dataesr/scientific-paragraphs-categorization huggingface dataset:
Project The dataset on Global Urban Expansion is the result of the work carried out by a group of researchers from the Beijing Normal University, the China University of Geosciences and Murray State University in the USA. Their aim was to create a new dataset on urban expansion using fully convolutional network (FCN)-based methods, which would be able to overcome some of the limitations of previous datasets on the same topic: outdated datasets, low spatial resolutions and low levels of accuracy. The dataset provides useful information for studies addressing global urbanization and its impacts on the environment. It considers as urban all those built-up areas where human-constructed or artificial elements cover more than half of the area or pixel.
Production method A specific fully convolutional network (FCN) was developed to map the urban areas in the Global Urban Expansion dataset. FCN are deep learning structures based on convolutional neural networks (CNN) that employ pixel-to-pixel image recognition. The FCN was fed with different sources of input data: Nighttime Light (NTL) imagery from NOAA and NPP-VIIRS, as well as Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data from MODIS. Other auxiliary data sources were also employed to obtain the Global Urban Expansion dataset: urban population statistics, Landsat imagery and the GHS and LC-CCI LUC datasets. LST data is only available for the period 2000-2016 and was not used to map the urban areas in 1992 and 1996. The FCN was calibrated with data from MODIS Land Cover, differentiating urban from non-urban areas.
Practical considerations Users can explore the different datasets available online, 4 including the complementary layer about the standard error of the Impervious Surface Percentage raster and the HBASE probability layer. Full metadata for GMIS and HBASE is also available online. 5  GMIS and HBASE have some limitations associated with their production methodology. For example, they may present areas of missing information due to cloud cover or other factors. The technical documents for the product (cited below) provide a detailed description of all these limitations. As part of the same project, Landsat imagery composites for 66 urban areas are also available for download. 6 4 https://sedac. ciesin. columbia. edu/mapping/gmis-hbase/explore-view/. 5 https://sedac. ciesin. columbia. edu/data/set/ulandsat-hbase-v1/metadata.
Project The European Settlement Map (ESM) is part of the Global Human Settlement Layer (GHSL) project, supported by the European Commission through the Joint Research Centre (JRC) and the Directorate General for Regional and Urban Policy (DG REGIO). ESM complements the GHSL global products by providing an urban settlement map for Europe at a very detailed spatial resolution: 2-2. 5 m versus 30 m for the GHSL. Both products share similar automatic methods for extracting LUC information from satellite imagery. ESM was initially released in 2014, with successive updates in 2016, 2017 and 2019. In 2014, a dataset was created for the reference year 2012, showing the percentage of the surface area that was built up. This was revised with a new production methodology in 2016 and again in 2017. The first update improved the accuracy of the product and its consistency with population data.
The spatial resolution was also improved: from 100 to 10 m. The second update increased the spatial and thematic detail of the product, at 2. 5 m and differentiating between 12 classes. A new dataset at 2 m for the year 2015 was released in 2019, using a different production methodology. Unlike previous editions, this map only shows the extent of built-up areas, without providing further information about the built-up fraction per pixel. In addition to the base layer delineating built-up areas, the latest edition of the product (2019) includes a classification differentiating residential from non-residential areas at a spatial resolution of 10 m.
Production method The ESM production method has changed over time, although it has always been fully automatic. The latest edition (2019) was produced at 2 m on the basis of the Copernicus VHR_IMAGE_2015 imagery dataset, made up of images captured by the satellites Pleiades, Deimos-02, WorldView-2, WorldView-3, GeoEye-01 and Spot 6/7. The imagery was classified through a scene-based classification algorithm: Symbolic Machine Learning (SML). The first three editions of ESM were obtained at 100, 10 and 2. 5 m through a textural and morphological technique of unsupervised built-up area detection. Spot 6/7 imagery was used as an input. In the third edition (2017), auxiliary data sources (Open Street Map, Urban Atlas…) were also used to provide more thematic detail, distinguishing between 13 LUC categories, instead of just between built-up and non-built-up areas.
