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865 results for “Geographic data”
FIGURE 9 in Uncertainties and risks in delimiting species of Cambeva (Siluriformes: Trichomycteridae) with single-locus methods and geographically restricted data
FIGURE 9 | ndividuals representing populations of Cambeva sp. 1 from distinct river drainages. (A) Maquiné River basin, UFRGS 22211, 58.6 mm SL, (B) Mampituba River basin, MCP 23623, 55.7 mm SL; and (C, D) Araranguá River basin, UFRGS 22962, 45.2 and 59.8 mm SL, respectively.
FIGURE 6 in Uncertainties and risks in delimiting species of Cambeva (Siluriformes: Trichomycteridae) with single-locus methods and geographically restricted data
FIGURE 6 | Variation on the coloration in Cambeva cubataonis, showing a mottled color pattern composed by blotches variable in shape and forming marked saddles dorsally, (C–E) specimens with large dark areas merged giving an overall dark color pattern. Specimens from tributaries of Guaratuba Bay (A) UFRGS 24549, 53.1 mm SL; (B) UFRGS 24556, 60.3 mm SL; and from tributaries to Babitonga Bay (C–E) UFRGS 24552, 77.2 mm SL, 85.7 mm SL, and 61.6 mm SL, respectively.
FIGURE 5 in Uncertainties and risks in delimiting species of Cambeva (Siluriformes: Trichomycteridae) with single-locus methods and geographically restricted data
FIGURE 5 | Variation on the coloration in vivo and ontogenetic series of Cambeva botuvera from (A, C–F) Itajaí-Açu River, UFRGS 23182, 47.3 mm SL, 64.8 mm SL, 68.1 mm SL, 81.1 mm SL, 95.6 mm SL, respectively; and (B) from Itajaí-Mirim River, UFRGS 24558, 67.1 mm SL, showing the color pattern with two layers in the skin composed of large and small round black blotches in the inner and outer layer, respectively.
FIGURE 4 in Uncertainties and risks in delimiting species of Cambeva (Siluriformes: Trichomycteridae) with single-locus methods and geographically restricted data
FIGURE 4 | Polymorphic color pattern within of Cambeva barbosae ranging from a mottled color pattern in individuals from (A) Santa Catarina Island, UFRGS 23183, 80.1 mm SL, and (B) Biguaçu River basin, UFRGS 22907, 61.3 mm SL; to a blotched color pattern with dark marks varying in shape and size from Biguaçu River basin, (C, D) UFRGS 22932, 58.7mm SL and 51.0 mm SL, respectively, and (E) UFRGS 20937, 30.0 mm SL.
FIGURE 3 in Uncertainties and risks in delimiting species of Cambeva (Siluriformes: Trichomycteridae) with single-locus methods and geographically restricted data
FIGURE 3 | A. Cambeva balios from Mampituba River basin, UFRGS 16295, 63.8 mm SL. B. Cambeva davisi from Ribeira de Iguape River basin, MZUEL 17202, 72.8 mm SL, fixed in alcohol. C. Cambeva iheringi from Piagui River, coastal drainage of São Paulo State, MNRJ 24008, 75.6 mm SL.
FIGURE 1 in Uncertainties and risks in delimiting species of Cambeva (Siluriformes: Trichomycteridae) with single-locus methods and geographically restricted data
FIGURE 1 | Geographical distribution of Cambeva species in the coastal drainages of Southern and Southeastern Brazil (crossed circles indicate their past known distribution). Some dots represent more than one locality.
FIGURE 2 in Uncertainties and risks in delimiting species of Cambeva (Siluriformes: Trichomycteridae) with single-locus methods and geographically restricted data
FIGURE 2 | Schematic drawing of morphometric measurements applied to Cambeva species: (1) standard length, (2) head length, (3) head width, (4) predorsal length, (5) prepelvic length, (6) pre-anal length, (7) scapular girdle width, (8) trunk length, (9) pectoral-fin length, (10) pelvic-fin length, (11) distance between pelvic-fin base and anus, (12) caudal peduncle length, (13) caudal peduncle depth, (14) body depth, (15) length of dorsal-fin base, (16) length of anal-fin base, (17) snout length, (18) interorbital distance, and (19) eye diameter. Illustration made by Alexandre Ribeiro.
