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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
Date of Images: Post-Event: 6/26/2026, 6/27/2026 Pre-Event: May 2026 Summary: These Black Marble Day-Night Band (BRDF-Corrected) images were created by the NASA Black Marble Science team. The images are corrected for atmospheric, terrain, lunar BRDF, and straylight effects, and directly measures light intensity on the ground in units of nanowatts/(steradian centimeter squared). The images are scaled from 0 - 30. The baseline image is from September 2025, generated from all daily atmospheric and lunar-corrected data from that month. There is a layer to display where clouds are present. This comparison between the images is meant as a visual assessment of outage impacts to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. Suggested Use: NASA's Black Marble Nighttime Light product suite is a state-of-the-art daily global collection of standard products for monitoring nighttime lights (NTL). Utilizing the Visible Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) aboard the Suomi-NPP, NOAA-20, and NOAA-21 satellites. The image is in inferno color scale. Yellow represents presence of more light; dark blue less lights. Observations may be obscured by total or partial cloud cover. Other factors such as snow reflectance, moon phase, wildfires, and wildfire smoke may distort light radiance and require further analysis when assessing power outages. Satellite/Sensor: Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP), NOAA-20, and NOAA-21 Resolution: 15 arc-seconds (~500 meters) Credits: NASA Black Marble Science team Please cite the following two references when using this data: Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883. Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43. Point of Contact: Ranjay Shrestha NASA Goddard Space Flight Center E-mail: ranjay.m.shrestha@nasa.gov Additional Links: NASA’s Black Marble Product Suite Román, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6). Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143. Service URL: See REST Endpoint on the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202606_EARTHQUAKE_VENEZUELA/202611_blackmarble_brdf_cloud/ImageServer/WMSServer
Dates of Images: 4/28/2024, 4/29/2024, 4/30/2024, 5/1/2024, 5/2/2024, 5/5/2024, 5/6/2024 Summary: LHASA was created at Goddard Space Flight Center to identify the potential for rainfall-triggered landslides in real time. LHASA version 2 adopts machine learning to estimate the probability of landslide occurrence at a 30-arcsecond (~1 km) daily resolution. In addition, it estimates the potential exposure of human population and roads to landslide hazard and maps the basins likely to experience post-fire debris flows. Suggested Use: The global landslide nowcast shows where landslide hazard is highest on a given date. Resolution: ~1km Credits: Please cite: Stanley T. A., J. R. Sutton, R. S. Vershel, P. M. Amatya. 2025. "Better Satellite Precipitation Algorithms Slightly Improved Landslide Hazard Assessment." Journal of Applied Meteorology and Climatology 64 (10): 1379-1394 10.1175/jamc-d-25-0021.1 Esri REST Endpoint: See URL on the right side of the page. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/EX2602_lhasa/ImageServer/WMSServer
Abstract Landslides can have significant and pervasive impacts to life and property around the world. Several attempts have been made to predict the geographic distribution of landslide activity at continental and global scales. These efforts shared common traits such as resolution, modeling approach, and explanatory variables. The lessons learned from prior research have been applied to build a new global susceptibility map from existing and previously unavailable data. Data on slope, faults, geology, forest loss, and road networks were combined using a heuristic fuzzy approach. The map was evaluated with a Global Landslide Catalog developed at the National Aeronautics and Space Administration, as well as several local landslide inventories. Comparisons to similar susceptibility maps suggest that the subjective methods commonly used at this scale are, for the most part, reproducible. However, comparisons of landslide susceptibility across spatial scales must take into account the susceptibility of the local subset relative to the larger study area. The new global landslide susceptibility map is intended for use in disaster planning, situational awareness, and for incorporation into global decision support systems. Data visualization Landslide susceptibility is shown using a 5-color Color Ramp from blue to red. Values represent Very Low (blue) to Very High (red) susceptibility to a landslide. Further reference Read more about the susceptibility map on the Global Precipitation Measurement (GPM) mission website. Stanley, T., and D. B. Kirschbaum (2017), A heuristic approach to global landslide susceptibility mapping, Nat. Hazards, 1–20, doi:10.1007/s11069-017-2757-y.
