dataesr/scientific-paragraphs-categorization huggingface dataset:
Product description CLC is made up of two spatial layers: a Land Use Cover map for each reference year  (1990, 2000, 2006, 2012, 2018)  and a layer of Land Use Cover changes for each analysis period  (1990-2000, 2000-2006, 2006-2012, 2012-2018). The reference map for each year provides Land Use Cover information for the total area of the participant countries. The map of changes only accounts for the changes that took place in the period under consideration. Rather than comparing two reference maps, the CLC layer of changes maps all changes bigger than 5ha and discards all technical changes that did not take place on the ground. CLC layers are provided in either vector (ESRI or GeoPackage databases) or raster (. tiff) formats. As might be expected, the vector data is much heavier than the raster data, because of its higher definition.
Downloads The base layers with LUC maps for each reference year (CLC) have the same structure and group of files, as do the change layers for each period of analysis (CHA). This is why we only describe the file structure once for each type of format. CLC 2018 (Geodatabase)/CHA 2012-2018 (Geodatabase) -Geodatabase files with CLC vector layers (DATA folder) -Folder with CLC vector data for French overseas departments -Layer style files for ArcGIS (. lyr), QGIS (. qml) and any other GIS software (. sld) (Legend folder) (continued) 2 https://land. copernicus. eu/pan-european/corine-land-cover. -Code_18: LUC code for the year 2018. -Remark -Area_Ha: Area of the polygon, in hectares. -ID: Unique identifier for each polygon. -Shape_Length: Perimeter of the polygon, in metres. -Shape_Area: Area of the polygon, in square metres.
Practical considerations CLC was originally mapped in vector format. This format provides higher precision and detail and is therefore recommended when working at local and regional scales. At national and supranational scales, raster data can be more suitable, as vector data is too heavy and may be difficult to handle in desktop computers with insufficient processing power. Users can download the vector CLC to rasterize the database to the spatial resolution they require. The 100 m offered is the reference resolution provided by the EEA, but it is not the only one at which the map could be used. Users should be aware that different mapping methodologies were used in different countries, and in some countries, at different times. This could result in significant differences in the way the landscape is mapped and conceptualised, which could introduce important sources of uncertainty in our studies and analyses.
The same category could be interpreted differently in different countries, and even within the same country, a particular category could be mapped differently at different times if the production method changes. Those wishing to analyse LUC change should therefore use the change layers rather than the maps. Project PELCOM (Pan-European Land Cover Monitoring) was a research project funded by the European Union that ran from 1996 to 1999. The main purpose of the project was to develop a consistent methodology to create a continental LUC map for Europe from remote sensing sources. Users were consulted about their needs and requirements and revealed that they would like to have LUC data at coarser and finer spatial resolutions than CLC, and that CLC could be updated more frequently. They also made clear that a dataset of this kind would be useful for environmental modelling and monitoring purposes.
Practical considerations The product is no longer available for download from the official website of the EEA. The only edition that can still be obtained is the map for 2005, which is available through the geoportal of the Université Catholique de Louvain, one of the producers of the dataset. The map can also be consulted online at the same website, without having to download it. While the GlobCorine classification legend focuses particularly on land use, GlobCover centres on land cover. GlobCorine can therefore be regarded as a complementary dataset to GlobCover.
Project N2K was developed as part of the Copernicus Land Monitoring programme. It maps land uses and covers in the areas that form part of the Natura 2000 network, plus a 2 km buffer zone around their perimeters. Natura 2000 is a network that protects natural areas with rare and threatened species or with rare types of natural habitat. The dataset first appeared in 2015. A reviewed edition was issued in 2017 with a new classification legend that made it compatible with other European local reference LUC datasets: Riparian Zones, N2K and the Coastal Zone product.
Project The Coastal Zones Land Cover/Land Use dataset is produced by the European Environment Agency (EEA) as part of the Copernicus Land Monitoring Service (CLMS). The dataset has been developed in collaboration with the Copernicus Marine Environment Monitoring Service (CMEMS) and representatives from the potential community of users. It is specifically intended for monitoring coastal areas and provides an important source of information for all EU policies dealing with coastal management and maritime spatial planning. The dataset maps, at very detailed scale, the land uses and covers in coastal areas in the 39 countries belonging to the EEA. The coastal area mapped is defined by a 10 km inland buffer zone and the Corine Land Cover (CLC) seawards buffer zone. Relevant estuaries, coastal lowlands and nature reserves that extend beyond the buffer zone have also been included.
The dataset's classification legend has been specifically designed to fit the needs of its user community. It is based on the Mapping and Assessment of Ecosystems and their Services (MAES) ecosystem typology and makes the product compatible with other CLMS local monitoring datasets, such as Urban Atlas, Riparian Zones and N2K. The dataset is composed of two LUC maps for the reference years 2012 and 2018, plus a change layer for the period 2012-2018. The dataset will be updated every 6 years, in accordance with the CLC production timeline.
