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The Cooperative Open Online Landslide Repository (COOLR) contains citizen science data from Landslide Reporter Catalog (LRC), data from NASA’s Global Landslide Catalog (GLC), NASA manually or automatically digitized landslides, and other inventories contributed by the research community. These are sorted into two main layers, "coolr_reports" and "coolr_events". The layers are further differentiated into a point or polygon layer, named accordingly. Within these layers, the user can further query out the manual or automated landslide inventories, events of a certain date, event triggers, and more. Each feature has a “title” in the attribute table with the digitization method, location, and event date (written as YYYY-MM-DD). The citation for each event inventory is located within the attribute table field “citation”. See the methods section for further detail about manual vs automatic landslide detection. The citation for each event inventory is located within the attribute table field “citation”. If you utilize this inventory, please reference each citation of the event(s) used. For example, if you use the entire inventory layer, please reference all the event citations contained within the layer. If you use just the events past a certain date, or another subset of the layer, please query those citations out and reference those. In addition, a CSV containing all citations can be found on the download page: https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?appid=574f26408683485799d02e857e5d9521. Methods We use 'landslide' as a general term to represent all event types. The catalog differentiates between landslide types when the information is available in the ls_type field. Manually digitized landslides (method = Manual): These are hand-digitized landslide points or polygons, created by NASA scientists or contributed by other institutions. They are created on a case-by-case basis, depending on time and resources, or if there is an event where a partner requests it. The ability to hand-digitize depends on the availability of high-resolution cloud-free imagery, ideally before and after the event. Depending on the event size, location, and the satellite overpass, imagery from multiple dates may be used. For points, the scientist tries to identify where the initiation zone of each landslide is using an elevation layer. For polygons, the initiation zone is not differentiated. Automatically digitized landslides (method = Automatic): The open-source Semi-Automatic Landslide Detection (SaLaD) system was created to rapidly map landslides. SALaD uses object-based image analysis and machine learning to detect landslides from optical imagery. To successfully detect landslides, cloud-free imagery from before and after an event is needed, in addition to training data from that region or a region nearby. The raw polygon output of this system can be useful for situational awareness, but it will require manual corrections to weed out false positives for other use cases. Once that is done, initiation points can be created using a DEM. For Further Reference Landslides.nasa.gov Amatya, P., Kirschbaum, D., Stanley, T., and Tanyas, H. (2021). Landslide mapping using object-based image analysis and open source tools. Engineering Geology. 282. doi: 10.1016/j.enggeo.2021.106000. Amatya, P., Kirschbaum, D., and Stanley, T. (2021). Rainfall-induced landslide inventories for Lower Mekong based on Planet imagery and a semi-automatic mapping method. Geoscience Data Journal. doi: 10.1002/gdj3.145. Juang CS, Stanley TA, and Kirschbaum DB (2019) Using citizen science to expand the global map of landslides: Introducing the Cooperative Open Online Landslide Repository (COOLR). PLOS ONE. 14(7): e0218657. doi: 1371/journal.pone.0218657. Kirschbaum, D.B., Stanley, T., & Zhou, Y. (2015). Spatial and temporal analysis of a global landslide catalog. Geomorphology, 249, 4-15. doi:10.1016/j.geomorph.2015.03.016 Kirschbaum, D.B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog for hazard applications: method, results, and limitations. Natural Hazards, 52, 561-575. doi:10.1007/s11069-009-9401-4
Medium resolution thermal data from the Copernicus Contributing Missions (CCM) consist of high-resolution satellite imagery provided by commercial mission operators. These datasets support the Copernicus programme in areas such as land monitoring, emergency response, security, and marine environment. The imagery covers a broad spectral range and is tailored for diverse operational and analytical applications.
Thermal high resolution data from the Copernicus Contributing Missions (CCM) consist of high-resolution satellite imagery provided by commercial mission operators. These datasets support the Copernicus programme in areas such as land monitoring, emergency response, security, and marine environment. The imagery covers a broad spectral range and is tailored for diverse operational and analytical applications.
Optical data from the Copernicus Contributing Missions (CCM) consist of high-resolution satellite imagery provided by commercial mission operators. These datasets support the Copernicus programme in areas such as land monitoring, emergency response, security, and marine environment. The imagery covers a broad spectral range and is tailored for diverse operational and analytical applications.