GLC-Share-Dominant land cover type -Raster file with LUC map displaying the dominant land cover type -Layer style file for ArcGIS (. lyr) -Text document showing the classification legend for the dataset GLC-Share-Sources -Raster file with information about which LUC dataset was used to map each area of the world -Layer style file for ArcGIS (. lyr) -Excel spreadsheet with information about which LUC dataset was used to map each area of the world -Text document explaining the downloaded product GLC-Share-Artificial surfaces -Raster file with information about the proportion of artificial surfaces.
Geodatabase files with N2K vector layers -Layer style files for ArcGIS (. lyr), QGIS (. qml) and any other GIS software (. sld) (Legend folder) -Metadata file (. xml) (Metadata folder) -PDF with nomenclature guidelines N2K 2012 (GeoPackage) -GeoPackage files with N2K vector layers -Layer style files for ArcGIS (. lyr), QGIS (. qml) and any other GIS software (. sld) (Legend folder) -Metadata file (. xml) (Metadata folder) -PDF with nomenclature guidelines Product: N2K 2000 -OBJECTID: Unique identifier for each polygon -ID -UID -SITECODE -GRASSTYPE -MAES_1_12: MAES class Level 1 for 2012 -MAES_2_12: MAES class Level 2 for 2012 -MAES_3_12: MAES class Level 3 for 2012 -MAES_4_12: MAES class Level 4 for 2012 -COMMENT_12: Comments on the 2012 mapping -NODATA_12: Objects with no data in 2012 -MAES_1_06: MAES class Level 1 for 2006 -MAES_2_06: MAES class Level 2 for 2006 -MAES_3_06: MAES class Level 3 for 2006 -MAES_4_06: MAES class Level 4 for 2006 -COMMENT_06: Comments on the 2006 mapping -NODATA_06: Objects with no data in 2006 -CHANGECODE: 2006-2012 changes -AREA_HA: Area of the polygon, in hectares -ID: unique identifier for each polygon.
RZ) is one of the local datasets produced as part of the Copernicus Land Monitoring Programme. It focuses on riparian areas (i. e. transitional areas between land and freshwater ecosystems with very specific characteristics) associated with Strahler level 2-8 rivers. This product was created to support the Mapping and Assessment of Ecosystems and their Services (MAES) within the context of the EU Biodiversity Strategy for 2020. It is also intended for use in relation to the Habitats, Birds and Water Framework Directives. The Riparian Zones dataset was initially launched in 2015, with an extension in 2017/18 to include riparian areas from Strahler 2 rivers. In 2017 the classification scheme was adapted to make it compatible with other local products developed under the Copernicus Land Monitoring framework.
Geodatabase files with Coastal Zones vector layers -Layer style files for ArcGIS (. lyr), QGIS (. qml) and any other GIS software (. sld) (Symbology folder) -Metadata file (. xml,. gfs) (Metadata folder) Coastal zones 2018 (Geodatabase) -fid: Identifier for each polygon -ID: Unique identifier for each polygon -DU: -CODE_1_18: LUC category for the Level 1 classification legend -CODE_2_18: LUC category for the Level 2 classification legend -CODE_3_18: LUC category for the Level 3 classification legend -CODE_4_18: LUC category for the Level 4 classification legend -CODE_5_18: LUC category for the Level 5 classification legend -COMMENT_18: Comments on the mapping -NODATA_18: Objects with no data in 2018 -AREA_HA: Area of the polygon, in hectares -Shape_Length: Perimeter of the polygon, in metres -Shape_Area: Area of the polygon, in square metresDatabaseLegend and codificationThe Coastal Zones dataset was produced following a hierarchical classification legend made up of five different levels, the most detailed of which is provided here.