Production method The Coastal Zones Land Cover/Land Use dataset is produced via computer-assisted photointerpretation of very high spatial resolution  (1. 5-4 m)  imagery from a wide variety of missions: SPOT, Pléiades, WorldView, SuperView, KOMPSat, Planet Dove, Deimos and TripleSat. A variable photointerpretation scale (1:5,000-1:10,000) was selected depending on the mapped landscape and feature characteristics. The following auxiliary datasets were also used in support of the photointerpretation process: CLC, Urban Atlas, HRL, Bing Maps and different imagery sources (DWH_MG2_CORE_01 Coverage, Sentinel-2, Landsat-8, national aerial imagery, Google Earth).
Product description Users can download the Coastal Zones dataset in two different formats: Geodatabase and GeoPackage. Different download files are available for each year of reference  (2012, 2018)  as well as for the change layer  (2012) (2013) (2014) (2015) (2016) (2017) (2018). All downloads include the same information: layers with LUC information, a style file for their symbolization in GIS and auxiliary data.
Project The SADC Land Cover Database is fruit of a project funded by the South African Department of Arts, Culture, Science and Technology (DACST) through the Regional Science and Technology Programme. It was coordinated by the Council for Scientific and Industrial Research (CSIR) in South Africa, with the participation of organizations from the different countries being mapped. The objective of the project was to deliver a coherent Land Use Cover map covering the Southern African Development Community (SADC) region. The project builds on earlier LUC mapping work carried out at national and regional scales for each of the mapped countries. The map covers those SADC countries that already had a LUC dataset available for their territory: Lesotho, Malawi, Mozambique, South Africa, Swaziland, Tanzania, Zimbabwe.
Production method The SADC Land Cover Database was obtained by harmonizing and fusing the different national and regional LUC datasets. All the datasets were originally obtained by classification or photointerpretation of Landsat imagery, although the reference years vary from country to country. The maps were combined by resampling to a spatial resolution of 1 km, before being reclassified according to the same classification system. This reduced the detail of the original maps, a deliberate action to avoid copyright and commercialisation issues.
Practical considerations A detailed description of the map categories is available in the dataset's metadata. The map's production method entails certain limitations and uncertainties, in that each country has been mapped by a different team, using different sources of imagery for different reference years. Inconsistencies may therefore arise when comparing information between countries. Project AFRICOVER was a project led and coordinated by the Food and Agriculture Organization (FAO) of the United Nations, which aimed to create georeferenced data for the African continent. The FAO helped the different countries and regions to develop their reference maps, establishing the standards for the final product. Twelve countries participated in the project (Burundi, Democratic Republic of Congo, Egypt, Eritrea, Kenya, Rwanda, Somalia, Sudan, Tanzania, Uganda, Libya and Malawi), which therefore required extensive coordination of many national and regional teams across Africa.
Project The Large-Scale Biosphere-Atmosphere Experiment in the Amazon (LBA) was an international project launched by the Brazilian scientific community in 1993. The main objectives were to study Amazonia and its role in the earth's ecosystem as well as to understand LUC changes in the area and their environmental consequences. As part of the project, a global LUC map covering South America was produced from imagery and data of the period 1987/91. Vegetation and soil maps for Brazil were also digitalized on the basis of previous resources. These maps are also available for any interested user as part of the same dataset.
Production method The LBA LUC map was produced after unsupervised classification of AVHRR imagery, postprocessing and labelling of the classification results. Different sources of auxiliary data were used in the production of the dataset to overcome the limitations of the imagery, including a Global Vegetation Index (GVI) layer, the UNESCO's Vegetation Map of South America, the Hueck's Vegetationsskarte Von Sudamerika and a potential vegetation map of South America based on the Holdridge bioclimatic scheme.
Production method The database was obtained by automatic segmentation of Landsat imagery for the reference year 2000 plus visual interpretation. The initial classification was refined by interpreting high resolution imagery from Google Earth. A layer of LUC changes was obtained by assessing the base map (2000) against historical imagery for the periods  1970-80, 1990 and 2007. No maps for the other years of reference are available, but only the respective layers of changes.
Product description The database is distributed at regional level in vector format for each of the countries and regions that make up the Himalayan region: Afghanistan, Bhutan, China-Yunnan Sheng, China-Xizang Zizhiqu, India, Nepal, Pakistan, Aksai Chin, Arunachal Pradesh, China/India, Jammu Kashmir and Myanmar. An additional vector layer with LUC changes for the period 1970-2007 is also included. The downloaded products consist solely of the vector layers with LUC data. No other auxiliary information is provided with the downloaded file. A detailed legend for the product can be downloaded separately in Excel or mdb formats. A layer with the boundaries of the region and its administrative units is also available for download. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material.