Medium Resolution Methane Concentration data from the Copernicus Contributing Missions (CCM) consist of high-resolution satellite imagery provided by commercial mission operators. These datasets support the Copernicus programme in areas such as land monitoring, emergency response, security, and marine environment. The imagery covers a broad spectral range and is tailored for diverse operational and analytical applications.
High Resolution Methane Concentration data from the Copernicus Contributing Missions (CCM) consist of high-resolution satellite imagery provided by commercial mission operators. These datasets support the Copernicus programme in areas such as land monitoring, emergency response, security, and marine environment. The imagery covers a broad spectral range and is tailored for diverse operational and analytical applications.
Hyperspectral high resolution data from the Copernicus Contributing Missions (CCM) consist of high-resolution satellite imagery provided by commercial mission operators. These datasets support the Copernicus programme in areas such as land monitoring, emergency response, security, and marine environment. The imagery covers a broad spectral range and is tailored for diverse operational and analytical applications.
Mise à jour : hebdomadaire Dernière mise à jour : 21 Mai 2026 Création de la base : 1994 / Révision de la base : 2007 Source des données : Géorisques Le jeu de données présente les zones sur territoire français exposées aux mouvements de terrain. Les mouvements de terrain recouvrent des formes très diverses. Selon la vitesse de déplacement, deux ensembles de mouvements de terrain peuvent être distingués : les mouvements lents, qui entraînent une déformation progressive des terrains, pas toujours perceptible par l’humain. Ils regroupent principalement les affaissements, les tassements, les glissements, le retrait-gonflement ; les mouvements rapides, qui surviennent de manière brutale et soudaine. Ils regroupent les effondrements, les chutes de pierres et de blocs, les éboulements et les coulées boueuses. Les mouvements de terrain sont les manifestations du déplacement gravitaire de masses de terrain déstabilisées sous l'effet de sollicitations naturelles (fonte des neiges, pluviométrie anormalement forte, séisme, etc) ou anthropiques (terrassement, vibration, déboisement, exploitation de matériaux ou de nappes aquifères, etc.). Les données sources sont fournies en format CSV par Géorisques, une collaboration entre le Ministère de la Transition écologique et de la Cohésion des territoires et le Bureau de Recherches Géologiques et Minières (BRGM). Depuis 1994, le BRGM inventorie tous les événements mouvements de terrain de France. La base Mouvement de Terrain (BDMvt) mémorise de façon homogène, l'ensemble des informations disponibles en France, sur des situations récentes et sur des événements passés, et permet le porté à connaissance des phénomènes. Les données CSV ont été adaptées par Esri France. Elles sont projetées en Web Mercator et couvrent la France métropolitaine et les DROM. Des métadonnées et une documentation sont disponibles sur le site web. Il convient de noter que certaines localisations et dates incohérentes, sont déjà présentes dans les données sources non modifiées. Cette couche d’entités est offerte par l’équipe GéoData d’Esri France. Les données sont optimisées pour l’usage dans la plateforme ArcGIS. Plus d’information sur les offres sur esrifrance.fr/contenus. Contactez-nous avec des questions ou des commentaires via livingatlas@esrifrance.fr.
A central focus for governing bodies in Africa is the need to secure the necessary food sources to support their populations. It has been estimated that the current production of crops will need to double by 2050 to meet future needs for food production. Higher level crop-based products that can assist with managing food insecurity, such as cropping watering intensities, crop types, or crop productivity, require as a starting point precise and accurate cropland extent maps indicating where cropland occurs. Current cropland extent maps are either inaccurate, have coarse spatial resolutions, or are not updated regularly. An accurate, high-resolution, and regularly updated cropland area map for the African continent is therefore recognised as a gap in the current crop monitoring services. Key Properties Geographic Coverage: Continental Africa - approximately 37° North to 35 South Temporal Coverage: 2019 Spatial Resolution: 10 x 10 meter Update Frequency: TBD Number of Bands: 3 Bands Parent Dataset: Digital Earth Africa's Sentinel-2 Semiannual GeoMAD Source Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933) Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933) Digital Earth Africa’s cropland extent maps for Eastern, Western, and Northern Africa show the estimated location of croplands in these countries for the period of January to December 2019: Eastern: Tanzania, Kenya, Uganda, Ethiopia, Rwanda and Burundi Western: Nigeria, Benin, Togo, Ghana, Cote d'Ivoire, Liberia, Sierra Leone, Guinea and Guinea-Bissau Northern: Morocco, Algeria, Tunisia, Libya and Egypt Sahel: Mauritania, Senegal, Gambia, Mali, Burkina Faso, Niger, Chad, Sudan, South