Land Cover map (country/region) -Vector file with Land Cover map (. shp) Land change Himalaya region -Vector file with map of Land Cover changes (. shp) -Vector file with boundaries of the Himalaya region (. shp) Himalaya Regional Land Cover Database -Z007CODE: LUC Code -Z007USLB: LUC User Label -Z007PERC: Percentage of the LUC(s) making up the polygon -HECTARES: Area of the polygon, in hectares -AREA: Area of the polygon, in square meters LCCSMAIN2: Main LUC 2 -AUTO_ID: Unique identifier for each polygon Database 370 D. García-Álvarez and J. Lara HinojosaOpen Access This chapter is licensed under the terms of the creativecommons. org/licenses/by/4. 0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
5, the cross-tabulation between a map and reference sample data is used to assess the thematic accuracy of the map by calculating various different accuracy indices. We present examples of analyses based on cross-tabulation for four different cases: To validate a series of maps with two or more time points, to validate a map against a reference map, to validate a simulation against a reference map and to validate simulated changes against a reference map of changes. In the example exercises, we use CORINE and SIOSE maps from the Asturias Central Area and Ariège Valley datasets and maps of the Marqués de Comillas region of south-eastern Mexico (MarquesLUC dataset). The cross-tabulation techniques proposed by Robert Gilmore Pontius Jr. are applied in Chapter "Pontius Jr.
ProjectMCD12Q1, also known as MODIS Land Cover type, dates back to 2002, after the launch into space of the TERRA satellite carrying the MODIS sensor. The MODIS sensor provided a new source of imagery for global LUC mapping. This led to the appearance of the MODIS Land Cover project, which aimed to produce a yearly series of LUC maps that could satisfy the demands of different communities interested in climate and environmental monitoring at global or very coarse scales. At the time the dataset was launched, only a few global LUC datasets were available, usually at coarser resolutions. MODIS Land Cover was created by a team led by the University of Boston. Since 2002, six versions of the product have been developed. The latest is MODIS Land Cover Collection 6, which has included the most important changes in the production method of the dataset since its early developments.
Raster file with multiple bands, including LUC information in five different classification schemes and data quality (. hdf) -PDFs with technical information about the productLegend and codificationMCD12Q1 is distributed for six different, widely used classification schemes. The only one displayed here is the IGBP scheme, which is one of the most commonly used. However, more information about the codes and class descriptions for the other classification legends is available in the user guide for this dataset(Sulla-Menashe et al. 2019).
This research was funded by the following projects: INCERTIMAPS: Suitability and uncertainty of land use and land cover maps for the analysis and modelling of territorial dynamics ( PGC2018-100770-B-100 ) financed by the  Spanish State Research Agency (SRA)  and the  European Regional Development Fund (ERDF)  and  Herramientas para la Enseñanza de la Geomática con programas de Código Abierto  ( PE117519 ) supported by the  Programa de Apoyo a Proyectos para la Innovación y Mejoramiento de la Enseñanza (PAPIME) ,  Universidad Nacional Autónoma de México (UNAM). The editors would like to show their appreciation to all the contributing authors, without whom this book would not have been possible. format of this book, and  Nigel Walkington , for his diligent proofreading. Finally, the editors.
usgs. gov/products/gfsad1kcmv001/ https://lpdaac. usgs. gov/products/gfsad1kcdv001/ Download site https://lpdaac. usgs. gov/products/gfsad1kcmv001/ https://lpdaac. usgs. gov/products/gfsad1kcdv001/ Availability Format(s) Code Label Code Label Availability Format(s) Availability Format(s) Availability Format(s) Open Access. tiff Technical documentation Gumma et al. (2020), Oliphant et al. (2019), Phalke et al. (2020), Teluguntla et al. (2018), Xiong et al. (2017) Other references of interest Teluguntla et al. (2015) Availability Format(s) Code Label 0-100 Cropland Coverage (0-100%) ASAP rangeland mask Code Label 0-100 Rangeland Coverage (0-100%) Availability Format(s) Legend and codification Code Label Code Label 0 Availability Format(s) Code Label GHS-Built-up 2014 (250 m) Code Label 0-100 Built-up area density (1-100) -200 No data GHS-Built-up multi-temporal (30 m) Code Label Code Label Availability Format(s) Database GAIA Code Label a Code Label Code Label Code Label Code Label 1 Availability Format(s) Code Label Code Label 0 Availability Format(s) Availability Format(s) Availability Format(s) Availability Format(s) Code Label Code Label 0 Non-settlement 255 Settlement WSF 100 m, 250 m, 500 m, 1 km, 10 km Code Label Code Label 0-100 Availability Format(s) Availability Format(s) Legend and codification Code Label Code Label 0 Availability Format(s) Availability Format(s) Availability Format(s) Availability Format(s) Imperviousness built-up (Status Map) Code Label Code Label 0 Non built-up 255 Outside area 1 Built-up Imperviousness density (Status Map) Code Label Code Label D.