Dates of Image: 3 Pairs: 6/13/2026, 6/25/2026 6/17/2026, 6/29/2026 6/18/2026, 6/30/2026 Date of Next Image: Unknown Summary: Scientists working with Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and California Institute of Technology, both in Pasadena, California, created this displacement map with measurements of the surface displacements, in the radar line-of-sight, caused by the recent major earthquake in Northern Venezuela (6/24/2026). The map was derived from synthetic aperture radar (SAR) images from the NASA-ISRO Synthetic Aperture Radar (NISAR) satellite. The team computed the interferometric difference (interferogram or interferometric SAR) between the post-event image acquired on 6/25/2026 a pre-event image acquired on 6/13/2026. The interferometric SAR (InSAR) measures the component of surface displacement in the line-of-sight (LOS) between the ground and the satellite, which is at an angle of about 40 degrees from the vertical and slightly south of due west. NOTE: The line-of-sight displacement (LOS) measurement is relative, and the dataset has not been calibrated to any ground location so there is a bias of the reference level, which is unknown. This data has not yet been validated. This displacement map should be used as guidance to identify areas of significant ground displacement, and may be less reliable over snow-covered and vegetated areas. Suggested Use: Unwrapped Interferogram (UNW): Red colors indicate increasing displacement towards the West, while the Blue areas indicate increasing displacement towards the East NOTE: Small vertical displacements have also occurred, but the primary surface displacements were in the horizontal plane. Satellite/Sensor: NASA-ISRO Synthetic Aperture Radar (NISAR) Resolution: 30 meters Credits: NASA-JPL/Caltech Team, NISAR Operations Science Team Esri REST Endpoint: https://gis.earthdata.nasa.gov/portal/rest/services/DISASTERS_202606_EARTHQUAKE_VENEZUELA/202611_nisar_LOSDisplacement/ImageServer WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202606_EARTHQUAKE_VENEZUELA/202611_nisar_LOSDisplacement/ImageServer/WMSServer
Labels for glaciers in the United States, published by the US Census Bureau. Each feature represents a glacier and includes attributes such as name, coordinates, and area.
The open water layer represents the compilation of later winter open water observations including regionally focused helicopter-based open water surveys from historical exploration work and various open water observations throughout the Yukon. Distributed from GeoYukon by the Government of Yukon. Discover more digital map data and interactive maps from Yukon’s digital map data collection. For more information: geomatics.help@yukon.ca
Mapping depicts Brampton Plan High Potential Mineral Aggregate Resource Area (HPMARA) in Schedule 9 identifies primary and secondary sand and gravel resources, as well as bedrock resources that are not constrained by Provincial, Region of Peel or City of Brampton policies and are to be protected for long-term use. Further constraints to extraction within and outside of HPMARA are subject to the Region of Peel Official Plan, Secondary Plan Area 52 – Heritage Heights (upon approval), and Corridor Protection Policies of this Plan and Greenbelt Plan. This data is intended to be used in conjuction with other related mapping layers for this schedule, and may be impacted by other schedules, with consideration to the text of Brampton Plan.
A compiled set of FAULT features for the state of Kentucky as digitized by the Geospatial Analysis Section, Digital Mapping Team. Data set is derived from the 7.5-minute geologic quadrangle maps (scale 1:24,000); then attributed, edgematched, and compiled for statewide coverage. The data are produced and distributed by the Kentucky Geological Survey.