Text-fig. 8. Palaeogene terrestrial fossiliferous deposits of Africa. Note the position of the Tunisian Oligocene deposits, and their obvious interest in terms of geographic position and age (data for the Palaeogene of North Africa are from Tabuce et al. 2000, 2001, 2011, Delmer et al. 2006, Coster et al. 2012, Yans et al. 2014, Solé et al. 2016). Note that Nakwai (Kenya) is no longer considered to be Oligocene. in Arsinoitherium (Embrithopoda) And Other Large Mammals And Plants From The Oligocene Of Tunisia
Text-fig. 8. Palaeogene terrestrial fossiliferous deposits of Africa. Note the position of the Tunisian Oligocene deposits, and their obvious interest in terms of geographic position and age (data for the Palaeogene of North Africa are from Tabuce et al. 2000, 2001, 2011, Delmer et al. 2006, Coster et al. 2012, Yans et al. 2014, Solé et al. 2016). Note that Nakwai (Kenya) is no longer considered to be Oligocene.
Data for: Historical and contemporary processes drive global phylogenetic structure across geographical scales: Insights from bat communities
<p><strong>Aim</strong>: Patterns of evolutionary relatedness among co-occurring species are driven by scale-dependent contemporary and historical processes. Yet, we still lack a detailed understanding of how these drivers impact the phylogenetic structure of biological communities. Here, we focused on bats – one of the most speciose and vagile groups of mammals – and test the predictions of three general biogeographical hypotheses that are particularly relevant to understanding how paleoclimatic stability, local diversification rates, and geographical scales shaped their present-day phylogenetic community structure.</p> <p><strong>Location</strong>: Worldwide, across restrictive geographical extents: global, east-west hemispheres, biogeographical realms, tectonic plates, biomes, and ecoregions.</p> <p><strong>Time period</strong>: Last Glacial Maximum (~22,000 years ago) to the present.</p> <p><strong>Major taxa studied</strong>: Bats (Chiroptera)</p> <p><strong>Methods</strong>: We estimated bat phylogenetic community structure across restrictive geographical extents and modelled it as a function of paleoclimatic stability, and in situ net diversification rates.</p> <p><strong>Results</strong>: Limiting geographical extents from larger to smaller scales strongly changed the phylogenetic structure of bat communities. The magnitude of these effects is less noticeable in the western hemisphere, where frequent among-realm biota interchange could have been maintained through bats' adaptive traits. Highly phylogenetically related bat communities are generally more common in regions that changed less in climate since the last glacial maximum, supporting the expectation that stable climates allow for increased phylogenetic clustering. Finally, increased in situ net diversification rates are associated with greater phylogenetic clustering in bat communities.</p> <p><strong>Main conclusions</strong>: We show that the worldwide phylogenetic structure of bat assemblages varies as a function of geographical extents, dispersal barriers, paleoclimatic stability and in situ diversification. The integrative framework used in our study, which can be applied to other taxonomic groups, has proven useful to not only explain the evolutionary dynamics of community assembly but could also help tackle questions related to scale dependence in community ecology and biogeography.</p>
Data from: Geographical variation of Artemisia leaf morphology along a large environmental gradient in China
<p>We collected 60 species of <em>Artemisia</em> from 81 sampling sites in 15 provinces in China from the end of July to August 2018. At each site, we recorded latitude and longitude. Then we identified all<em> Artemisia</em> species and randomly sampled 4 plants for each species. A total of 1,041 plants were sampled. In the laboratory, 10 leaves in the middle of each plant were selected and detached. Because light might have effect on leaf morphology, we only used sun exposed leaves. The leaves were soaked in distilled water for 24 h to fully restore their shapes. After blotted-dried, leaves were place as petiole to the left and tip to the right, and an Epson V370 (Seiko Epson Corp., Nagano, Japan) was used to scan the leaves at a resolution of 600 DPI. </p> <p>We set 40 landmarks along the blade edge on each leaf. The first point was set at the petiole and the 21st point at the tip, and these two points were defined as landmarks. The remaining points were evenly distributed at the blade edge and defined as semilandmarks. Landmarks were saved in tps format using Geomorph package.</p>