García-Álvarez et al. Tree Cover Change Mask Code Label Code Label 0 Code Label Code Label Database Legend and codification Small Woody Features (Geodatabase and GeoPackage) Code Label Code Label 1 Linear structures of trees, hedges, bushes and scrub Code Label Code Label 0 Availability Format(s) Code Label Code Label 0-1 Built-up percentage (0-100%) -2 No data ESM 2012 (2016)-100 m Code Label 0-1 Built-up percentage (0-100%) ESM 2012 (2016)-10 m Code Label 0-100 Built-up percentage (0-100%) ESM 2012 (2017)-100 m-Class 50 (Buildings) Code Label 0-1 Percentage (0-100%) of the selected class (50) ESM 2012 (2017)-10 m-Class 50 (Buildings) Code Label 0-100 Percentage (0-100%) of the selected class (50) ESM 2012 (2017)-2. 5 m Code Label Code Label 50 Code Label Code Label Code Label Code Label.
Downloads GLC-Share-Dominant land cover type -Raster file with LUC map displaying the dominant land cover type -Layer style file for ArcGIS (. lyr) -Text document showing the classification legend for the dataset GLC-Share-Sources -Raster file with information about which LUC dataset was used to map each area of the world -Layer style file for ArcGIS (. lyr) -Excel spreadsheet with information about which LUC dataset was used to map each area of the world -Text document explaining the downloaded product GLC-Share-Artificial surfaces -Raster file with information about the proportion of artificial surfaces.
- Geodatabase files with N2K vector layers -Layer style files for ArcGIS (. lyr), QGIS (. qml) and any other GIS software (. sld) (Legend folder) -Metadata file (. xml) (Metadata folder) -PDF with nomenclature guidelines N2K 2012 (GeoPackage) -GeoPackage files with N2K vector layers -Layer style files for ArcGIS (. lyr), QGIS (. qml) and any other GIS software (. sld) (Legend folder) -Metadata file (. xml) (Metadata folder) -PDF with nomenclature guidelines Product: N2K 2000 -OBJECTID: Unique identifier for each polygon -ID -UID -SITECODE -GRASSTYPE -MAES_1_12: MAES class Level 1 for 2012 -MAES_2_12: MAES class Level 2 for 2012 -MAES_3_12: MAES class Level 3 for 2012 -MAES_4_12: MAES class Level 4 for 2012 -COMMENT_12: Comments on the 2012 mapping -NODATA_12: Objects with no data in 2012 -MAES_1_06: MAES class Level 1 for 2006 -MAES_2_06: MAES class Level 2 for 2006 -MAES_3_06: MAES class Level 3 for 2006 -MAES_4_06: MAES class Level 4 for 2006 -COMMENT_06: Comments on the 2006 mapping -NODATA_06: Objects with no data in 2006 -CHANGECODE: 2006-2012 changes -AREA_HA: Area of the polygon, in hectares -ID: unique identifier for each polygon.
