Philippa Kaur

Study areaSituated in western India’s southwestern part of the Gujarat state, the Saurashtra region typically represents the semi-arid Gujarat-Rajputana province 4B23, which covers 11 out of 33 districts of the state. The region forms a rocky tableland (altitude 300–600 m) fringed by coastal plains with an undulating central plain broken by hills and dissected by various rivers that flow in all directions24. With the longest coastline (~ 1600 km) in India, Gujarat is endowed with rich coastal biodiversity25,26. The Saurashtra coast in Gujarat is encircled by the open sea between two Gulfs (68° 58′–71° 30′ N and 22° 15′–20° 50′ E) and divided into two segments, viz. the southwestern coast from Dwarka to Diu (~ 300 km stretch) and south-eastern coast from Diu to Bhavnagar (~ 250 km stretch)26.The Asiatic Lion Landscape covers an area of ~ 30,000 km2 (permanent lion distribution range: ~ 16,000 km2; visitation record range: ~ 14,000 km2) of varied habitat types within Saurashtra. The landscape includes five protected areas (Gir National Park, Gir Wildlife Sanctuary, Paniya Wildlife Sanctuary, Mitiyala Wildlife Sanctuary, and Girnar Wildlife Sanctuary) and other forest classes (reserved forests, protected forests, and unclassed forests).The coastal habitats extend across the districts of Bhavnagar, Amreli, Gir-Somnath, and Junagadh (Fig. 1). Within these districts (Fig. 1), the tehsils (sub-divisions/taluka) of Mangrol, Malia, Patan-Veraval, Sutrapada, Kodinar and Una are categorized under the southwestern coast (hereafter western coastal habitat), Jafrabad, Rajula, form the south-eastern coast and Mahuva and Talaja constitute the Bhavnagar coast and represent distinct lion range units (Fig. 1). The total area covered in the study is 2843 km2 on the eastern coast and 1413 km2 on the western coast (Fig. 1).The Saurashtra region is bestowed with three distinct seasons, viz. dry and hot summer (March–June), monsoon (July–October), and primarily dry winter (November–February). It receives a mean annual rainfall of ~ 600 mm, with most rainfall during the southwest monsoon27. The mean maximum and minimum temperatures are 34 °C and 19 °C, respectively28. There is a 110 km2 stretch of forests along the coast. The rest of the areas are multi-use consisting of private, industrial, pastoral and wastelands of varied ownerships. The natural vegetation primarily consists of Prosopis juliflora and Casuarina equistsetifolia. On the beach and dune areas, vegetation such as Ipomea pescaprae, Sporobolus trinules, Fimrystylis sp., Crotalaria sp., and Euphorbia nivuleria29. The mudflats along the coast are restricted to Talaja, Mahuva, Pipavav Port, Jafrabad creek, and Porbandar, sparsely covered by the Avicennia marina29. Fisheries, agriculture, horticulture, livestock rearing, and some large- and small-scale industries are the leading economies in the coastal belt.Coastal segments are characterized by the variety of vegetation, sandy beaches, small cliffs, wave-cut platforms, open and submerged dunes, minor estuaries, embankments, and transition from the open sea to gulf environment with tidal mud26,29 and also support a diverse assemblage of biodiversity25. This biodiversity is further enriched by several perennial/ephemeral rivers originating from the Gir PA (Shetrunji, Machundari, Raval, Ardak, Bhuvatirth, Shinghoda, Hiran, Saraswati, etc.)12. These rivers meet the sea at different sections of the coast, forming prominent coastal ecosystems25. The riverine tracts act as important corridors for wildlife movement9,12,30. Dispersing through these corridors, lions have started inhabiting these coastal habitats30,31.MethodsAll the research activities involved in this study on Asiatic lions were carried out after taking due permission from the Ministry of Environment, Forests & Climate Change (MoEF&CC), Government of India (Letter No.: F. No. 1-50/2018 WL) and Principal Chief Conservator of Forests (Wildlife) & Chief Wildlife Warden, Gujarat State, Gandhinagar (Letter No.: WLP 26B 781-83/2019-20). Procedures and protocols were followed as per the Standard Operating Procedures of the Gujarat Forest Department, Government of Gujarat, concerning the handling of wild animals. Qualified and experienced veterinarians and their team carried out all procedures related to radio-collaring. Moreover, the study is reported in accordance with ‘Animal Research: Reporting of In Vivo Experiments’ (ARRIVE) guidelines as applicable.A