ben-ge/ESAWorldCover: BigEarthNet Extended with Geographical and Environmental Data/Land-use/land-cover Data
<p><strong>ben-ge/ESAWorldCover: BigEarthNet Extended with Geographical and Environmental Data/Land-use/land-cover Data</strong><br> </p> <p>M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023.</p> <p>ben-ge is a multimodal dataset for Earth observation (https://github.com/HSG-AIML/ben-ge) that serves as an extension to the BigEarthNet dataset. ben-ge complements the Sentinel-1/2 data contained in BigEarthNet by providing additional data modalities:</p> <p>* elevation data extracted from the Copernicus Digital Elevation Model GLO-30;<br> * land-use/land-cover data extracted from ESA Worldcover;<br> * climate zone information extracted from Beck et al. 2018;<br> * environmental data concurrent with the Sentinel-1/2 observations from the ERA-5 global reanalysis;<br> * a seasonal encoding.</p> <p>This archive contains the land-use/land-cover data of ben-ge, which were extracted from the ESA WorldCover service.</p> <p><br> <strong>Data</strong><br> Land-use/land-cover map tiles matching the Sentinel-1/2 patches were extracted from ESA WorldCover (<a href="https://esa-worldcover.org">https://esa-worldcover.org</a>). Relevant tiles were downloaded and reprojected into the coordinate frame of the corresponding Sentinel-1/2 patches. WorldCover data are available both as maps and as class fractions that are aggregated over each patch. </p> <p>Land-use/land-cover map data are provided in a separate geotiff file for each patch. The naming convention for these files uses the Sentinel-2 patch_id to which we append _esaworldcover.tif. Each file contains a single band with 8-bit integer values that map to land-use/land-cover definitions provided by the ESA WorldCover Product User Manual (<a href="https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf">https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf</a>) (page 15).</p> <p>The file ben-ge_esaworldcover.csv contains the fractions by which each of the different classes cover the corresponding patch. This product may be useful to generate single-label or multi-label targets for different classification setups.</p> <p>Relevant meta data for the ben-ge dataset are compiled in the file ben-ge_meta.csv. This file resides on the root level of this archive and contains the following data for each patch:<br> * patch_id: the Sentinel-2 patch id, which plays a central role for cross-referencing different data modalities for individual patches;<br> * patch_id_s1: the Sentinel-1 patch id for this specific patch;<br> * timestamp_s2: the timestamp for the Sentinel-2 observation;<br> * timestamp_s1: the timestamp for the Sentinel-1 observation;<br> * season_s2: the seasonal encoding (see below) for the time of the Sentinel-2 observation;<br> * season_s1: the seasonal encoding (see below) for the time of the Sentinel-1 observation;<br> * lon: longitude (WGS-84) of the center of the patch [degrees];<br> * lat: latitude (WGS-84) of the center of the patch [degrees];<br> * climatezone: integer value indicating the climate zone based on Beck et al. 2018 (see below for details).</p> <p><br> <strong>File and directory structure</strong><br> This archive contains the following directory and file structure:</p> <p>|<br> |--- README (this file)<br> |--- ben-ge_meta.csv (ben-ge meta data)<br> |--- ben-ge_esaworldcover.csv (patch-wise ben-ge land-use/land-cover data)<br> |--- esaworldcover/ (land-use/land-cover data)<br> |--- S2B_MSIL2A_20170914T93030_26_83_esaworldcover.tif<br> ...