- Geodatabase files with Coastal Zones vector layers -Layer style files for ArcGIS (. lyr), QGIS (. qml) and any other GIS software (. sld) (Symbology folder) -Metadata file (. xml,. gfs) (Metadata folder) Coastal zones 2018 (Geodatabase) -fid: Identifier for each polygon -ID: Unique identifier for each polygon -DU: -CODE_1_18: LUC category for the Level 1 classification legend -CODE_2_18: LUC category for the Level 2 classification legend -CODE_3_18: LUC category for the Level 3 classification legend -CODE_4_18: LUC category for the Level 4 classification legend -CODE_5_18: LUC category for the Level 5 classification legend -COMMENT_18: Comments on the mapping -NODATA_18: Objects with no data in 2018 -AREA_HA: Area of the polygon, in hectares -Shape_Length: Perimeter of the polygon, in metres -Shape_Area: Area of the polygon, in square metresDatabaseLegend and codificationThe Coastal Zones dataset was produced following a hierarchical classification legend made up of five different levels, the most detailed of which is provided here.
Downloads 1 - 1 Land Cover map (country/region) -Vector file with Land Cover map (. shp) Land change Himalaya region -Vector file with map of Land Cover changes (. shp) -Vector file with boundaries of the Himalaya region (. shp) Himalaya Regional Land Cover Database -Z007CODE: LUC Code -Z007USLB: LUC User Label -Z007PERC: Percentage of the LUC(s) making up the polygon -HECTARES: Area of the polygon, in hectares -AREA: Area of the polygon, in square meters LCCSMAIN2: Main LUC 2 -AUTO_ID: Unique identifier for each polygon Database 370 D. García-Álvarez and J. Lara HinojosaOpen Access This chapter is licensed under the terms of the creativecommons. org/licenses/by/4. 0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
13 Land Use Cover Map Marqués de Comillas Ariège Valley (Val d'Ariège) Projection: WGS84/UTM 31N (EPSG: 32631) Associated files: BD_Val_Ariege (Word document file): explanation and legend 6 Review of Land Use Cover Datasets File name Name in the book CORINE Land Cover CLC_2000 CORINE Land Cover Map Val d'Ariège 2000 CLC_2012 CORINE Land Cover Map Val d'Ariège 2012 CLC_2018 CORINE Land Cover Map Val d'Ariège 2018 Model Drivers Roads_dist Distance to roads Simulation output CLC_predict_2018 Simulation LCM Val d'Ariège 2018 CLC_predict_2018_soft_UTM Soft prediction LCM Val d'Ariège 2018 00_12_18_transition_2_to_1 Transition potential map from agricultural to artificial areas 00_12_18_transition_3_to_1 Transition potential map from forests to artificial areas Markov18_class1_utm Markovian probability map for artificial areas Ariège Valley.
cover time series derived from change-based updating. In: Proceedings of the 7th International workshop on the analysis of multi-temporal remote sensing images: "our dynamic environment" (MultiTemp 2013), pp 1-3. https://doi. org/10. 1109/Multi-Temp. accuracy assessment framework for large-area land cover classification products derived from medium-resolution satellite data. Int J Remote Sens 27(4):663-683. https://doi. org/10. 1080/0143116050 0185284 Yang Y, Xiao P, Feng X, Li H (2017) Accuracy assessment of seven global land cover datasets over China. ISPRS J Photogramm Remote Sens 125:156-173. https://doi. org/10. 1016/j. isprsjprs. 2017.
6 in chapter "Global General Land Use Cover 500 m 2008 (500 m) Datasets with a Time Series of Maps" 2013 (500 m) GlobCover 300 m 2005, 2009 23 No Sect. 7 in chapter "Global General Land Use Cover Datasets with a Time Series of Maps" FROM-GLC 30 m 2010 (30 m) 11 Not recommended Sect. 8 in chapter "Global General Land Use Cover 10 m 2015 (30 m) Datasets with a Time Series of Maps" 2017 (30, 10 m) CGLS-LC100 100 m 2015-2019 23 Yes Sect. 9 in chapter "Global General Land Use Cover Datasets with a Time Series of Maps".
