dataesr/scientific-paragraphs-categorization huggingface dataset:
Execution If necessary, install the Processing R provider plugin, and download the Local accuracy assessment statistics. rsx R script into the R scripts folder (processing/ rscripts). For more details, see chapter "About This Book" of this book. Step 1 Open the Local accuracy assessment statistics function and fill in the required parameters (see Fig. 2 ). The input for this tool is the point layer containing the LUC random sample dataset. Select the type of accuracy assessment statistic to be obtained ("Overall"), and indicate the corresponding attribute table columns with the reference data and the classified data. The category can also be indicated, although this is only used to estimate the user's and producer's accuracy values. The remaining value to be set is the bandwidth, which in this exercise is 0.
Abstract Global general Land Use and Land Cover (LUC) datasets map all land uses and covers across the globe, without focusing on any specific use or cover. This chapter only reviews those datasets available for one single date, which have not been updated over time. Seven different datasets are described in detail. Two other ones were identified, but are not included in this review, because of its coarsens, which limits their utility: Mathews Global Vegetation/Land Use and GMRCA LULC. The first experiences in global LUC mapping date back to the 1990s, when leading research groups in the field produced the first global LUC maps at fine scales of 1 km spatial resolution: the UMD LC Classification and the Global Land Cover Characterization. Not long afterwards, in an attempt to build on these experiences and take them a stage further, an international partnership produced GLC2000 for the reference year 2000.
These initial LUC mapping projects produced maps for just one reference year and were not continued or updated over time. Subsequent projects have mostly focused on the production of timeseries of global LUC maps, which allow us to study LUC change over time (see Chapter "Global General Land Use Cover Datasets with a Time Series of Maps"). As a result, there are relatively few single-date global LUC maps for recent years of reference. The latest projects and initiatives producing global LUC maps for single dates have focused on improving the accuracy of global LUC mapping and the use of crowdsourcing production strategies. The Geo-Wiki Hybrid and GLC-SHARE datasets built on the previous research in a bid to obtain more accurate global LUC maps by merging the data from existing datasets. OSM LULC is an ongoing test project that is trying to produce a global LUC map cheaply, using crowdsourced information provided by the Open Street Maps community.
Project The Department of Geography of the University of Maryland hosted one of the first research groups to use the classification of satellite imagery for global LUC mapping. They initially produced an LUC map at a spatial resolution of 1 degree for the year of reference 1987. This was followed sometime later by the production of a finer map at 8 km for 1984. Finally, the project delivered a map at 1 km, which at that time was the finest resolution at which global LUC mapping had ever been carried out. The Global Land Cover Facility that hosted all this data recently went offline. This means that there is currently no official website that supports the datasets and provides information about their particular specifications. The map at 1 km can however be downloaded from external sites. The earlier maps at coarser resolutions are no longer available.
Practical considerations There is no official website hosting this dataset, which makes it more difficult to access and understand. Users must bear in mind that this was one of the first global LUC datasets ever developed and it can therefore be considered outdated in technical terms. Coarser versions of the 1 km map, resampled at 0. 25, 0. 5 and 1 degree of spatial resolution, are also available. 2 cheme. In this case, however, we will only display the IGBP Land Cover classification scheme (IGBP), because it is the most commonly used of all the schemes provided by the dataset. Information about the codification and the meaning of all the other classification schemes can be found in the technical documentation included in the downloaded product, as well as in the documentation available on the project's website.
Project GLC2000 was a project run by the Joint Research Centre (JRC) of the European Commission in collaboration with regional teams across the globe. The objective of the project was to create a homogeneous, coherent global LUC map that was suitable for environmental monitoring. The reference year 2000 was chosen because of its particular significance for that purpose. One of the most successful aspects of the project was the coordination of different teams across the globe to produce a global LUC map. To this end, GLC2000 provides a global dataset, together with a set of more detailed regional datasets adapted to the specificities of each territory.
Product description GLC2000 consists of two main products: the harmonized global LUC dataset covering the whole earth and the set of detailed regional LUC datasets. The Global LUC map can be downloaded in four different formats (ESRI, Binary, Tiff, Img), whereas the regional maps are only available in two (ESRI, Binary). The product for download includes a file to symbolize the raster LUC map as well as auxiliary information to interpret the legend. -Folder with raster file of the regional LUC map (glc_eu_v2) -Colormap file to symbolize the raster in ArcGIS (. clr) -DBF file with the map legend.