</p> <p>To properly conserve the file and directory structure of the ben-ge dataset, please place this archive file on the root level of the ben-ge dataset and then unpack it. Once unpacked, ben-ge/esaworldcover requires 8.7 GB of space.</p> <p>Other data modalities from ben-ge (as well as Sentinel-1/2 data as provided by BigEarthNet, <a href="https://bigearth.net/#downloads">https://bigearth.net/#downloads</a>), may be added as required. For reference, the recommended structure for the full dataset looks as follows:</p> <p>|<br> |--- ben-ge_meta.csv (ben-ge meta data)<br> |--- ben-ge_era-5.csv (ben-ge environmental data)<br> |--- ben-ge_esaworldcover.csv (patch-wise ben-ge land-use/land-cover data)<br> |--- dem/ (digital elevation model data)<br> | |--- S2A_MSIL2A_20171208T093351_3_82_dem.tif<br> | ...<br> |--- esaworldcover/ (land-use/land-cover data)<br> | |--- S2B_MSIL2A_20170914T93030_26_83_esaworldcover.tif<br> | ...<br> |--- sentinel-1/ (Sentinel-1 SAR data)<br> | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43/<br> | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_labels_metadata.json (BigEarthNet label file)<br> | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VH.tif (BigEarthNet/Sentinel-1 VH polarization data)<br> | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VV.tif (BigEarthNet/Sentinel-1 VV polarization data)<br> | ...<br> |--- sentinel-2/ (Sentinel-2 multispectral data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83/<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B01.tif (BigEarthNet/Sentinel-2 Band 1 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B02.tif (BigEarthNet/Sentinel-2 Band 2 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B03.tif (BigEarthNet/Sentinel-2 Band 3 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B04.tif (BigEarthNet/Sentinel-2 Band 4 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B05.tif (BigEarthNet/Sentinel-2 Band 5 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B06.tif (BigEarthNet/Sentinel-2 Band 6 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B07.tif (BigEarthNet/Sentinel-2 Band 7 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B08.tif (BigEarthNet/Sentinel-2 Band 8 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B09.tif (BigEarthNet/Sentinel-2 Band 9 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B11.tif (BigEarthNet/Sentinel-2 Band 11 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B12.tif (BigEarthNet/Sentinel-2 Band 12 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B8A.tif (BigEarthNet/Sentinel-2 Band 8A data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_labels_metadata.json (BigEarthNet label file)<br> ...</p> <p><br> <strong>More Information</strong><br> For more information, please refer to <a href="https://github.com/HSG-AIML/ben-ge">https://github.com/HSG-AIML/ben-ge</a>.</p> <p><br> <strong>Citing ben-ge</strong><br> If you use data contained in this archive, please cite the following paper:</p> <blockquote> <p>M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023.</p> </blockquote>
ben-ge/DEM: BigEarthNet Extended with Geographical and Environmental Data/Elevation Data
<p><strong>ben-ge/DEM: BigEarthNet Extended with Geographical and Environmental Data/Elevation Data</strong><br> </p> <blockquote> <p>M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023.</p> </blockquote> <p>ben-ge is a multimodal dataset for Earth observation <a href="http://(https://github.com/HSG-AIML/ben-ge">(https://github.com/HSG-AIML/ben-ge</a>) that serves as an extension to the BigEarthNet dataset. ben-ge complements the Sentinel-1/2 data contained in BigEarthNet by providing additional data modalities:</p> <p>* elevation data extracted from the Copernicus Digital Elevation Model GLO-30;<br> * land-use/land-cover data extracted from ESA Worldcover;<br> * climate zone information extracted from Beck et al. 2018;<br> * environmental data concurrent with the Sentinel-1/2 observations from the ERA-5 global reanalysis;<br> * a seasonal encoding.