Production method The World's Forests 2000 map was produced in two stages. In the first stage, closed forest and open or fragmented forest categories were mapped on the basis of a classification of AVHRR imagery for the period 1995-1996. A complex methodology based on a mixture analysis model and a geographical stratification to account for regional variation in the mapped features was employed to calculate the fraction cover per pixel. The two LUC categories were extracted from these layers based on the tree cover percentages defined by the FAO: 40-100% for closed forest and 10-40% for open or fragmented forest. In the second stage, the Global Land Cover Characteristics Database (GLCC), obtained from a classification of AVHRR imagery for the period 1992/93, was used to map the remaining categories: other wooded land, other land cover and water.
Practical considerations The dataset can be easily visualized and consulted through a web-based visualization tool. 6 For those who want to work with data for the whole Earth rather than for specific areas of the world (tiles), the producers provide txt files with a full list of download links for each of the 6 layers that make up the product. Landsat 8 imagery enabled better detection and mapping of forest disturbance. Some uncertainties may therefore emerge when comparing forest losses before and after the inclusion of Landsat 8 imagery.
Project The Percent Tree Cover Global version is a dataset created within the context of the Global Mapping Project, which aimed to create a global reference database of geospatial information. The project was promoted by the International Steering Committee for Global Mapping (ISCGM) in cooperation with National Geospatial Information Authorities (NGIAs) from different countries and regions across the world. It came to an end in 2016, when the ISCGM decided to wind up the project and transfer all the data to the Geospatial Information Section of the United Nations. The PTC map was generated by a group of researchers from the Geospatial Information Authority of Japan (GSI) and Chiba University. Two versions of the map were produced: one for the reference year 2003 and another for the reference year 2008.
Project Global Rainfed, Irrigated and Paddy Croplands (GRIPC) is a map developed by researchers from German and American universities, who aimed to overcome some of the limitations of previous datasets focusing on irrigated croplands. At the time it was released, the dataset offered an up-to-date representation of irrigated croplands across the world at the highest spatial resolution available. It could be useful for those studying agricultural productivity, agricultural hydrology and food security in general.
Production method The GRIPC map is made up of 4 different categories. Uncropped areas were extracted from the non-cropland categories of the MODIS Land Cover database for the period 2004-2006. Paddy croplands were independently mapped from different sources, such as crop inventories, due to the challenges involved in classifying cloudy imagery in the tropics. Rainfed and irrigated cropland were mapped using a decision-tree classification algorithm (C4. 5) and the "boosting" machine learning technique. MODIS imagery was used as the input for the classification. Climate and agroecozones data were also used as auxiliary datasets. Probability layers obtained from the classification were combined with information from national and subnational cropland inventory-based datasets to finally map the rainfed and irrigated cropland areas.
Practical considerations GRIPC does not map various important irrigated cropland categories, such as deficit irrigation (irrigation occurring less than once a year), permanent crops (orchards and vineyards) and unharvested pastures. As there is no official website describing the GRIPC and its characteristics, users wishing to find out more about this dataset should consult the scientific paper in which it was presented  (Pittman et al. 2010). Practical considerations GFSAD1KCM and GFSAD1KCD were produced independently for different purposes and cannot therefore be compared. Although GFSAD1KCD provides information on crop dominance, it can also be used to study cropland extent.
Project The Global Synergy Cropland Map is a dataset created within the framework of the Spatial Production Allocation Model (SPAM), which maps agriculture production across the world. It is a joint effort involving different institutions and universities across the world: AGRIRS, IFPRI Chinese Academy of Agricultural Sciences and Victoria University of Wellington. The project team aimed to create a more accurate cropland dataset that would be useful for agricultural monitoring and food security policies and studies. The obtained map is a critical input of SPAM.
Production method A self-adapting statistics allocation model (SASAM) is used to generate the Global Synergy Cropland Map, using LUC datasets at global, supranational and national scales as input, as well as FAO agricultural statistics at national and subnational levels. Two layers were generated by the model. Firstly, an agreement layer, which shows the level of agreement of all the datasets regarding the location of cropland areas, and secondly, an average cropland percentage layer, obtained by calculating the average of all the input maps. For the agreement layer, datasets with a higher accuracy are given more weight. This accuracy is based on the agreement between each input dataset and the FAO statistics. For the cropland percentage layer, the cropland category definitions in the input maps were translated into cropland percentages.