long-term lion monitoring project was initiated in 2019 by the Gujarat Forest Department to understand the movement patterns and ecology of lions in the Asiatic Lion Landscape. Looking at the heterogeneity and vastness of the coastal areas, ten individuals were carefully selected for satellite radio-collaring based on their frequent movement in different coastal habitats and monitored from 2019 to 2021.The lions were deployed with Vertex Plus GPS Collars (Vectronics Aerospace GmbH, Berlin, Germany) that weighed less than three per cent of the individual’s body weight, irrespective of age and sex. The lions were immobilized using a combination of Ketamine hydrochloride (2.2 mg per kg body weight; Ketamine, Biowet, Pulawy) and Xylazine hydrochloride (1.1 mg per kg body weight; Xylaxil, Brilliant Bio Pharma Pvt. Ltd., Telangana)32 administered intramuscularly using a gas-powered Telinject™ G.U.T 50 (Telinject Inc., Dudenhofen, Germany) dart delivery system. A blindfold was placed to protect the eyes and decrease visual stimuli33,34. Each sedated individual was sexed, aged, and measured as per the standard operating procedure (SOP) of the Gujarat Forest Department, Government of Gujarat, and recorded the data in the trapping datasheet. The radio-collars were deployed considering the neck girth of the individual, ensuring free movement of it so as not to hamper the individual’s routine activities. After deploying the radio-collar, we used the specific antidote for Xylazine, i.e., Yohimbine hydrochloride (0.1–0.15 mg per kg body weight; Yohimbe, Equimed, USA) intravenously, resulting in the total recovery of immobilized individuals32 within 5–10 min. The individuals were intensively monitored for 72 h and, after that, regularly monitored throughout the functional period of the radio-collars. The entire radio-collaring exercise was carried out by trained and experienced veterinary officers and their teams that constituted wildlife health care personnel and field staff.Each collar had a unique VHF and UHF frequency. The radio-collars were equipped with a programmable GPS schedule and configured to record the location fixes at every hour and provided the data through the constellation of low-earth-orbit Iridium satellite data service (Iridium Communications Inc., Virginia, USA) at four-hour intervals after getting activated. The data logs included location fixes in degree decimal format (latitude/longitude), speed (km/hour), altitude (meters above mean sea level), UTC timestamp (dd-mm-yyyy h:m:s), direction (degrees), and temperature (Celsius). Radio-collars were equipped with mortality sensors and a programmable drop-off activation system. Gir Hi-Tech Monitoring Unit, Sasan-Gir, Gujarat, monitored and coordinated these activities. The location data from each radio-collar was downloaded using the GPS Plus X software (Vectronics Aerospace GmbH, Berlin, Germany) in the Gir Hi-Tech Monitoring Unit (a technology-driven scientific monitoring initiative in the landscape established in 2019 at Sasan-Gir, Gujarat).Data analysisIn this study, we calculated the home range of lions resident in the coastal region using the Fixed Kernel method. We expressed them as 90% and 50% Fixed Kernel (FK) to summarize the overall home range and core area, respectively35,36,37. Additionally, the home range of lions categorized as “link lions” and lions of the protected area was summarized for comparison (Table 1).MaxEnt (version 3.4.1) stand-alone software38 was applied for fine-scaled lion distribution modelling39,40. The logistic output format was set for the MaxEnt output. 30% random lion occurrence points were used as test data to evaluate model performance. The area under the receiver operating characteristic curve (AUC) was used to evaluate the discriminative ability of the model based on the values of sensitivity (correct discrimination of true positive location points) and specificity (correct discrimination of true negative absence points)41. The Jackknife regularised training gain for the species was used to understand the effect of each variable in model building. The logical output by the MaxEnt was presented in a table format as “percent contribution” and “permutation importance” values (from 0 to 100%). Spatial inputs were prepared on the GIS platform using ArcMap (version 10.8.1, ESRI, Redlands, USA)42. Input data for MaxEnt were categorized as (i) lion occurrence data, (ii) model variables were prepared as described below:

i.