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 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
Date of Images: Post-Event: 6/26/2026, 6/27/2026 Pre-Event: May 2026 Summary: These Black Marble Day-Night Band (BRDF-Corrected) images were created by the NASA Black Marble Science team. The images are corrected for atmospheric, terrain, lunar BRDF, and straylight effects, and directly measures light intensity on the ground in units of nanowatts/(steradian centimeter squared). The images are scaled from 0 - 30. The baseline image is from September 2025, generated from all daily atmospheric and lunar-corrected data from that month. There is a layer to display where clouds are present. This comparison between the images is meant as a visual assessment of outage impacts to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. Suggested Use: NASA's Black Marble Nighttime Light product suite is a state-of-the-art daily global collection of standard products for monitoring nighttime lights (NTL). Utilizing the Visible Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) aboard the Suomi-NPP, NOAA-20, and NOAA-21 satellites. The image is in inferno color scale. Yellow represents presence of more light; dark blue less lights. Observations may be obscured by total or partial cloud cover. Other factors such as snow reflectance, moon phase, wildfires, and wildfire smoke may distort light radiance and require further analysis when assessing power outages. Satellite/Sensor: Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP), NOAA-20, and NOAA-21 Resolution: 15 arc-seconds (~500 meters) Credits: NASA Black Marble Science team Please cite the following two references when using this data: Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883. Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43. Point of Contact: Ranjay Shrestha NASA Goddard Space Flight Center E-mail: ranjay.m.shrestha@nasa.gov Additional Links: NASA’s Black Marble Product Suite Román, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6). Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143. Service URL: https://gis.earthdata.nasa.gov/portal/rest/services/DISASTERS_202606_EARTHQUAKE_VENEZUELA/202611_brdf/ImageServer WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202606_EARTHQUAKE_VENEZUELA/202611_brdf/ImageServer/WMSServer
Perimetrazioni dei vincoli idrogeologici nella regione Umbria
Date of Image: Sentinel 1 UNW: 2/21/2023 Sentinel-1 RNG & AZI: 2/9/2023 JAXA ALOS-2 UNW, AZI & RNG: 2/8/2025 Summary: Scientists working with Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and California Institute of Technology, both in Pasadena, California, created this displacement map with measurements of the surface displacements, in the radar line-of-sight, caused by the major earthquake in Turkiye. NOTE: The line-of-sight displacement (LOS) measurement is relative, and the dataset has not been calibrated to any ground location so there is a bias of the reference level, which is unknown. This data has not yet been validated. This displacement map should be used as guidance to identify areas of significant ground displacement, and may be less reliable over snow-covered and vegetated areas. SENTINEL-1: The map was derived from synthetic aperture radar (SAR) images from the Copernicus Sentinel-1 satellites, operated by the European Space Agency (ESA). The team computed the interferometric difference (interferogram or interferometric SAR) between the post-event and pre-event images on the Sentinel-1 ascending (satellite moving north) track 14. The interferometric SAR (InSAR) measures the component of surface displacement in the line-of-sight (LOS) between the ground and the satellite, which is at an angle of about 40 degrees from the vertical and slightly south of due west. Sentinel 1 UNW: post-event image from 2/21, pre-event image from 2/9. Sentinel 1 RNG & AZI: post-event image from 2/9, pre-event image 1/28. JAXA ALOS-2: The maps were derived from synthetic aperture radar (SAR) images from the ALOS-2 L-band operated by the Japanese Aerospace Exploration Agency (JAXA) ALOS-2 SAR satellite. The team computed the interferometric difference (interferogram or interferometric SAR) and pixel offset tracking between the post-event image acquired on February 8, 2023 with a pre-event image acquired on April 6, 2022, on the ALOS-2 descending (satellite moving south) track 78. The interferometric SAR (InSAR) measures the component of surface displacement in the line-of-sight (LOS) between the ground and the satellite, which is at an angle of about 30 degrees from the vertical up and to the east. Suggested Use: Unwrapped Interferogram (UNW): Red colors indicate increasing displacement towards the West, while the Blue areas indicate increasing displacement towards the East Across Track Displacement (RNG): Red colors indicate increasing displacement towards the West, while the Blue areas indicate increasing displacement towards the East Along Track Displacement (AZI): Red colors indicate increasing displacement towards the West, while the Blue areas indicate increasing displacement towards the East NOTE: Small vertical displacements have also occurred, but the primary surface displacements were in the horizontal plane. Satellite/Sensor: Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) Japan Aerospace Exploration Agency (JAXA) ALOS-2 PALSAR-2 Resolution: Sentinel-1: 90 meters ALOS-2: 30 meters Credits: NASA-JPL/Caltech ARIA Team The Sentinel-1 data products contain modified Copernicus Sentinel data (2023), processed by the European Space Agency and analyzed at the NASA Jet Propulsion Laboratory. The ALOS-2/PALSAR-2 Data Products are provided by JAXA, facilitated by NASA and analyzed at the NASA Jet Propulsion Laboratory. Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=bbcdefb8b11546f28e83bfa9cac87f9a WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202302_EARTHQUAKE_TURKIYE/2302_opera_surf_disp/ImageServer/WMSServer Data Download: Sentinel-1 UNW: https://aria-share.jpl.nasa.gov/20230206_Turkey_EQ/Displacements/Sentinel1/A014_20230209_20230221/ Sentinel 1 RNG & AZI: https://aria-share.jpl.nasa.gov/20230206_Turkey_EQ/Displacements/Sentinel1/A014_20230128_20230209/ ALOS-2: https://aria-share.jpl.nasa.gov/20230206_Turkey_EQ/Displacements/
Dates of Images: Post-Event: 06/25/2026 Pre-Event: 04/07/2026-06/13/2026 (8 images total) Summary: The NASA GSFC landslides team produced this landslide proxy heatmap from Sentinel-1 imagery. Potential landslides were detected by calculating the change in the backscatter coefficient before and after the triggering event using the log ratio approach (Iratio). The Iratio is generated by subtracting median pre- and post-event Radiometrically Terrain Corrected (RTC) gamma0 backscatter in decibel scale at 10 m resolution obtained from the Alaska Sar Facility’s Hybrid Pluggable Processing Pipeline (HyP3). This map should be used as a guide to identify areas likely affected by landslides. This is a rapid response product. We have not done any form of manual verification or corrections to remove false positives. The likely landslide pixels are identified using 99th percentile Iratio threshold considering areas greater than 10-degree in slope and not affected by radar shadow and layover effects. A heatmap is generated using identified landslide pixels for visualization ease. Pre-event imagery = median imagery generated using images from 04/07/2026-06/13/2026 (8 images total) Post-event imagery = 06/25/2026 Suggested Use: The red and yellow areas indicate potential zones of dense landsliding. False positives related to natural and human-made ground surface change, including but not limited to deformation from mining, construction, deforestation, agriculture, flooding, snow cover, and changes in reservoir water levels can also be within the red and yellow areas. Satellite/Sensor/Resolution: Synthetic Aperture Radar on European Space Agency's (ESA) Copernicus Sentinel-1 satellite, 10 meters Credits: NASA GSFC landslides team, Copernicus Sentinel-1 data Citation: Handwerger AL, Huang M-H, Jones SY, Amatya P, Kerner HR, Kirschbaum DB. 2022. Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine. Nat. Hazards Earth Syst. Sci. Copernicus Publications, 22(3): 753–773. https://doi.org/10.5194/nhess-22-753-2022. Esri REST Endpoint: See URL to the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202606_EARTHQUAKE_VENEZUELA/202611_sentinel1_landslide_heatmap/ImageServer/WMSServer
These data provide users with a basic geologic framework of mapped structure contours for the state of Kentucky. These data were created with funding from the National Cooperative Geologic Mapping Act's STATEMAP Program, administered by the U.S. Geological Survey. KGS utilized this program to convert existing 7.5-minute geologic quadrangle maps to digital format. The goal of the digital mapping program is to establish a mapping database that links the graphic data with their geologic components. The data are produced and distributed by the Kentucky Geological Survey. Stuctural contours of varying datum digitized from the 1:24,000 Geologic Map Series maps. Structural contours were constructed by geologic map authors when the original geologic maps were published during the Kentucky geologic mapping program (Kentucky Geological Survey - U.S. Geological Survey from 1960 to 1980). Contoured formations were determined by the quadrangle map author, so contour datum may vary across quadrangle boundaries. Therefore contours are not based on the most recent subsurface data and should only be used as a general guideline for the underlying geologic structure. Contours are in feet above sea level, and may vary in contour interval across quadrangle boundaries. Please note that some contours are projected above the land surface. Download File Geodatabase: https://ky.box.com/s/wng7lo6z4ko4zvfdfqvmg28ravwxi09p