</p> <p>This archive contains the digital elevation model (DEM) data of ben-ge, which were extracted from the Copernicus Digital Elevation Model (GLO-30). </p> <p><strong>Data</strong></p> <p>Topographic maps are generated based on the global Copernicus Digital Elevation Model (GLO-30) (<a href="https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model">https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model</a>). Relevant GLO-30 map tiles from the 2021 data release were downloaded through AWS (<a href="https://registry.opendata.aws/copernicus-dem/">https://registry.opendata.aws/copernicus-dem/</a>), reprojected into the coordinate frame of the corresponding Sentinel-1/2 patches and interpolated with bilinear resampling to 10 m resolution on the ground. </p> <p>Elevation data are provided in a separate geotiff file for each patch. The naming convention for these files uses the Sentinel-2 patch_id to which we append _dem.tif. Each file contains a single band with 16-bit integer values that refer to the elevation of that pixel over sea level.</p> <p>Relevant meta data for the ben-ge dataset are compiled in the file ben-ge_meta.csv. This file resides on the root level of this archive and contains the following data for each patch:<br> * patch_id: the Sentinel-2 patch id, which plays a central role for cross-referencing different data modalities for individual patches;<br> * patch_id_s1: the Sentinel-1 patch id for this specific patch;<br> * timestamp_s2: the timestamp for the Sentinel-2 observation;<br> * timestamp_s1: the timestamp for the Sentinel-1 observation;<br> * season_s2: the seasonal encoding (see below) for the time of the Sentinel-2 observation;<br> * season_s1: the seasonal encoding (see below) for the time of the Sentinel-1 observation;<br> * lon: longitude (WGS-84) of the center of the patch [degrees];<br> * lat: latitude (WGS-84) of the center of the patch [degrees];<br> * climatezone: integer value indicating the climate zone based on Beck et al. 2018 (see below for details).</p> <p><br> <strong>File and directory structure</strong></p> <p>This archive contains the following directory and file structure:</p> <p>|<br> |--- README (this file)<br> |--- ben-ge_meta.csv (ben-ge meta data)<br> |--- dem/ (digital elevation model data)<br> |--- S2A_MSIL2A_20171208T093351_3_82_dem.tif<br> ...</p> <p>To properly conserve the file and directory structure of the ben-ge dataset, please place this archive file on the root level of the ben-ge dataset and then unpack it. Once unpacked, ben-ge/DEM requires 17.2 GB of space.</p> <p>Other data modalities from ben-ge (as well as Sentinel-1/2 data as provided by BigEarthNet, <a href="https://bigearth.net/#downloads">https://bigearth.net/#downloads</a>), may be added as required. For reference, the recommended structure for the full dataset looks as follows:</p> <p>|<br> |--- ben-ge_meta.csv (ben-ge meta data)<br> |--- ben-ge_era-5.csv (ben-ge environmental data)<br> |--- ben-ge_esaworldcover.csv (patch-wise ben-ge land-use/land-cover data)<br> |--- dem/ (digital elevation model data)<br> | |--- S2A_MSIL2A_20171208T093351_3_82_dem.tif<br> | ...<br> |--- esaworldcover/ (land-use/land-cover data)<br> | |--- S2B_MSIL2A_20170914T93030_26_83_esaworldcover.tif<br> | ...<br> |--- sentinel-1/ (Sentinel-1 SAR data)<br> | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43/<br> | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_labels_metadata.json (BigEarthNet label file)<br> | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VH.tif (BigEarthNet/Sentinel-1 VH polarization data)<br> | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VV.tif (BigEarthNet/Sentinel-1 VV polarization data)<br> | ...<br> |--- sentinel-2/ (Sentinel-2 multispectral data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83/<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B01.tif (BigEarthNet/Sentinel-2 Band 1 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B02.tif (BigEarthNet/Sentinel-2 Band 2 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B03.tif (BigEarthNet/Sentinel-2 Band 3 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B04.tif (BigEarthNet/Sentinel-2 Band 4 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B05.tif (BigEarthNet/Sentinel-2 Band 5 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B06.tif (BigEarthNet/Sentinel-2 