Occurrence data
At the first level, inconsistent location fixes (records with missing coordinates, time stamps, and elevation) and outliers were filtered out. Next, each lion’s hourly GPS location fixes obtained from remotely monitored radio-telemetry data were randomized to overcome spatial and temporal biases. The data was reduced by taking every three-hour location fix43,44. The data was further categorized season-wise, viz. summer, monsoon and winter. This consolidated data was then subject to spatial thinning of one kilometre using SDMtoolbox (version 2.0)45,46.

ii.

Model variables

The variables used for distribution modelling broadly included different categories of land use, including both natural habitats and anthropogenic factors, namely, roads and human settlement areas. All variables were rasterized at 10 m spatial resolution.Land Use Land Cover (LULC) data of Saurashtra was obtained from Bhaskaracharya National Institute for Space Applications and Geo-informatics (BISAG-N), Gandhinagar, Gujarat. The data was then further classified into 18 sub-classes—Forest, Sandy areas, Salt-affected, Saltpan, open scrub, dense scrub (Wastelands), Waterlogged, River/Stream/Drain, Lakes and Ponds, Mining/Industrial areas, Reservoir/Tanks, Mangrove/Swamp Area, Crop Land, Agriculture Plantation (horticulture and agro-forestry), Core urban, Mixed settlement, Peri-urban, Village (Fig. 2).Roads and highways were also analyzed as separate variables in the model. Roads were classified as village roads, major district roads, and state and national highways and digitized individually to estimate Euclidean distance further (Table 2). Euclidean distance from the human settlement (Core-urban, Peri-urban, villages and mixed settlement) was analyzed and taken as a separate input variable for the model. More

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Urbanization is fast progressing in the Global South, requiring new solutions for infrastructure, services, industrial development and land and energy use for these regions. In this context, fast-growing cities in Africa can take on a leadership role in driving climate change mitigation and adaptation, disaster risk reduction and sustainable development.
Credit: Stefan Rotter / Alamy Stock PhotoCities in Africa and elsewhere in the Global South continue to grapple with the challenge of delivering equitable services, infrastructure, housing and action to respond to climate change extremes and disasters. One well-known problem is a mismatch between the pace of urban growth and the slower development of basic services and critical infrastructure. This results in, for example, deficient sanitation, water supply systems and localized waste management for large parts of the population, which in turn contribute substantially to heightened poverty and inequality. For inclusive, equitable, prosperous and climate-resilient cities, urban management needs to integrate low-income communities into the urban economy by ensuring access to water, sanitation, energy transition, waste management, poverty reduction and by improving resilience through innovative solutions.
Credit: Patrick J. Endres/Corbis Documentary/GettySuch an equitable urban transition requires changes in the urban infrastructure, and land and energy use, as well as water and ecosystem management. The key research question in this field is to find ways to ensure city-wide access to infrastructure and services, while minimizing emissions and resource use, and building resilience to climate change impacts. In this regard, cities in the Global South and Africa in particular can serve as examples for other parts of the world as they have the potential to adopt disruptive, innovative yet practical solutions to low emissions, resource minimization and resilience building.
Credit: Nature Picture Library / Alamy Stock PhotoFor example, rapid urbanization creates the opportunity to develop economic structures in African cities that strongly integrate waste by promoting recovery, recycling, re-use and repair for lengthening lifecycles. Such a circular economy can create business opportunities, while also reducing resource use, thus creating a pathway for sustainable development. Another potential solution is hybrid systems for urban water management that are off-grid and utilize multiple water sources and treatment but that can also connect to centralized water systems. Business models for micro-to-medium enterprises have the potential to integrate some of the low-income groups through these kinds of technology and building social resilience.