Production method The hybrid map of the Geo-Wiki project was produced by merging three global LUC datasets: GLC2000, GlobCover and MODIS LC. Whereas GLC2000 shows the LUC state of the world for the reference year 2000, the other two sources provide LUC information for the reference year 2005. The spatial resolution of the hybrid map is the same as applied in the dataset with the highest resolution: GlobCover (300 m). The other two datasets, which had a spatial resolution of 1 km, were resampled to fit this resolution. For each dataset, a probability layer was produced indicating the probability of that source representing the correct LUC class on the ground. These layers were obtained by regressing the datasets with validation points created through Geo-Wiki campaigns. A Geographically Weighted Regression (GWR) algorithm was employed to this end.
Project Land Degradation Assessment in Dryland (LADA) is a project led by the Food and Agriculture Organization (FAO) of the United Nations that aims to assess and map land degradation at different scales and levels, so as to understand its impact on land use. As part of the datasets created in the project, a map of the world's Land Use Systems (LUS) was developed. Many other datasets were also created within the framework of this project, which may be of interest to users.
Legend and codification Code Label Although the GLC-SHARE was produced in 2014, it was conceived as a living database that could integrate new LUC datasets as they were released or updated. Its production method has been made public, so enabling product replication. As GLC-SHARE was produced by merging data from multiple databases, it has no specific date of reference. There are different dates for each part of the world, according to the main product that was used to map them.
Production method GLC-SHARE was produced by merging and integrating high-quality LUC data for different areas of the world. LUC data at all scales (global, national, sub-national, regional) was used to produce the map. In order to merge the various LUC datasets into a single product, their legends had to be harmonized. When different products were available for the same area, the one with the most detailed, most accurate data was chosen. If no products were available at detailed or national scales, global LUC datasets (Globcover 2009, MODIS VCF 2010 and Cropland database 2012) were used instead. The main areas not covered by high-resolution datasets included Latin America, West Africa, Indonesia and important parts of Asia, such as Thailand and the Arabian Peninsula. An initial map for each of the 11 land cover classes that make up the classification legend of the GLC-SHARE was obtained.
Product description GLC-SHARE products can be downloaded in raster format or as a kml file to upload in Google Earth or any other GIS software. GLC-SHARE maps are also available through a WMS web service. Users can download the global GLC-SHARE LUC map, which indicates the dominant land cover type in each pixel, or individual LUC rasters showing the proportions of each LUC type in each pixel. In these rasters, the pixel value refers to the proportion (0-100) at which each category is represented in the pixel. A pixel covered exclusively by artificial surfaces would have a value of 100 in the "GLC-Share -Artificial surfaces" raster. Users can also download auxiliary information about the dataset from the website. This includes a technical report about the product (GLC-Share report) as well as a raster and an excel spreadsheet explaining which dataset was used to map each area of the world (GLC-Share-Sources).
Practical considerations GLC-SHARE is a single product with no information about changes in LUC over time. It was created in 2014, which may therefore be considered the reference year for the dataset. However, this date may vary a great deal between the different parts of the world. GLC-SHARE is therefore not recommended for studies or analyses of LUC change. Although the dataset was conceived as a live map, it has not been further updated with the inclusion of new LULC datasets since 2014.
Project OSM Landuse/Landcover (LULC) is a LUC dataset created as part of the H2020 project "LandSense", which aims to engage citizens in the production of LUC information. The OSM LULC has been developed above all by the GIScience research group from Heidelberg University. OSM LULC is an attempt to exploit the LUC information contained in the OpenStreetMaps (OSM) database. It is a test project and therefore cannot be regarded as a final product with full global coverage. Nevertheless, the project has developed a workflow to obtain LUC information from the OSM database as well as a methodology for obtaining an LUC map with full coverage over a specific test area (Heidelberg), filling the gaps in the OSM via classification of satellite imagery.
Production method OSM LULC was produced using a very simple method. Authors downloaded the OSM database and translated the tags that define the features stored in the database into LUC terms (the legend for the Corine Land Cover (CLC) survey was used as a reference). An equivalence table between the OSM tags and the CLC level 2 legend was created. The OSM LUC information, in vector, was generalized in a 30 m pixel side grid. In the event of feature overlap when aggregating information, preference was given to the smaller features. Gap areas not covered by the OSM database were filled with the LUC information obtained by a supervised classification of Landsat imagery with the random forest classifier. This process was only carried out for a European test area, leaving important information gaps in the rest of the global map.