Band 6 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B07.tif (BigEarthNet/Sentinel-2 Band 7 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B08.tif (BigEarthNet/Sentinel-2 Band 8 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B09.tif (BigEarthNet/Sentinel-2 Band 9 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B11.tif (BigEarthNet/Sentinel-2 Band 11 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B12.tif (BigEarthNet/Sentinel-2 Band 12 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B8A.tif (BigEarthNet/Sentinel-2 Band 8A data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_labels_metadata.json (BigEarthNet label file)<br> ...</p> <p><br> <strong>More Information</strong></p> <p>For more information, please refer to <a href="https://github.com/HSG-AIML/ben-ge">https://github.com/HSG-AIML/ben-ge</a>.</p> <p><br> <strong>Citing ben-ge</strong><br> If you use data contained in this archive, please cite the following paper:</p> <blockquote> <p>M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023. </p> </blockquote> <p><br> </p>
ben-ge-8k: BigEarthNet Extended with Geographical and Environmental Data
<p><strong>ben-ge-8k: BigEarthNet Extended with Geographical and Environmental Data</strong><br> </p> <blockquote> <p>M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023.</p> </blockquote> <p>ben-ge-8k is a small-scale multimodal dataset for Earth observation that is a subset of the ben-ge dataset (https://github.com/HSG-AIML/ben-ge), which in turn serves as an extension to the BigEarthNet dataset. ben-ge complements the Sentinel-1/2 data contained in BigEarthNet by providing additional data modalities:</p> <p>* elevation data extracted from the Copernicus Digital Elevation Model GLO-30;<br> * land-use/land-cover data extracted from ESA Worldcover;<br> * climate zone information extracted from Beck et al. 2018;<br> * environmental data concurrent with the Sentinel-1/2 observations from the ERA-5 global reanalysis;<br> * a seasonal encoding.</p> <p>ben-ge-8k contains 8000 patches out of 590,326 patches in the full ben-ge dataset. These 8000 patches were sampled in such a way that for each of the 8 most common ESA WorldCover land-use/land-cover classes (tree cover, shrubland, grassland, cropland, built-up, bare/sparse vegetation, permanent water bodies, herbaceous wetland), we sampled 1000 patches randomly and used the fractional coverage of this class as a weight in the sampling process. As a result, these classes are slightly more balanced in ben-ge-8k than in the full dataset.</p> <p><br> <strong>Data Modalities and Products</strong></p> <p><br> <strong>Meta Data</strong></p> <p>Relevant meta data for the ben-ge-8k dataset are compiled in the file ben-ge-8k_meta.csv. This file resides on the root level of this archive and contains the following data for each patch:<br> * patch_id: the Sentinel-2 patch id, which plays a central role for cross-referencing different data modalities for individual patches;<br> * patch_id_s1: the Sentinel-1 patch id for this specific patch;<br> * timestamp_s2: the timestamp for the Sentinel-2 observation;<br> * timestamp_s1: the timestamp for the Sentinel-1 observation;<br> * season_s2: the seasonal encoding (see below) for the time of the Sentinel-2 observation;<br> * season_s1: the seasonal encoding (see below) for the time of the Sentinel-1 observation;<br> * lon: longitude (WGS-84) of the center of the patch [degrees];<br> * lat: latitude (WGS-84) of the center of the patch [degrees];<br> * climatezone: integer value indicating the climate zone based on Beck et al. 2018 (see below for details).</p> <p><br> <strong>Digital Elevation Model (Copernicus DEM GLO-30)</strong></p> <p>DEM data are contained in the dem/ directory of this archive.</p> <p>Topographic maps are generated based on the global Copernicus Digital Elevation Model (GLO-30) (https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model). Relevant GLO-30 map tiles from the 2021 data release were downloaded through AWS (https://registry.opendata.aws/copernicus-dem/), reprojected into the coordinate frame of the corresponding Sentinel-1/2 patches and interpolated with bilinear resampling to 10 m resolution on the ground.