Credit: Images of Africa Photobank / Alamy Stock PhotoThese examples are part of a broader assessment of urban infrastructure innovations, their disruption of centralized systems and rethinking of urban form for more compact, walkable, co-located land use for low carbon intensity towards net-zero cities. However, to translate research on these new solutions into action, a shift is necessary in the planning, governing and managing of cities so as to allow for opportunities for leapfrogging to emerge and expand the possibilities of urban development for inclusive and resilient African cities. More

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To build Sentinel2GlobalLULC, we followed two main steps. First, we established a spatio-temporal consensus between 15 global LULC products for 29 LULC classes. Then, we extracted the maximum number of Sentinel-2 RGB images representing each class. Each image is a tile that has 224 × 224 pixels at 10 × 10 m spatial resolution and was built as a cloud-free composite from all the Sentinel-2 images acquired between June 2015 and October 2020. Both tasks were implemented using GEE, an efficient programming, processing and visualisation platform that allowed us to have free manipulation and access to all used LULC products and Sentinel-2 imagery, simultaneously.Finding spatio-temporal agreement across 15 global LULC productsTo establish the spatio-temporal consensus between different LULC products for each one of the 29 LULC classes, we followed four steps: (1)Identification of the LULC products to be used in the consensus, (2)Standardization and harmonization of the LULC legend that was subsequently used to annotate the image tiles, (3)Spatio-temporal aggregation across LULC products, and (4)Spatial reprojection and tile selection based on optimized spatial purity thresholds.Global LULC products selectionThe adopted purity measure for spatio-temporal agreement across the 15 global LULC products we selected from GEE (Table 2) aims to find areas of high consensus to maximize the annotation quality. Spatial and temporal consensus across such rich diversity of LULC products, in terms of spatial resolution, time coverage, satellite source, LULC classes and accuracy, was used as a source of robustness for our subsequent LULC annotation. Products outside GEE were not used due to computing limitations.Table 2 Main characteristics of the 15 global Land-Use and Land-Cover (LULC) products available in Google Earth Engine (GEE) that were combined to find consensus in the global distribution of 29 main LULC classes.Full size tableStandardization and Harmonization of LULC legendsLand cover (LC) data describes the main type of natural ecosystem that occupies an area; either by vegetation types such as shrublands, grasslands and forests, or by other biophysical classes such as permanent snow, bare land and water bodies. Land use (LU) includes the way in which humans modify or exploit an area, such as urban areas or agricultural fields.To build our 29 LULC classes nomenclature, we established a standardization and harmonization approach based on expert knowledge. During this process, we took into account both the needs of different practitioners in the global and regional LULC mapping field and the thematic resolution of the global LULC legends available in GEE. Our nomenclature consists of 23 LC and 6 LU distinct classes identified through specific consensus rules across 15 LULC products (see Table 4). A six-level (L0 to L5) hierarchical structure was adopted in the creation of these 29 LULC classes (Fig. 2). To facilitate the inter-operability of our 29 legends at the finest level L5 across all LULC products and with the widely used FAO’s hierarchical Land Cover Classification System (LCCS)1, we have established an LULC classification system where the 29 classes can be mapped directly to FAO’s LCCS as explained in the table of Supplementary File 1. The LC part in our dataset contains 20 terrestrial ecosystems and 3 aquatic ecosystems. The terrestrial systems are: Barren lands, Grasslands, Permanent snow, Moss and Lichen lands, Close shrublands, Open shrublands, in addition to 12 Forests classes that differed in their tree cover, phenology, and leaf type. The aquatic classes are: Marine water bodies, Continental water bodies, and Wetlands; furthermore, wetlands were divided into 3 classes: Marshlands, Mangroves and Swamps. The LU part is composed of urban areas and 5 coarse cropland types that differed in their irrigation regime and leaf type. In Table 3, you can find the semantic definition of each one of the 29 classes in Sentinel2GlobalLULC. We provided a table in Supplementary File 2, for a more detailed definition of each LULC class.Fig. 2Tree representation of the six-level (L0 to L5) hierarchical structure of the Land-Use and Land-Cover (LULC) classes contained in the Sentinel2GlobalLULC dataset. Outter circular leafs represent the final or most detailed 29 LULC classes (C1 to C29) of level L5. The followed path to define each class is represented through inner ellipses that contain the names of intermediate classes at different levels between the division of the Earth’s surface (square) into LU and LC (level L0) and the final class circle (level L5). All LULC classes belong to three levels at least, except the 12 forest classes that belong to L5 only.Full size imageTable 3 Semantic signification of each one of the 29 Land Use and