6 in chapter "Global General Atlas areas of Europe MMU: 2018 layer of Land Use Cover Datasets for (EU) 0. 25-1 ha changes Europe" N2K Natura 2000 1:5000- 2006, 2012, 11 Yes Sect. 7 in chapter "Global General reserves of 1:10,000 2018 Land Use Cover Datasets for Europe (EU) MMU: 0. 5 ha Europe" Riparian Riparian areas of 1:10,000 2012, 2018 56 One-date map Sect. 8 in chapter "Global General Zones Europe (EU) MMU: 0. 5 ha Land Use Cover Datasets for Europe" Coastal Coastal regions 1:10,000 2012, 2018 71 Yes, through Sect. 9 in chapter "Global General Zones of Europe (EU) MMU: 0. 5 ha layer of Land Use Cover Datasets for changes Europe" S2GLC Europe (EU) 10 m 2017 13 One-date map Sect. 10 in chapter "Global 2017 General Land Use Cover Datasets for Europe".
3 in chapter "Global Mask 2000 cover Thematic Land Use Cover Datasets Characterizing Vegetation Covers" SYNMAP 1 km 26 vegetation classes 2000 One-date map Sect. 4 in chapter "Global Thematic Land Use Cover Datasets Characterizing Vegetation Covers" GFCC 30 m Percentage of tree 2000, 2005, 2010, Yes Sect. 5 in chapter "Global cover and forest 2015 (tree cover) Thematic Land Use Cover gains/losses 1990-2000/2000-2005 Datasets Characterizing (forest change) Vegetation Covers" Hansen Forest 30 m Percentage of tree 2000-2019 Yes, through Sect. 6 in chapter "Global Map cover and forest specific layers of Thematic Land Use Cover gains/losses forest gains and Datasets Characterizing losses Vegetation Covers" MOD44B 250 m Percentage of tree 2000-2019 Yes Sect. 7 in chapter "Global cover Thematic Land Use Cover Datasets Characterizing Vegetation Covers" PTC Global 500 m Percentage of tree 2003 (1 km) Possible Sect.
8 in chapter "Global version 1 km cover 2008 (500 m) Thematic Land Use Cover Datasets Characterizing Vegetation Covers" FNF 25 m Forest extent 2007-2010 Possible Sect. 9 in chapter "Global 2015-2017 Thematic Land Use Cover Datasets Characterizing Vegetation Covers" Forests of the 250 m Percentage of tree 2010 One-date map Sect. 10 in chapter "Global World 2010 cover Thematic Land Use Cover Datasets Characterizing Vegetation Covers" TanDEM-X 50 m Forest/Non forest 2011/16 One-date map Sect. 11 in chapter "Global Forest/Non-Forest Thematic Land Use Cover Map Datasets Characterizing Vegetation Covers".
5 in "Supra- Features Woody Features moment national Thematic Land Use Cover Datasets" ESM Europe (EU) 2 m (2015) Built-up extent 2012, 2015 No Sect. 6 in "Supra- 2. 5 m (2012) (2015) national Thematic Land 10 m (2012) Residential areas Use Cover Datasets" extent (2012) 13 built-up categories (2012) Percentage of built-up areas (2012).
Int Yearbook Cartogr 18:21-32 Sons, New York, p 245 Robinson AH (1953) Elements of cartography, 1st edn. John Wiley & The American Cartographer, 13:3, 280, (1986) design. University of Wisconsin, Madison, US, p 105. Reprinted in Robinson AH (1952) The look of maps: an examination of cartographic 80F. Escobar Fig. 10 Cartographic representation of LUC changesReferences Appleton K, Lovett A, Sünnenberg G, Dockerty T (2002) Rural landscape visualisation from GIS databases: a comparison of approaches, options and problems. Comput Environ Urban Syst 26(2-3):141-162 Bertin J (1967) Sémiologie graphique. Les diagrammes, les réseaux et les cartes, Mouton, Gauthier-Villars, Paris, p 431 Brewer C (2021) ColorBrewer Tool. http://www. ColorBrewer. org. Accessed 8 July 2021 Brooks CEP, Carruthers N (1953) Handbook of statistical methods in meteorology.