dataesr/scientific-paragraphs-categorization huggingface dataset:
Platforms and Repositories A few online platforms and repositories provide an overview of the LUC datasets available. The Geo-Wiki platform (www. geo-wiki. org) is one of the most recent. It was initially developed to collect reference LUC information through crowdsourcing and to create a hybrid LUC map. It now hosts both general and thematic LUC maps. The Google Earth Engine Platform, which was also recently launched, includes a repository of spatial datasets, with a specific section devoted to Land Cover data (https://developers. google. com/ earth-engine/datasets/tags/landcover). The FAO Geonetwork repository (www. fao. org/ geonetwork/) makes a great deal of spatial datasets available to users. The repository includes a specific section on LUC data. It hosts LUC maps at all scales and is a valuable source of LUC information for developing countries.
The Land Processes Distributed Active Archive Center (LP DAAC) (https:// lpdaac. usgs. gov/) holds most of the LUC datasets produced by NASA and the United States Geological Survey (USGS), in addition to other important global datasets. The Copernicus Land Monitoring System website (https://land. copernicus. eu/) is the main source of LUC products created through the Copernicus programme, and is of particular interest for those working with European LUC information. All Copernicus layers are also available through the WEkEO Copernicus DIAS service (https://wekeo. eu/), a cloud-based platform that provides access to Copernicus datasets and to various tools for processing them, including all the land monitoring data.
Global LUC Maps The production of global LUC datasets started at the end of the twentieth century. By then, coarse-resolution satellite imagery was available for producing consistent global LUC datasets at a low cost. A previous attempt had been made to create a global LUC map through photointerpretation of aerial imagery  (Campbell 1983). Some authors also mention the maps developed by  Matthews (1983) ,  Olson et al. (1983)  and  Wilson and Henderson-Sellers (1985) , when reviewing the first global LUC datasets. However, these datasets are quite thematic, focusing particularly on vegetation. They were created by combining existing maps with data obtained in the field and via interpretation of aerial imagery  (Giri 2005). The first global general LUC map of which we have record dates from 1994 (Table  1 )  (Defries and Townshend 1994).
A lot of new maps have been produced since these first global general LUC maps appeared, especially since 2010. Tables  1  and 2  provide a synthetic overview of these efforts. When available, the tables include a reference to the section of this book where these datasets are described in detail. For the datasets providing a time series of maps, we also specify to what extent LUC changes can be studied over the series of maps without important sources of uncertainty. As in the case of the pioneering maps from the University of Maryland, all the datasets reviewed here have been Most of these datasets are intended for use in climate change modelling, for which coherent global LUC maps at coarse resolutions are required. However, these databases are becoming increasingly popular and are used for many other purposes, a lot of them related with land change.
The Production Methods Nowadays, global LUC maps are created using improved and innovative production methods, involving advanced classifiers, such as those based on machine learning, as well as a lot of auxiliary data. In many cases, specific LUC categories are mapped through several specific procedures due to their particular patterns, reflectance behaviour, etc. Additional post-classification treatments have also become common in a bid to avoid some of the uncertainties and errors associated with the production of these maps. In recent years, due to the increasing availability of LUC datasets, more and more global LUC maps are being produced by data fusion, in which new maps are created by combining existing datasets using a range of different algorithms and approaches. The aim of these projects is to create datasets with higher levels of accuracy and, therefore, less uncertainty.
To this end, they usually combine the most accurate or highest quality LUC information from each dataset. FAO-GLCShare is perhaps the best-known example of an attempt to build a new global LUC map from data fusion. It was created in 2014 by merging high-quality detailed national and regional LUC databases  (Latham et al. 2014). In many cases, the new maps were obtained from the fusion of existing LUC datasets at global scales. Geo-Wiki Hybrid  (See et al. 2015)  is one of the most famous examples of maps created using this approach. LUC maps obtained from data fusion do not have a single specific date of reference for the mapped area. When first produced, they are considered as up-to-date LUC databases. However, if they are not updated frequently, they eventually become obsolete and can no longer be regarded as useful sources for LUC change analysis.
Accuracy The development and application of new methods and techniques to produce LUC maps has not improved the accuracy of these datasets. Although some global LUC maps are more accurate than others, there is no correlation between time, the introduction of new methods and techniques and the achievement of higher levels of accuracy  (Yu et al. 2014). Global LUC datasets usually have accuracy levels of over 60%. In the best cases, they are around 80%. They are therefore still subject to high degrees of uncertainty. This is to be expected given the high level of abstraction they require. The entire surface of the Earth is being mapped according to the same method and must fit into the same legend. This means there is little room for local or regional specificities, which inevitably introduces a degree of uncertainty.