Land Cover (LULC) classes contained in the Sentinel2GlobalLULC dataset according to the six-level (L0 to L5) hierarchical structure.Full size tableCombining products across time and spaceFor each one of the 29 LULC classes, we combined in space and time the global LULC information among the 15 GEE LULC products. This way, each image was annotated with a LULC class only if all combined products agreed in its corresponding tile (i.e., 100% of agreement in space and time). For each product and LULC type, we first set one or more criteria to create a global mask at the native resolution of the product in which each pixel was classified as 1 or 0 depending on whether it met the criteria for belonging to that LULC type or not, respectively (see first stage in Table 4). For certain LULC classes, some products did not provide any relevant information, so they were not used. For example (Table 4), in Grasslands (C3), Open Shrublands (C4) and Close Shrublands (C5), we combined 14 products, while in UrbanBlUpArea (C29) and Permanent Snow (C23) we only combined 10 and 7 products, respectively.Table 4 First stage of the rule set criteria used to find consensus across the 15 Land-Use and Land-Cover (LULC) products available in Google Earth Engine (GEE) for each of the 29 LULC classes contained in the Sentinel2GlobalLULC dataset.Full size tableThen (see second stage in Table 5), for each LULC type, we calculated the average of all the masks obtained from each product to create a final global probability map from all products with values ranging between 0 and 1. Value 1 meant that all products agreed to assign that pixel to a particular LULC class, while 0 meant that none of the products assigned it to that particular class (Fig. 3). These 0-to-1 values are interpreted as the spatio-temporal purity level of each pixel to belong to a particular LULC class and are provided as metadata with each image.Table 5 Second stage of the rule set criteria used to find consensus across the 15 Land-Use and Land-Cover (LULC) products available in Google Earth Engine (GEE) for each of the 29 LULC classes contained in the Sentinel2GlobalLULC dataset.Full size tableFig. 3Example of the process of building the final global probability map for one of the 29 Land-Use and Land-Cover (LULC) classes (e.g. C1: “Barren”) by means of spatio-temporal agreement of the 15 LULC products available in Google Earth Engine (GEE). The final map is normalized to values between 0 (white, i.e., areas with no presence of C1 in any product) and 1 (black spots, i.e., areas containing or compatible with the presence of C1 in all 15 products), whereas the shades of grey corresponds to the values in between (i.e., areas that did not contain or were not compatible with the presence of C1 in some of the products). This process is divided into two stages: the first stage (the blue part, see details in Table 4) and the second stage (the yellow part, see details in Table 5). LULC products available for several years are represented with superposed rectangles, while single year products are represented with single rectangles. GMP: global probability map, NA: Not Available.Full size imageAs an example of the first stage (see details in Table 4), to specify if a given pixel belongs to Dense Evergreen Needleleaf Forest, we evaluated its tree cover level using “ ≤ “ and “ ≥ “, while for bands containing the leaf type information, we used the equal operator “ = “. For the spatio-temporal combination of multiple criteria we have used the following operators: “AND”,“OR” and “ADD”. For example, we combined the tree cover percentage criteria with the leaf type criteria using “AND” to select forest pixels that met both conditions. To combine many years instances of the same product, we used “ADD”, except for product P13, where we used “AND” to identify permanent water areas only. Whenever we used the “ADD” operator, we normalized pixel values afterwards to bring it back to a probability interval between 0 and 1 using the division by the total number of combined years or criteria.In the second stage (see details in Table 5), we combined for each LULC class the 15 global probability maps previously derived from each product to create a final global probability map (Fig. 3). This combination was carried out using various operators such as “ADD”, “MULTIPLY” and “OR”, depending on the LULC type. When “ADD” was used, the final pixel values were normalized by dividing the final addition value of each pixel by the total number of added products. The “MULTIPLY” operator was mostly used at the end, to remove urban areas from non-urban LULC classes, or to remove water from non-water LULCs. The multiplication operator was also adopted to make sure that a certain criteria was respected in the final probability map. For instance, for the swamp class, we multiplied all pixels in the final stage by a water mask where saline water areas have a value of 0 in order to eliminate mangrove from swamp pixels and vice versa. Finally, we used “OR” operator between different water related products to take advantage of the fact that they complement each other in terms of spatial-temporal coverage and accuracy.In GEE, when two products are aggregated using “ADD”, “MULTIPLY” or any other operator, the output is aggregated at the spatial resolution of the product at the left of the operator. Hence, to maintain the finest spatial resolution in the final probability