Practical considerations The website for this database includes a form for those who want to download the map. However, interested users are recommended to contact the map producers directly, as the first approach does not always work. Contact details for the map producers are available at the project's website. 6 sers should be aware of the limitations of this dataset. As there is no single reference year for all the mapped areas, it may be difficult to use the map as a reference when analysing changes over time. 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. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Global General Land Use Cover Datasets with a Time Series of Maps David García-Álvarez, Javier Lara Hinojosa, Francisco José Jurado Pérez, and Jaime Quintero Villaraso Abstract General Land Use Cover (LUC) datasets provide a holistic picture of all the land uses and covers on Earth, without focusing specifically on any individual land use category. As opposed to the LUC maps which are only available for one date or year, reviewed in Chap. "Global General Land Use Cover Datasets with a Single Date", the maps with time series allow users to study LUC change over time. Time series of general LUC datasets at a global scale is useful for understanding global patterns of LUC change and their relation with global processes such as climate change or the loss of biodiversity. MCD12Q1, also known as MODIS Land Cover, was the first time series of LUC maps to be produced on a global scale.
When it was first launched in 2002, there were already many organizations and researchers working on accurate, detailed global LUC maps, although these were all one-off editions for single years. The MCD12Q1 dataset continues to be updated today, providing a series of maps for the period 2001-2018. Since the launch of MCD12Q1, many other historical series of LUC maps have been produced, especially in the last decade. This has resulted in the LUC map series covering a longer time period at higher spatial resolution. Recent efforts have focused on producing consistent time series of maps that can track LUC changes over time with low levels of uncertainty. GLCNMO (500 m), GlobCover (300 m) and GLC250 (250 m) provide time series of LUC maps at similar spatial resolutions to MCD12Q1 (500 m), although for fewer reference years.
GLCNMO provides information for the years  2003 , 2008 and 2013 , GlobCover for 2005 and 2009 and GLC250 for 2001 and 2010. GLASS-GLC is the dataset with the coarsest spatial resolution of all those reviewed in this chapter (5 km), even though it was released very recently, in 2020. Map producers have focused on this dataset's long timespan  rather than on its spatial detail. LC-CCI and CGLS-LC100 are the recently launched datasets providing a consistent series of LUC maps, which show LUC changes over time with lower levels of uncertainty. LC-CCI provides LUC information for one of the longest timespans reviewed here (1992-2018) at a spatial resolution of 300 m. CGLS-LC100 provides LUC information for a shorter period (2015-2019) but at a higher spatial resolution (100 m). In both cases, updates are scheduled.
The datasets with the highest levels of spatial detail are FROM-GLC and GLC30. These were produced using highly detailed Landsat imagery, delivering time series of maps at 30 m. The FROM-GLC project even has a test LUC map at a spatial resolution of 10 m from Sentinel-2 imagery for the year 2017, making it the global dataset with the greatest spatial detail of all those reviewed in this book. Both FROM-GLC and GLC30 provide data for three different dates: the former for 2010, 2015 and 2017 and the latter for 2000, 2010 and 2020. The purpose of the project is to deliver a time series of land cover data that is stable, dynamic, transparent and flexible. This means: first, obtaining a historical series of land cover maps that show the changes over time, with no technical errors or instability: second, the production of a LUC dataset with a wide range of applications; and third, the provision of all relevant information regarding the quality of the dataset.
Product description The LC-CCI dataset is distributed in different ways. This gives users the flexibility to download the product that best suits their needs. A single LUC map in either GeoTIFF or NetCDF4 may be downloaded for each year of the period 1992-2015. For the most recent years (2016-2018), these are only available in NetCDF4 format. Additionally, the whole time series of maps for the period 1992-2015 can be downloaded as a single raster with multiple bands, in either of the two formats available. When downloading the LUC maps, users only gain access to the rasters with LUC information. However, other supplementary information is available on the project's website. This includes a CSV file with the legend description; layer style files for displaying the rasters in common GIS software (ArcGIS, ENVI and QGIS); GeoTIFF files with information about the quality and uncertainty of the LUC maps time series (Quality flags); and a data package for users working with the Sen2Cor classification software.
Project GlobeLand30 (GLC30) is a project funded and promoted by the Chinese government and the National Science Foundation of China. It aims to coherently map the land uses and covers on the world's surface at a detailed scale, using images from the Landsat satellite imagery archive. The project initially focused on analysing the best methods and procedures to carry out such an ambitious task. It then produced a global LUC map at 30 m for the reference years 2000 and 2010. An update of the dataset for the year 2020 was recently released, in which Antarctica was mapped for the first time.
