audio files of duration between 6 and 90 minutes. The audio was extracted from TV programs of the Austrian National Broadcasting Corporation (ORF). The music content of each file ranges from 1. 5% to 80%.
Related Work Tzanetakis 6 generated another dataset for the task of music and speech discrimination named GTZAN music/speech collection. It consists of 128 manually annotated 30-seconds 16-bit monophonic wav audio files at a sampling rate of 22050 Hz. These audio files are divided equally between the classes Music and Speech. Some of the present music genres are classical music, folk music, jazz, pop, rock or electronic music. Regarding the speech part, most of the content is in English, but other languages such as German, Chinese, Greek or Serbian appear too. MUSAN (Snyder et al. , 2015) is a dataset that contains: 60 hours of speech coming from Librivox 7 and archived US government files, which are unevenly distributed between 12 different languages; 42 hours of varied music styles such as Baroque, Classical, Romantic, Country, Hip-Hop, Jazz, etc.
Raw corpus OpenBMAT contains 27. 4 hours of audio divided in 1647 one-minute audio files. Each of these audio files comes from a different recording that we have sampled from BMAT's 8 private database, which temporarily stores recordings from over 2000 TV channels that this company monitors as part of its business. We consider that having many short audio files allows the dataset to include a greater variety of contexts. Nevertheless, these audio files are long enough to be multi-class. Some of the recordings in the database include tags about their content. Using these tags, we have forced the sampled audio files to cover a set of varied program types to ensure that the dataset is representative of several different broadcast contexts. The selected program types are: Children, Documentary, Entertainment, Music, News, Series and Films, Sports and Talk.
Unfortunately, only the recordings from certain countries include these tags. This has constrained the dataset to audio broadcast by TV channels from France, Germany, Spain and the United Kingdom. We set a limit of 60 audio files for each country and program type, but for several combinations there were not enough audio files to reach that value. Figure  1  shows the distribution of audio files by program type for each country. All the audio files in the dataset are 16-bit monophonic WAV files at a sampling rate of 22050 Hz and have been extracted from audio broadcast during 2017. Their loudness ranges roughly from -51 to -7 Loudness Units relative to Full Scale (LUFS). LUFS is a logarithmic measure of the perceived loudness in an audio file used in media broadcasting that was introduced in EBU R128.
For instance, when dealing with a large number of audio files daily, producing a high number of segments for each of them can lead to problems with the database. We have set this limit to 1 second in an attempt to consider both points of view. To ensure that the annotators follow this rule, we have defined a set of annotation steps: Once two groups merge they take the class that is majority. (b) and it is surrounded by No Music, annotate it as the appropriate class. (c) and it is surrounded by still not annotated audio, leave the group with no annotation. 5. Annotate any part of the audio that is not yet annotated at this point as Music.
Annotation reliability OpenBMAT has been manually cross-annotated by three different annotators: two males and one female of ages comprised between 20 and 40 years and experience working with sound and/or music. Cross-annotating allows us to assess the reliability of the annotations produced (see Section 4. 1). All three annotators have been trained to use the annotation tool described in Section 3. 2. 4 and have learned the annotation steps before starting the annotation of the dataset. Throughout the whole process they have been allowed to ask questions and we have regularly provided feedback. In average, they spent approximately 130 hours annotating the dataset. This means that the annotation rate was around 4. 75 hours of annotation per hour of audio.
Annotation tool The tool that we have used for the annotation of the dataset is BAT, an open-source, web-based tool for the manual annotation of events in audio files. As mentioned in Section 3. 2. 1, the selected taxonomy implies that classes cannot overlap and allows all audio to be annotated as one of its classes. BAT provides two functionalities that make annotation faster and easier in this situation: (1) it can be configured to avoid any overlap between regions; and (2) it includes shortcuts to expand a region to the limits of adjacent or the limits of the audio file being annotated. Additionally, the tool is exclusively oriented to the annotation of audio events, which allows for a simple and clear user interface, and it is connected to the database where all annotations are stored automatically.