</p> <p>Elevation data are provided in a separate geotiff file for each patch. The naming convention for these files uses the Sentinel-2 patch_id to which we append _dem.tif. Each file contains a single band with 16-bit integer values that refer to the elevation of that pixel over sea level.</p> <p><br> <strong>Land-use/Land-cover Data (ESA WorldCover)</strong></p> <p>Land-use/land-cover data are contained in the esaworldcover/ directory of this archive.</p> <p>Land-use/land-cover map tiles matching the Sentinel-1/2 patches were extracted from ESA WorldCover (https://esa-worldcover.org). Relevant tiles were downloaded and reprojected into the coordinate frame of the corresponding Sentinel-1/2 patches. WorldCover data are available both as maps and as class fractions that are aggregated over each patch.</p> <p>Land-use/land-cover map data are provided in a separate geotiff file for each patch. The naming convention for these files uses the Sentinel-2 patch_id to which we append _esaworldcover.tif. Each file contains a single band with 8-bit integer values that map to land-use/land-cover definitions provided by the ESA WorldCover Product User Manual (https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf) (page 15).</p> <p>The file ben-ge-8k_esaworldcover.csv contains the fractions by which each of the different classes cover the corresponding patch. This product may be useful to generate single-label or multi-label targets for different classification setups.</p> <p><br> <strong>Environmental Data (ERA-5)</strong></p> <p>Weather data are contained in the ben-ge-8k_era-5.csv file.</p> <p>Weather data at the time of observation (temperature at 2 m above the ground, relative humidity, wind vectors at 10 m above the ground) are extracted from the ERA-5 global reanalysis (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels) for the pressure level at the mean elevation of the observed scene and the time of observation (separately queried for Sentinel-1/2 observations).</p> <p>Environmental data are available in the file ben-ge-8k_era-5.csv. For each patch, identified through the Sentinel-2 patch_id or the corresponding Sentinel-1 patch id patch_id_s1, the file contains the following parameters:<br> * atmpressure_level: atmospheric pressure level at which parameters have been queried [mbar]<br> * temperature_s2: temperature 2m above ground at the time of the Sentinel-2 observation [K]<br> * temperature_s1: temperature 2m above ground at the time of the Sentinel-1 observation [K]<br> * wind-u_s2: eastward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s]<br> * wind-u_s1: eastward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-1 observation [m/s]<br> * wind-v_s2: northward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s]<br> * wind-v_s1: northward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s]<br> * relhumidity_s2: relative humidity at the time of the Sentinel-2 observation [%]<br> * relhumidity_s1: relative humidity at the time of the Sentinel-1 observation [%]</p> <p>as extracted from the ERA-5 global reanalysis (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels) for the patch location. Please see the corresponding documentation for details.</p> <p><br> <strong>Seasonal Encoding</strong><br> To capture the season at the time of observation, we apply a non-linear encoding that scale the date of the observation into the interval [0, 1], referring to [winter, summer] solstice. For any given date, we derive the fractional year and shift it by 9 days such that 21 June has the fractional year 0.5 and 22 December has the fractional year 0 or 1. To account for this ambiguity and the periodicity of the seasons, we modulate the fractional year with a sine function such that 21 June leads to a seasonal encoding of 1 and 22 December leads to a seasonal encoding of 0.</p> <p>Seasonal encodings are provided by the column season in the ben-ge-8k_meta.csv file. Season values cover the interval [0,1] as a continuous variable where 1 refers to summer solstice and 0 refers to winter solstice.