Sudan, Somalia and Djibouti Southern: South Africa, Namibia, Botswana, Lesotho and Eswatini Cropland is defined as: "a piece of land of minimum 0.01 ha (a single 10m x 10m pixel) that is sowed/planted and harvestable at least once within the 12 months after the sowing/planting date." This definition will exclude non-planted grazing lands and perennial crops which can be difficult for satellite imagery to differentiate from natural vegetation. The provisional cropland extent maps have a resolution of 10 metres and were built using Copernicus Sentinel-2 satellite images from 2019. The cropland extent maps were built separately using extensive training data from Eastern, Western, and Northern Africa, coupled with a Random Forest machine learning model. A detailed exploration of the methods used to produce the cropland extent map can be found in the Jupyter Notebooks in DE Africa’s crop-mask GitHub repository. Independent validation datasets suggest the following accuracies: The Eastern Africa cropland extent map has an overall accuracy of 90.3 %, and an f-score of 0.85 The Western Africa cropland extent map has an overall accuracy of 83.6 %, and an f-score of 0.75 The Northern Africa cropland extent map has an overall accuracy of 94.0 %, and an f-score of 0.91 The Sahel Africa cropland extent map has an overall accuracy of 87.9 %, and an f-score of 0.78 The Southern Africa cropland extent map has an overall accuracy of 86.4 %, and an f-score of 0.75 The algorithms for all regions tend to report more omission errors (labelling actual crops as non-crops) than commission errors (labelling non-crops as crops). Where commission errors occur, they tend to be focussed around wetlands and seasonal grasslands which spectrally resemble some kinds of cropping. Available Bands Band ID Description Value range Data type NoData/Fill value mask crop extent (pixel) 0 - 1 uint8 0 prob crop probability (pixel) 0 - 100 uint8 0 filtered crop extent (object-based) 0 - 1 uint8 0 mask: This band displays cropped regions as a binary map. Values of 1 indicate the presence of crops, while a value of 0 indicates the absence of cropping. This band is a pixel-based cropland extent map, meaning the map displays the raw output of the pixel-based Random Forest classification. prob: This band displays the prediction probabilities for the ‘crop’ class. As this service uses a random forest classifier, the prediction probabilities refer to the percentage of trees that voted for the random forest classification. For example, if the model had 200 decision trees in the random forest, and 150 of the trees voted ‘crop’, the prediction probability is 150 / 200 x 100 = 75 %. Thresholding this band at > 50 % will produce a map identical to mask. filtered: This band displays cropped regions as a binary map. Values of 1 indicate the presence of crops, while a value of 0 indicates the absence of cropping. This band is an object-based cropland extent map where the mask band has been filtered using an image segmentation algorithm (see this paper for details on the algorithm used). During this process, segments smaller than 1 Ha (100 10m x 10m pixels) are merged with neighbouring segments, resulting in a map where the smallest classified region is 1 Ha in size. The filtered dataset is provided as a complement to the mask band; small commission errors are removed by object-based filtering, and the ‘salt and pepper’ effect typical of classifying pixels is diminished. More details on this dataset can be found here.
Very high resolution radiance data from the Copernicus Contributing Missions (CCM) consist of high-resolution satellite imagery provided by commercial mission operators. These datasets support the Copernicus programme in areas such as land monitoring, emergency response, security, and marine environment. The imagery covers a broad spectral range and is tailored for diverse operational and analytical applications.
The Copernicus DEM and related land surface datasets provide high-quality geographic information derived from satellite observations. The EEA-10 dataset covers 39 European countries, including all associated islands as well as the French Overseas Departments (excluding French Overseas Territories). It provides a spatial resolution of 10 metres and covers a total surface area of approximately 6 million km². This dataset is suitable for regional-scale analysis, environmental assessment, land monitoring, and other spatial applications requiring detailed geographic coverage across the European region.
Hyperspectral very high resolution data from the Copernicus Contributing Missions (CCM) consist of high-resolution satellite imagery provided by commercial mission operators. These datasets support the Copernicus programme in areas such as land monitoring, emergency response, security, and marine environment. The imagery covers a broad spectral range and is tailored for diverse operational and analytical applications.
Dekadal (10 days) Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.
Monthly Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.
Dekadal (10 days) Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.
Monthly Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.