map, we multiplied everything by product P15 and placed it at the left of the final “MULTIPLY” operation (See Table 5). Hence, all the 29 final probability maps were generated at the P15 spatial resolution of 30 m/pixel (except the urban class C29 which maintained the 30 m/pixel resolution of product P14).Re-projection and Selection of purity thresholdSince our objective was finding pure Sentinel-2 image tiles of 224 × 224 10-m pixels representing each LULC class, we reprojected the 30 m/pixel probability maps to 2240 m/pixel using the spatial mean reducer in GEE. That is, each pixel value at 2240 m resolution was computed using the mean over all the 30m-pixel values contained within it. Hence, the resulting pixel values at 2240 m resolution represent the purity level that each Sentinel-2 image tile of 224 × 224 10-m pixels has. We illustrated the reprojection and selection processes in Fig. 4.Fig. 4Example of the workflow to obtain a Sentinel-2 image tile of 2240 × 2240 m for one of the 29 Land-Use and Land-Cover (LULC) classes (e.g. C1: “Barren”). The process starts with the reprojected final global probability map obtained from stage two (Table 5) and ends with its exportation to the repository of a Sentinel-2 image tile of 224 × 224 pixels. The white rectangle is the only one having a probability value of 1 (Recall that the purity threshold used for Barren was 1, i.e., 100%). The black pixels has a null probability value, while the probability values between 0 and 1 are represented in gray scale levels.Full size imageFor each one of the reprojected maps, we defined a pixel value threshold to decide whether a given 2240 × 2240 m tile was representative of each LULC class or not. Since training DL image classification models needs a large number of high quality (both in terms of image quality and annotation quality) image tiles to reach a good accuracy, when the spatial purity of 100% (full agreement across products in all the pixels of the 224 × 224 tile) resulted in a small number of agreement tiles for a particular class, the purity threshold was decreased for that class until the number of tiles was larger than 1000 or further decreased in less abundant classes to a minimum of 75% of purity. The found purity value is always provided as metadata for each image in the dataset, so the user can always restrict its analysis to those image tiles and classes at any desired purity level. Decreasing the purity threshold down to 75% for the less abundant classes (e.g swamp, mangrove, etc.) was a trade-off between maintaining a good data annotation quality and providing a sufficient number of tiles in each class. In Table 6, we present the number of agreement tiles found at different purity thresholds ranging from 75% to 100% for each LULC class. This spatial purity was not further decreased since machine learning image classification models are known to be robust when the target class is spatially dominant in each training image (it occupies more than 60% of the pixels in the scene)42. On the other hand, when the number of pure tiles for a LULC class was too large to be downloaded (i.e., greater than 14000), we applied a selection algorithm as described in the Supplementary File 3, to download a maximum of 14000 spatially representative images. For this, the world was divided into a one-degree squared cell grid. If a cell contained less than 50 image tiles, we selected them all. If it contained more than 50, we applied that automatic maximum geographic distance algorithm that selected images as far from each other as possible in a number proportional to the number of existing images in that cell. The map in Fig. 6 shows the global distribution of the selected 194877 image tiles contained in Sentinel2GlobalLULC and distributed in 29 LULC classes.Table 6 Summary of the varying number of found and eventually selected Sentinel-2 image tiles of 224 × 224 pixels depending on the different consensus level reached across the 15 Land-Use and Land-Cover (LULC) products available in Google Earth Engine (GEE) for each of the 29 LULC classes contained in the Sentinel2GlobalLULC dataset.Full size tableData extractionSentinel2GlobalLULC provides the user with two types of data: Sentinel-2 RGB images (jpeg and geotif versions) and CSV files with associated metadata. In the following subsections, we describe the process for associating metadata, including the Global Human Modification (GHM) index.Global human modification index extractionAs an additional metadata related to the level of human influence in each image, we calculated for each tile in GEE, the spatial mean of the global human modification index for terrestrial lands43, where 0 means no human modification and 1 means complete transformation. Since the original GHM product was mapped at 1 × 1 km resolution, we reprojected it to 2240 × 2240 m using the same reprojection procedure explained in (Re-projection and Selection of purity threshold).CSV files generationOnce the tiles were selected, for each LULC class we listed the image tiles in descendent order of purity. Metadata included: geographical coordinates of each tile centroid, tile purity value, name and ID of the LULC class, and average GHM index for that tile. Then, we used the geographical coordinates of each tile to identify its exact administrative address geolocation. To implement this reverse geo-referencing operation, we used a free request-unlimited python