</p> <p><br> <strong>Climate zone classification (Beck et al. 2018)</strong></p> <p>Patch-based climate zone classifications, based on the Köppen-Geiger scheme, were extracted from Beck et al. (2018) (https://www.nature.com/articles/sdata2018214), utilizing their present-day 1-km resolution map. Due to geographical focus of BigEarthNet on Europe, only 11 out of 27 different classes are present in this dataset. Please note that patches that are fully covered by surface water have no climate zone class assigned to them (class label equals zero in this case). Labels are encoded as discrete integer values that follow the schema introduced by Beck et al. 2018 in their legend.txt file that is included here: https://doi.org/10.6084/m9.figshare.6396959.</p> <p>Climate zone classification labels are provided by the column climatezone in the ben-ge-8k_meta.csv file.</p> <p><br> <strong>File and directory structure</strong></p> <p>This archive contains the following directory and file structure:</p> <p>|<br> |--- README (this file)<br> |--- ben-ge-8k_meta.csv (ben-ge-8k meta data)<br> |--- ben-ge-8k_era-5.csv (ben-ge-8k environmental data)<br> |--- ben-ge-8k_esaworldcover.csv (patch-wise ben-ge-8k land-use/land-cover data)<br> |--- dem/ (digital elevation model data)<br> | |--- S2A_MSIL2A_20171208T093351_3_82_dem.tif<br> | ...<br> |--- esaworldcover/ (land-use/land-cover data)<br> | |--- S2B_MSIL2A_20170914T93030_26_83_esaworldcover.tif<br> | ...<br> |--- sentinel-1/ (Sentinel-1 SAR data)<br> | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43/<br> | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_labels_metadata.json (BigEarthNet label file)<br> | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VH.tif (BigEarthNet/Sentinel-1 VH polarization data)<br> | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VV.tif (BigEarthNet/Sentinel-1 VV polarization data)<br> | ...<br> |--- sentinel-2/ (Sentinel-2 multispectral data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83/<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B01.tif (BigEarthNet/Sentinel-2 Band 1 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B02.tif (BigEarthNet/Sentinel-2 Band 2 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B03.tif (BigEarthNet/Sentinel-2 Band 3 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B04.tif (BigEarthNet/Sentinel-2 Band 4 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B05.tif (BigEarthNet/Sentinel-2 Band 5 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B06.tif (BigEarthNet/Sentinel-2 Band 6 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B07.tif (BigEarthNet/Sentinel-2 Band 7 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B08.tif (BigEarthNet/Sentinel-2 Band 8 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B09.tif (BigEarthNet/Sentinel-2 Band 9 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B11.tif (BigEarthNet/Sentinel-2 Band 11 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B12.tif (BigEarthNet/Sentinel-2 Band 12 data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_B8A.tif (BigEarthNet/Sentinel-2 Band 8A data)<br> | |--- S2B_MSIL2A_20170818T112109_31_83_labels_metadata.json (BigEarthNet label file)<br> | ...<br> |--- splits/<br> |--- ben-ge-8k_test.csv (index file for test split, 10%)<br> |--- ben-ge-8k_validation.csv (index file for validation split, 10%)<br> |--- ben-ge-8k_train.csv (index file for training splits, 80%)</p> <p>Once unpacked, ben-ge-8k requires 4.2 GB of space.</p> <p><br> <strong>More Information</strong></p> <p>For more information, please refer to https://github.com/HSG-AIML/ben-ge.</p> <p><br> <strong>Citing ben-ge-8k</strong><br> If you use data contained in this archive, please cite the following two papers:</p> <blockquote> <p>M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023.</p> <p>G. Sumbul, A. d. Wall, T. Kreuziger, F. Marcelino, H. Costa, P. Benevides, M. Caetano, B. Demir, V. Markl, "BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval", IEEE Geoscience and Remote Sensing Magazine, 2021, doi: 10.1109/MGRS.2021.3089174.</p> </blockquote> <p> </p> <p> </p>
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