Dekadal (10 days) Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.
Monthly Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.
Monthly Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.
Dekadal(10days) Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.
Dekadal(10days) Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.
Monthly Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.
The Census Bureau mapped the Democratic Republic of the Congo and Zambia. A variety of spatial datasets were combined with census data and estimates to produce gridded population layers.
Facebook has produced a model to help map global medium voltage (MV) grid infrastructure, i.e. the distribution lines which connect high-voltage transmission infrastructure to consumer-serving low-voltage distribution. The data found here are model outputs for six select African countries: Malawi, Nigeria, Uganda, DRC, Cote D’Ivoire, and Zambia. The grid maps are produced using a new methodology that employs various publicly-available datasets (night time satellite imagery, roads, political boundaries, etc) to predict the location of existing MV grid infrastructure. The model documentation and code are also available , so data scientists and planners globally can replicate the model to expand model coverage to other countries where this data is not already available. You can find the model code and documentation here: https://github.com/facebookresearch/many-to-many-dijkstra Note: current model accuracy is approximately 70% when compared to existing ground-truthed data. Accuracy can be further improved by integrating other locally-relevant information into the model and running it again. Resolution: geotiff is provided at Bing Tile Level 20
Dekadal (10 days) Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.
Monthly Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.
In line with ongoing efforts to streamline the Cadre Harmonisé (CH) process, and given the budget constraints that have limited the resources available for CH analyses in the region, only four countries conducted CH analyses during the February/March 2026 cycle. The Gambia, Senegal and Chad conducted new analyses covering both the current period (March to May 2026) and the projected period (June to August 2026). Nigeria updated the projected analysis from the October/November 2025 cycle for nine states. This included Borno State, where the current situation was also analyzed in 15 of the 23 LGAs. For the remaining countries, namely Benin, Cabo Verde, Cameroon, Côte d’Ivoire, Ghana, Guinea, Liberia, Mali, Mauritania, Niger, Sierra Leone and Togo, the projected situation from the October/November 2025 cycle was carried forward. In addition, the Central African Republic (CAR) conducted an IPC analysis in May 2026. There is also a global Acute Food Insecurity Country dataset.
Dekadal (10 days) Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.
Monthly Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.
Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 35+ year quasi-global rainfall data set. It is a gridded rainfall time series for trend analysis and seasonal drought monitoring, spans 50°S-50°N (and all longitudes) and ranges from 1981 to near-present. The anomaly refers to the difference between current rainfall and the average rainfall that occurred between 1981 and 2010 in millimeters. For more information visit the CHIRPS site. This dataset contains the latest available CHIRPS anomaly data. The full list of data available is available from USGS for Mar-May data, Oct-Dec data, and others. Additionally, subnational statistics (mean, min, max) have been calculated for Ethiopia, Kenya, and Somalia and are available in the csv resource.
Monthly and annual burnt area 300m rasters are calculated from 2018 onwards. The source data is the Burnt Area (BA) product from COPERNICUS Land Monitoring Service v4 (300m resolution), reprocessed to extract bushfire occurrence statistics. https://land.copernicus.eu/en/products/vegetation/burnt-area-v4-monthly-300m The data used comes from the data generated by the COPERNICUS terrestrial service, the European Commission's Earth observation programme. The research that led to the current version of the product has received funding from various research and technical development programmes of the European Commission. DN values given by GeoTIFF correspond to the month number of fire occurence for the selected season. GeoTIFF raster files and shapefile vector files are available for download at https://geosahel.info For more information, please visit: https://sigsahel.info ******************************************************************************************************** Afrique de l’Ouest et du Centre – Surfaces brûlées Rasters Surfaces annuelles brûlées calculées à partir de 2018. La source des données est le produit « Burnt Area (BA) » du service de surveillance des terres COPERNICUS v4 (résolution de 300 m), retraité pour extraire des statistiques sur les feux de brousse. https://land.copernicus.eu/en/products/vegetation/burnt-area-v4-monthly-300m Les données utilisées sont issues des données générées par le service terrestre COPERNICUS, le programme d'observation de la Terre de la Commission européenne. La recherche qui a conduit à la version actuelle du produit a reçu un financement de divers programmes de recherche et de développement technique de la Commission européenne. Les valeurs DN fournies par le GeoTIFF correspondent au numéro du mois de survenue des feux pour la saison sélectionnée. Les fichiers raster GeoTIFF et les fichiers vectoriels shapefile sont disponibles au téléchargement sur https://geosahel.info Pour plus d’informations, veuillez consulter : https://sigsahel.info