Practical considerations The GLC30 LUC maps for 2000, 2010 and 2020 can also be accessed online through the project website,  2  which also includes plenty of information about the project and various other datasets. These include the 2020 imagery used to map the latest update of the dataset and different sources of reference data used as auxiliary information in the mapping process. There are no technical documents describing the latest update of the map for the year 2020. Methodological changes in the production of the map could have been implemented which could lead to errors when comparing with previous editions. The project website is not always maintained. It has been unattended for many months over recent years. If the website is not maintained, it is possible that the dataset may be not accessible in the future.
Production method MCD12Q1 was obtained by means of supervised classification (Random Forests) of MODIS imagery for the period 2001-2020. Once the classification had been obtained for each year, it was adjusted with the aid of auxiliary data: C5 MCD12Q1, C6 MODIS Land Water mask, C5 MODIS Vegetation Continuous Fields (VCF), WorldClim dataset, a global urban layer and global crop type information compiled from census data. As a result of the classification, class probability rasters were obtained for each LUC category. These inform about the probability of each pixel belonging to a specific LUC category. These probability layers provided a base on which to map LUC covers according to six different classification schemes: IGBP, UMD, LAI, BGC, PFT and FAO-LCCS. In order to ensure the consistency of the classification over time, a hidden Markov model (HMM) was applied to the adjusted classification to reduce spurious changes over time.
Production method GlobCover maps were obtained by classifying imagery captured by the MERIS sensor. Urban and wetland areas, which are not well represented, were classified using a supervised classifier. The remaining categories were classified in a series of spectro-temporal classes through an unsupervised classifier. Once classified, the spectro-temporal classes were labelled automatically according to the information provided by the reference datasets. For the 2005 map, the reference datasets were the GLC2000 global LUC map (see Sect. 3 in Chap. "Global General Land Use Cover Datasets with a Single Date" Global General Land Use Cover Datasets with a Single Date) and other high-quality regional LUC maps. For the 2009 map, the GlobCover 2005 map was used as a reference. The area for classification was divided into different regions, to account for the ecological and reflectance diversity of the world.
Product description A zipped file is available for each GlobCover map. It contains the raster layer with the LUC information and all the auxiliary data that users may need to correctly interpret the dataset. This includes the classification legend, technical and data quality information, and files with the layer style of the map to automatically symbolize the raster in GIS software. A complementary raster detailing the source of the LUC information for each pixel (MERIS sensor classification (value = null) or a land cover database (value = 1)) is also provided. In a separate file, users can also download a raster for a coloured version of the LUC map.
Practical considerations The project website, where all the information is stored and available for download, is not user-friendly. It is not easy to find the information the user is looking for. Users may also struggle to download datasets for their area of interest according to latitude and longitude information. When available, we recommend using the kmz file with Google Earth for this purpose. There is little additional information. For a complete description of the characteristics of the different maps, we recommend users to read the scientific papers cited in the introduction to this dataset above (14. 8. Technical Documentation). 7 http://data. ess. tsinghua. edu. cn/.
Production method The Copernicus Global Land Service Dynamic Land Cover map is produced through a multistep processing framework. First, PROBA-V satellite images are pre-processed and merged following a Sentinel-2 tiling grid to create a 3-year epoch mosaic for each reference year. Second, a series of metrics (spectral and textural metrics, descriptive statistics) are extracted from each epoch mosaic. Third, imagery for all the epochs is classified using a regression algorithm, which delivers a cover fraction layer for each basic LUC class and reference year, and a supervised classification algorithm, which delivers a LUC map for each reference year. Various auxiliary data sources are used in the classification phase, i. e. seven different data masks and three extra datasets: biome clusters, water cover fractions and built-up cover fractions.
Practical considerations Because of the large number of datasets available through this project, users are encouraged to make use of the different layers of LUC information available. This will give them a better understanding of the uncertainties and limitations of the product. Users can download the product covering the whole globe, which is distributed through the files in the Zenodo repository. 8  Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4. 0 International License (http:// 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.
Production method The production of CLC is coordinated by the European Environment Agency (EEA). Each participant country is responsible for mapping its own territory according to the general guidelines developed by the EEA. The method of production may vary from country to country. Initially, CLC was mapped at national scales based on the photointerpretation of Landsat imagery. In the following editions, most of the countries decided to stick to this method, using different satellite imagery according to EEA prescriptions: Landsat, SPOT; ITS P6, RapidEye, LISS III, Sentinel. In the latest editions, the production method has varied in some cases. A few countries, like Germany or Spain, produce the CLC database by generalizing national LUC databases at finer scales. This has introduced important changes in the way land uses and covers are mapped over time for these countries.