module called reverse_geocoder. This way, we assigned a country code, two levels of administrative departments, and the locality to each tile.For LULC classes that had more than 14000 pure tiles, we have released the coordinates before and after the distance-based selection in case the user wants to download more tiles or use our consensus coordinates for other purposes.Sentinel-2 RGB images exportationAfter extracting all these pieces of information and grouping them into CSV files, we went back to the geographic center coordinates of each tile and used them to extract the corresponding 224 × 224 Sentinel-2 RGB tiles using GEE. Each exported image was identical to the 2240 × 2240 m area covered by its Sentinel-2 tile.We chose “Sentinel-2 MSI (Multi-Spectral Instrument) product” since it is free and publicly available in GEE at the fine resolution of 10 × 10 m. We chose “Level-1C” (i.e., top-of-atmosphere reflectance) since it provides the longest data availability of Sentinel-2 images without any modification of the data. To build RGB images, we extracted the three bands B4, B3 and B2 that correspond to Red, Green and Blue channels, respectively. More bands available in Sentinel-2 or even in Sentinel-1 images can be incorporated in the future to our dataset. However, computational limitations (i.e., the size of the dataset would be impractical) did not allowed us to handle it as a first goal. In addition, the spatial resolution of the images would be heterogeneous across bands.To minimize the inherent noise due to atmospheric conditions (e.g. clouds, aerosols, smoke, etc.) that could affect the satellite RGB images, every image was built as a temporal aggregation of all images gathered by Sentinel-2 satellites between June 2015 and October 2020. During this aggregation, only the highest quality images in the corresponding image collection were considered, as we firstly discarded all image instances where the cloud probability exceeded 20% according to the metadata provided in their corresponding Sentinel-2 collection. Then, we calculated the 25th-percentile value between all remaining images for each reflectance band (R, G, and B), and built the final image with the obtained 25-percentile values in each pixel for its RGB bands. The 25th-percentile choice was adopted giving its suitability in atmospheric noise reduction44,45,46,47,48.Usually, Sentinel-2 MSI product includes true colour images in JPEG2000 format, except for the “Level-1C” collection used here. The three original bands (B4, B3, and B2) required a saturation mapping of their reflectance values into 0–255 RGB digital values. Thus, we mapped the saturation reflectance of 3558 into 255 to obtain true RGB channels with digital values between 0 and 255. The choice of these mapping numbers was taken from the Sentinel-2 true colour image recommendations section of Sentinel user guidelines. Finally, after exporting the selected tiles for each LULC class as “.tif” images, we converted them into “.jpeg” format using a lossless conversion algorithm.Technical implementationTo implement all our methodology steps, we first created a javascript in GEE for each LULC class. Each script is a multi-task javascript where we implemented a switch command to control which task we want to execute (between the spatio-temporal aggregation task, the spatial reprojection and tiles selection task, or the data exportation task). In each one of these scripts, we selected from GEE LULC datasets repository the 15 LULC products used to build the consensus of that LULC class. Each script was responsible of elaborating the spatio-temporal combination of the selected products and generating the final consensus map for that LULC class as described in the subsection “Combining products across time and space”. Then, it exports the final global probability map as an asset into GEE server storage to make its reprojection faster. In the same script, once the consensus map exportation was done, we imported it from the GEE assets storage and reprojected it to 2240 × 2240 m resolution; then, we exported the new reprojected map into GEE assets storage again to make its analysis and processing faster. Afterwards, we imported the reprojected map into the same script and applied different processing tasks. During this processing phase, many purity threshold values were evaluated. Then, we elaborated in this same script the pure tiles identification and their center coordinates exportation into a CSV file. A distinct GEE script was developed to import, reproject and export the global GHM map. The resulted GHM map was saved as an asset too, then imported and used in each one of the 29 LULC multi-task scripts.A python script was developed separately to read the exported CSV files for each LULC class and apply the reverse geo-referencing on their pure tiles coordinates then add the found geolocalization data (country code, locality…etc) to the original CSV files as new columns. Then, another python script was implemented to read the new resulted CSV files with all their added columns (reverse geo-referencing data, GHM data) and use the center coordinates of each pure tile in that class to export first its corresponding Sentinel-2 satellite geotiff image within GEE through the python API. Finally, after downloading all the selected geotiff images from our Google drive, we created another python script to convert these geotiff images into JPEG format. More