Temporal Resolution The temporal resolution of LUC maps has also increased over time, especially in recent years. Historical time series of LUC maps are becoming more common (Table  2 ). When-MODIS Land Cover (MCD12Q1) was launched in 2002, it was the first global LUC dataset to provide a series of LUC maps for different years  (Friedl et al. 2002). It was later joined by GLCNMO, GlobCover, FROM-GLC and GLC30, which all provided new series of LUC maps for at least two different points in time. However, in most of these series, LUC change cannot be reliably detected by cross-tabulating the different maps that make up the dataset. Different methods of production for each year, changes in the source of imagery, differences in the reflectance of the images, etc. , introduce a lot of noise in the comparison.
This makes it impossible to obtain meaningful results from LUC change analyses. The latest version of the MODIS Land Cover (Collection 6) incorporated important changes in the product algorithm and workflow to account for these sources of uncertainty  (Sulla-Menashe et al. 2019 ). However, change detection is still not supported and is therefore not recommended. New time series of LUC maps have been produced recently with the specific purpose of enabling change detection. These include the LC-CCI (ESA 2017) and GLASS-GLC maps  (Liu et al. 2020). They provide a long record of LUC information: with yearly maps for the period 1992-2018 in the case of the LC-CCI, and for the period 1982-2015 in the case of GLASS-GLC. The latter dataset has the longest, most frequent time series currently available.
Classification Schemes Unlike the spatial and temporal resolutions, there are no important variations over time in the thematic resolution of most global LUC products. In fact, standard LUC classification systems are now widely used so as to ensure that the different databases are comparable. One of the most common is the International Geosphere-Biosphere Programme   FAO in 1998 (Di Gregorio and Jansen 1998)  has become the standard LUC classification method today. It is a flexible classification system that can be adapted to LUC maps at different scales and for different areas of the world. It first distinguishes between 8 broad land cover categories, each of which is later disaggregated into a varying number of subcategories based on a series of classifiers, which define the attributes or characteristics of each land cover.
Supra-national LUC Maps A lot of international institutions and organizations need comprehensive and coherent worldwide data to support their activities. Global datasets are also required by research communities that study the whole Earth as a system. For their part, national governments and organizations require large amounts of data to support policymaking at a national level. Many other institutions, associations, professionals and researchers need very detailed data that is only available at regional and local scales. Within this context, supra-national datasets do not provide much detail and work at a different scale to that at which most institutions and organizations implement their policies. They therefore do not meet the requirements of the research and policy-making communities working at global scales.
This means that there is less interest and consequently less funding for datasets at these scales, hence the relative lack of supra-national LUC maps. Supra-national LUC maps have been developed by the European institutions to assist policymaking and environmental monitoring in Europe. In other continents, supra-national LUC maps are usually developed within the context of different projects funded by international institutions, such as the FAO and various different US and European institutions. The latter include the European Space Agency (ESA) and the Joint Research Centre (JRC) of the European Commission, which have been actively involved in the production of supra-national LUC maps for many developing areas with important biodiversity values.
Europe Europe is the continent with the widest range of supra-national LUC maps. The European Union (EU) has certain powers over the European environment and is therefore interested in monitoring any changes in land use. To this end, the EU has invested in the production of EU-wide reference data as a reliable source of information on which to base their policy decisions. As a consequence, plenty of detailed, high-quality datasets are now available providing LUC information for the European continent (Table  3 ). The quality and detail of these datasets reveal the large amount of resources that the EU has invested in land monitoring, especially in recent years via the Copernicus programme. Of all the European LUC datasets, CORINE Land Cover (CLC) is by far the best known. It is one of the oldest and most successful programmes on land monitoring, offering very high levels of accuracy and detail.
All these qualities have made CLC a reference in LUC mapping worldwide. It is the only cross-country initiative working at similar scales that provides detailed, temporally rich LUC data, which can be used effectively for change detection. CLC is one of the best examples of decentralized, coordinated LUC mapping. CLC is produced at a national level, which allows European countries to develop their own national datasets while taking advantage of the work and the resources invested to create CLC. A few non-European countries have mapped the land uses and covers in their entire nations or in certain specific areas following the CLC model. Some of them have done so with the help of the European institutions and other European research groups. These include Palestine, Morocco, Tunisia, San Salvador, Guatemala, Honduras, Haiti, Dominican Republic, Colombia, Burkina Faso and Gabon  (Jaffrain 2011 ).