Practical considerations More information about the associated SPAM project is available at www. mapspam. info. The website includes all the spatial datasets about agricultural production generated as part of the project. These complement the information provided by the cropland map reviewed here. 6 UCL-Unified Cropland Layer Project Global Food Security-support Analysis Data 30 metre (GFSAD30) was a project aimed at producing high-resolution cropland maps to inform global food and water security studies and policies. The project sought to overcome some of the limitations presented by previous cropland datasets, such as sources of uncertainty, insufficient precision in the allocation of cropped areas, and a lack of information regarding the intensity and irrigation status of cropland areas.
GFSAD30 was the continuation of earlier projects (the GFSAD1KCM and GFSAD1KCD datasets described above) with similar purposes. They all formed part of the MEa-SUREs (Making Earth System Data Records for Use in Research Environments) programme, which promotes the use of data from NASA missions to produce innovative products that are useful for research and policy-making. Various different US institutions (USGS, BAER Institute, U. S. Department of Agriculture, U. S. Environmental Protection Agency) and universities (New Hampshire, California, Wisconsin, Northern Arizona) took part in the project, together with Google and institutions from other countries (ICRISAT, IAARD). A global map of cropland extent at a spatial resolution of 30 m for the reference year 2015 was delivered as part of the project.
Production method GFSAD30 is made up of 7 datasets which were independently produced for different regions across the world: Europe, Middle East, Russia and Central Asia; Africa; Australia, New Zealand, China, and Mongolia; Southeast and Northeast Asia; North America; and South America. Each dataset was produced following a specific production method, although they all share certain common features. The same imagery source (Landsat) was used for all 7 datasets. Sentinel-2 imagery was also used to map the extent of cropland in Africa. Other auxiliary data, such as elevation data from the SRTM radar, were used for the production of several datasets. In all cases, the extent of cropland was computed using the Google Earth Engine (GEE) platform. The classification workflow varies in each case.
Product description GFSAD30 is distributed in tiles with a 10º edge for each of the mapped regions. Datasets are available from different servers or tools, including Data Pool, NASA Earthdata Search, USGS EarthExplorer and the DAAC2Disk Utility. We recommend users to download the dataset through NASA Earthdata Search and USGS EarthExplorer, on which the geographical coverage of each tile can be visualized. In most cases, the download only includes a raster file with the extent of cropland in. tiff format. Nonetheless, the download from the Data Pool server also includes a metadata file and a preview image of the product.
Practical considerations The global map obtained after merging the 7 GFSAD30 datasets can be consulted online at the project's website. 3 he website also includes other important products for mapping cropland at coarser scales (250 m, 1 km), as well as datasets about irrigated/rainfed cropland areas for South Asia, Iran, Afghanistan and Australia. Users can also download a dataset validating the product (GFSAD30VAL). 4 n addition to the technical documentation published as reports and papers in journals, other interesting technical documents are also available on the website.
Practical considerations Although not directly available for download, access to the original map at a spatial resolution of 250m is possible on request to the members of the ASAP Team. 6 Previous versions of the dataset for Africa developed by  Vancutsem et al. (2013)  and Pérez Hoyos (2017a) can also be accessed in the same way. 6 https://mars. jrc. ec. europa. eu/asap/about. php. 1 Global Urban Land.
Project Global Urban Land, also referred to as Multi-temporal Global Impervious Surface (MGIS), is a project developed by researchers from different Chinese universities (Sun Yat-sen, East China Normal, Guangzhou and Jiangsu Normal) to create a high-resolution multi-temporal urban land dataset. They aimed to provide high-resolution data about urban areas at multiple dates, which could be useful for those studying urbanization and the impact of artificial surfaces and human activities on the environment. In this dataset, urban land is understood as an impervious surface. It can therefore be assimilated to all the datasets mapping artificial or impervious surfaces, such as GAIA. Initially, the dataset was produced for the period 1990-2010, with maps every 5 years. However, it has since been updated, with new data for the years 1980 and 2015.
The final Global Urban Land dataset was obtained after applying region-specific segmentation thresholds to the NUACI images showing the degree of imperviousness. After this step, a binary urban/non-urban map was generated. For the calibration of the NUACI index, as well as for the application of segmentation thresholds, cities were randomly assigned to three equal-sized groups: centroid sites, threshold sites and testing sites. Different criteria for index calibration and threshold segmentation were decided for each type of site.
Product description The Global Urban Land dataset can be downloaded from three different servers: Baidu Drive, Google Drive and FTP. From them, users will be able to separately download the dataset for each of the available years of reference. For each year, there is a compressed folder (. zip) containing the whole dataset distributed in tiles. An auxiliary vector file (. shp) is provided to help users identify the number of the files corresponding to their area of interest (field "grid_id"). The scientific paper presenting the dataset is also available for download, together with a text file with relevant technical information about the product and the reference data used to produce the dataset for the initial period 1990-2010.