Yearly biomass productions are retrieved from DMP delivered by Copernicus Land Monitoring Service. https://land.copernicus.eu/en/products/vegetation?tab=primary_production The data used comes from the data generated by the COPERNICUS terrestrial service, the European Commission's Earth observation programme. The research that led to the current version of the product has received funding from various research and technical development programmes of the European Commission. The product is based on data from Sentinel-3 OLCI, PROBA-V and SPOT-VEGETATION from ESA. These data are freely accessible for humanitarian or research purposes. Any commercial exploitation of this data is prohibited. No compensation can be demanded in exchange for the dissemination of this data. Biomass file values: [0 ; +inf[: Yearly biomass production (kg/ha) -9999: No data -9995: Outside area of interest GeoTIFF raster files and shapefile vector files are available for download at https://geosahel.info For more information, please visit: https://sigsahel.info **************************************************************************************************** Productions annuelles de biomasse en Afrique de l'Ouest depuis 1999 : version raster Production annuelle de biomasse extraites du DMP delivré par Copernicus Land Monitoring Service. https://land.copernicus.eu/en/products/vegetation?tab=primary_production Les données utilisées sont issues des données générées par le service terrestre COPERNICUS, le programme d'observation de la Terre de la Commission européenne. La recherche qui a conduit à la version actuelle du produit a reçu un financement de divers programmes de recherche et de développement technique de la Commission européenne. Le produit est basé sur les données de Sentinel-3 OLCI, PROBA-V et SPOT-VEGETATION de ESA. Ces données sont accessibles librement à des fins humanitaires ou de recherche. Toute exploitation commerciale de ces données est interdite. Aucune compensation ne peut être exigée en échange de la diffusion de ces données. Valeurs des fichiers de biomasse : [0 ; +inf[ : Production annuelle de biomasse (kg/ha) -9999 : Aucune donnée -9995 : En dehors de la zone d'intérêt Les fichiers raster GeoTIFF et les fichiers vectoriels shapefile sont disponibles au téléchargement sur https://geosahel.info Pour plus d'informations, veuillez visiter : https://sigsahel.info
This version of the Combined Drought Indicator (CDI) utilizes ERA5 reanalysis data as the primary input for the precipitation component (SPI), rather than gauge-only or other satellite-blended products. ERA5, produced by ECMWF, offers comprehensive spatial coverage and temporal consistency, making it valuable for regions where gauge networks are failing or sparse. Like the standard CDI, this dataset synthesizes rainfall deficits (via ERA5), soil moisture anomalies, and vegetation stress into a single classification system to monitor the evolution of drought episodes.
The Standardized Precipitation Index (SPI) is the globally recommended standard for characterizing meteorological drought. It quantifies observed precipitation as a standardized deviation from the long-term probability distribution function for a specific location and time scale. This dataset typically includes multiple time-scales (e.g., 1-month, 3-month, 6-month, 12-month) to capture different types of drought impacts, from immediate soil moisture deficits to long-term hydrological stress. Because SPI is normalized, it allows for safe comparisons between different climatic zones—allowing a wet region and a dry region to be compared on the same scale of drought severity.
This dataset contains the absolute values of the Fraction of Absorbed Photosynthetically Active Radiation (fAPAR). While anomalies show deviations from the norm, these absolute values provide a direct measure of the current state of vegetation density and health. It represents the fraction of incoming solar radiation in the photosynthetically active spectrum that is absorbed by the canopy, ranging theoretically from 0 (bare soil) to 1 (dense, healthy vegetation). Absolute fAPAR is crucial for initializing crop growth models, estimating net primary productivity (NPP), and monitoring the phenological development of crops and natural vegetation throughout the growing season.
The fAPAR Anomaly dataset maps deviations in vegetation health and photosynthetic activity across Africa. fAPAR measures the proportion of solar radiation absorbed by plants for photosynthesis, making it a robust biophysical indicator of vegetation status. The anomaly product highlights areas where vegetation activity is significantly below or above the long-term average, serving as a primary proxy for agricultural production and biomass availability. Unlike simple greenness indices (like NDVI), fAPAR is physically related to the energy and mass exchange processes of the canopy. Negative anomalies in this dataset are strong indicators of vegetation stress caused by drought, pests, or disease, making it a critical input for yield forecasting and food security analysis.