Two other series of LUC maps, which are complementary to CLC, are also available for Europe. Annual Land Cover is a recently launched product that provides annual LUC maps, so overcoming the temporal resolution limitations of CLC, which is only updated once every 6 years. Annual Land Cover is produced as part of a project funded by the European Commission, which aims to create harmonized spatial datasets for Europe. However, it is not recommended for change detection, as there is a lot of inter-annual variability between LUC covers. HILDA is another LUC dataset providing a long time series of LUC maps for Europe. Although it has a coarser resolution, it provides the longest time series of maps reviewed here: 1900-2010. It was produced by a research project team, who combined various different datasets and applied complex modelling techniques  (Fuchs et al.
Africa A large number of supra-national LUC maps have also been found for Africa (Table  4 ). Most of the datasets cover specific regions of the continent, such as Eastern, Western or Southern Africa. Areas that are particularly relevant for environmental research, such as the Congo Basin, have also been mapped. Only a few projects tried to offer an overview of the LUC covers for the entire African continent. The FAO mapped the covers for many African countries as part of the AFRI-COVER project, but did not encompass the whole continent. The first comprehensive, Africa-specific, general LUC dataset only appeared quite recently. It was produced by EU research and earth-observation organizations. No similar initiatives have been found for America, Asia and Oceania. They are also quite rare for Europe as a whole, where continental LUC data usually covers the EU and associated countries.
The Americas In the Americas, there is a clear distinction between the datasets covering North America and those covering South America and the Caribbean (Table  5 ). For North America, the North American Land Change Monitoring System (NALCMS) is of particular interest. It provides LUC maps for Canada, Mexico and the USA at three points in time. It is the only LUC supra-national American dataset with a time series of LUC maps. The NALCMS maps are created by merging datasets produced individually for each participating country following a similar approach. Three different maps have been produced for South America, including in some cases the Caribbean. These were the result of various different research projects and activities and two of them (SERENA and South America 30 m) are no longer accessible for use.
Asia and Antarctica We only found one supra-national dataset for Asia, which covered the LUC of the Himalayan region (Table  6 ). It is possible that other supra-national datasets are available, although language barriers would prevent us from reviewing them properly. In any case, China is the most advanced country in Asia in terms of LUC mapping, and its research is focused above all on global and national mapping projects. No supra-national maps are available for Oceania, due to its particular characteristics in which continental areas and islands are usually separate individual nations. These countries have no shared continental or inland regions for which a supra-national LUC dataset might be useful. As a result, no datasets of this kind have been produced. Finally, a specific LUC map for Antarctica was produced recently by Chinese researchers  (Hui et al.
Thematic Land Use Cover Datasets Thematic Land Use Cover (LUC) datasets map parts of the Earth's surface as a specific land cover, considering not just its extent but also its intensity of distribution. They normally focus on land covers and provide very little information about land use. Thematic LUC maps are usually produced using automatic remote sensing techniques that find accurate land use characterization difficult. Thematic LUC maps usually represent land covers in greater detail than general LUC maps. Some provide information about the proportion of the study area occupied by a particular land cover on the ground. In other cases, they delineate the extent of a specific cover with great detail and accuracy. Other thematic LUC maps share certain features with general LUC maps, in that they map the Earth according to a set of predefined categories, which are usually subclasses of a specific type of cover (e.
g. vegetation). Many maps charting vegetation in its various different forms can therefore be regarded as thematic sources of LUC information in that they characterize a specific cover. Some maps may provide thematic information about specific land covers together with other relevant data. This was especially true in the twentieth century, when many different maps combining biogeographic and climate information were produced for the climate and other research communities. These maps were usually produced by merging different techniques and datasets. Examples include the maps produced by  Matthews (1983)  and  Olson et al. (1983). As these maps are now outdated and were not focused exclusively on land cover, we decided not to include them in this review. Prior to the advent of satellite remote sensing, there were also a large number of traditional maps obtained through photointerpretation of aerial imagery and field surveys that provided information on certain specific land covers.
These maps charted vegetation above all and, to a lesser extent, agricultural areas. These can be useful sources of information for historical LUC change analysis. However, as they are usually only available for national or more detailed areas and in many cases have not been digitalized, they are not reviewed here either. There are also plenty of other spatial datasets that provide useful information for studying specific land covers. One example for vegetation covers are maps of live biomass  (Kindermann et al. 2008; Thurner et al. 2014). Accordingly, there is a huge supply of information that can be used to study and characterize land covers, which comes in datasets of many different kinds. In this review, however, we will only be analysing datasets with a pure land cover approach. The fact that thematic LUC maps focus on a single, specific cover normally means they are more accurate than general LUC maps.
They are often more detailed too. This makes them especially useful for uncertainty analysis and validation exercises. As a general rule, they are a good source of reference data for studying land covers in a particular study area. However, they may not be as easy to use or to process as general LUC maps. If they provide too much information, users will have to process it to meet the specific needs of their studies. The progress made in recent decades in the production of general LUC maps has also been achieved in thematic LUC mapping, with increasing levels of detail and more innovative, more complex methods. Some of the newest products have been produced using the cloud-computing capabilities of Google Earth Engine, which seems likely to play a key role in thematic LUC mapping in the future, and will allow more thematic datasets to be produced.
Global Thematic LUC Maps Focusing on Vegetation Covers One of the most common features mapped by thematic LUC products is natural vegetation and tree and forest covers in particular. In fact, forest monitoring is one of the main applications of Landsat data, as reviewed by  Hansen and Loveland (2012). This is because of widespread scientific interest in the study of vegetation dynamics and the fact that remote sensing techniques have made it much easier to characterize vegetation covers. LUC maps focusing on vegetation covers usually offer coherent time series of LUC data that support change detection (Table  7 ). The most popular include the Vegetation Continuous Fields (VCF) datasets produced by NASA. These were first produced at the beginning of the 2000s and were obtained from AVHRR data at 1 km  (Hansen et al.
2017). Since then, more VCF datasets have been produced at increasing levels of spatial detail, based above all on imagery from MODIS and Landsat  (Hansen et al. 2003; Sexton et al. 2013). The temporal resolution of these products has also improved, with FCover providing information every 10 days for the period 1999-2020. VCF datasets provide information about the vegetation cover fraction for each pixel in the analysed area. FCover is the only dataset that provides information on the percentage of vegetation cover, whereas all the others focus on tree or forest covers. Whereas FCover considers all kinds of natural vegetation, MEaSUREs VCF (VCF5KYR), MODIS VCF (MOD44B), Landsat VCF (GFCC) and the Hansen Forest Map focus exclusively on tree covers. In addition, GFCC and Hansen Forest Map include specific layers of forest change.
Global Thematic LUC Maps Focusing on Agricultural Covers Agricultural areas are also widely mapped with specific LUC products (Table  8 ). Thematic agricultural LUC datasets usually show the extent of croplands and pasturelands or the cover fraction per unit of analysis, i. e. per pixel. In some cases, very detailed information on different types of crops is provided. These detailed LUC datasets are obtained from a wealth of detailed auxiliary information, as it is very difficult to accurately differentiate crop covers using standard remote sensing techniques. Unlike other LUC thematic products, those mapping agricultural areas do not usually offer a time series, which means they cannot be used for land change analysis. Mapping agricultural areas is quite complex and this has hindered the production of coherent time series of agricultural LUC maps.
Global Thematic LUC Maps Focusing on Artificial Covers Built-up areas are becoming a common subject for thematic LUC products. As with the datasets focusing on vegetation covers, they provide time series of data which support change detection (Table  9 ). However, many of these maps are binary maps that only differentiate between urban/impervious and non-urban/non-impervious surfaces. They do not provide information about specific land uses so limiting their utility. However, people working with artificial surfaces are more interested in land use than in land cover, as artificial areas can be used for many different purposes, each of which has a different impact on the Earth. 3. 3. 4 Global Thematic LUC Maps Focusing on Water and Other Covers Some thematic LUC products focus specifically on water covers, two of which provide information on their change over time (Table  10 ).
Other products offer a hybrid between general and thematic LUC datasets. These include the Global 1-km Consensus Land Cover, which provides a LUC thematic map for 12 different land covers  (Tuanmu and Jetz 2014). It has 12 layers, each of which contains information about the fraction of the pixel occupied by the cover being mapped. A thematic LUC dataset with a similar approach was obtained for 13 different covers as part of the ClimAfrica project for the period  1901 -2017 (Churkina et al. 2009)). Like other similar datasets already reviewed, it was obtained by a model based on different sources of historical LUC information.
Supra-national Thematic LUC Maps We have only reviewed a few experiences of supra-national thematic LUC mapping (Table  11 ). The majority of them map vegetation covers, focusing especially on areas of special biodiversity or environmental value. 1980, 1990, 1995, 2000, 2005, 2010  They are usually produced by international institutions, such as the European Commission, or research groups from internationally renowned universities. They are interested in monitoring and understanding the land dynamism of high biodiversity areas of worldwide importance. The European Commission, through the Copernicus programme, is behind some of the few supra-national thematic LUC datasets that focus on other covers such as artificial surfaces or agricultural areas.