Date of Images: Dates used for landslide mapping: April 20, 2024; April 21, 2024; May 6, 2024; May 7, 2024; May 8, 2024 Dates of Planet Imagery: 4/20/2023, 5/6/2024, 5/7/2024 Summary: The NASA GSFC landslides team manually mapped landslide initiation points after the April 29, 2024 heavy rainfall in Rio Grande do Sul, Brasil. The landslide initiation points were derived from PlanetScope imagery. This is only a portion of the region where landslides occurred, and areas covered with clouds were not included. More landslides will be mapped in coming days. NOTE: This is a rapid response product. These landslides were manually mapped using different dates of PlanetScope imagery, but all are presumed to have been triggered by the 4/29/2024 heavy rainfall event. As clouds cleared, more landslides were identified. This PlanetScope imagery captured by Planet Labs Inc. in May 2024 shows landslides following the heavy rainfall in Brasil. True Color RGB provides a product of how the surface would look to the naked eye from space. The True Color RGB s produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred. Suggested Use: True Color RGB provides a product of how the surface would look to the naked eye from space. The True Color RGB is produced using the 3 visible wavelength bands (red, green, and blue) from the respective sensor. Some minor atmospheric corrections have occurred. Satellite/Sensor: PlanetScope Resolution: 3 meters Credits: NASA Disasters Program, NASA GSFC, Includes copyrighted material of Planet Labs PBC. All rights reserved. Esri REST Endpoint: See Layers below. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/ex2602_landslide_points/MapServer/WMSServer
Landslides are a widespread and often underestimated natural hazard, causing thousands of deaths and significant economic damage each year. They routinely block roads, destroy infrastructure, and disrupt communities worldwide. While intense or prolonged rainfall is the most common landslide trigger, earthquakes and human activities can also cause them. The global Landslide Hazard Assessment for Situational Awareness (LHASA) model provides users with a comprehensive view of landslide hazard in near real-time. Using machine learning, LHASA combines rainfall data from the Global Precipitation Measurement (GPM) satellite with antecedent soil moisture from the Soil Moisture Active Passive (SMAP) satellite to identify areas currently at risk from landslides. The model shows where and when landslides are most probable, then calculates the exposed population and road length within each administrative district, helping users understand both the hazard and potential impacts on communities and infrastructure. Daily global landslide exposure assessment from the LHASA (Landslide Hazard Assessment for Situational Awareness) model. Color-coded hazard levels range from red (very high exposure) to yellow (low exposure), helping users identify areas where landslides may occur. To learn more please visit: https://landslides.nasa.gov/
Earthquake impacts mapping from drone data in Nepal (Sanfebagar, Achham District, Sudurpashchim Province) from GLOBHE Crowddroner. The dataset includes high-resolution actionable drone data showing the area impacted from the earthquakes of early November 2022. Reference: https://reliefweb.int/disaster/eq-2022-000358-npl MORE CROWDDRONING BY GLOBHE Web: https://globhe.com/ Facebook: https://www.facebook.com/Crowddroning Twitter: https://twitter.com/globhedrones Instagram: https://www.instagram.com/globhedrones/ LinkedIn: https://www.linkedin.com/company/globhedrones/
Earthquake impacts mapping in Nepal (Sayapada, Chaurpati-0, Achham) from GLOBHE Crowddroner. The dataset includes high-resolution actionable drone data showing the area impacted from the earthquakes of early November 2022. Reference: https://reliefweb.int/disaster/eq-2022-000358-npl MORE CROWDDRONING BY GLOBHE Web: https://globhe.com/ Facebook: https://www.facebook.com/Crowddroning Twitter: https://twitter.com/globhedrones Instagram: https://www.instagram.com/globhedrones/ LinkedIn: https://www.linkedin.com/company/globhedrones/
Earthquake impacts mapping from drone data in Nepal (Gairagaun, Pubichauki rural municipality ward-3, Doti) from GLOBHE Crowddroner. The dataset includes high-resolution actionable drone data showing the area impacted from the earthquakes of early November 2022. Reference: https://reliefweb.int/disaster/eq-2022-000358-npl MORE CROWDDRONING BY GLOBHE Web: https://globhe.com/ Facebook: https://www.facebook.com/Crowddroning Twitter: https://twitter.com/globhedrones Instagram: https://www.instagram.com/globhedrones/ LinkedIn: https://www.linkedin.com/company/globhedrones/
Operational Presence for the Earthquake Response
Imagery captured by Digital Globe on 2 October 2018 of Palu, Indonesia. The data is available as a service https://www.arcgis.com/home/item.html?id=d0ff196041e04e0d8d3a09e1e435625d where the tiffs can also be downloaded.
A compilation of publicly available data from the National Earthquake Monitoring and Research Center, Government of Nepal, Ministry of Industry, Commerce and Supplies, Department of Mines and Geology. The accuracy and completeness of the data are subject to the original source.
The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) Emissivity for Land (CAMEL) dataset provides monthly emissivity at 0.05 degree (~5 kilometer) resolution (CAM5K30EM). The CAM5K30EM data product was created by combining the University of Wisconsin-Madison MODIS Baseline Fit (UWBF) emissivity database and the Jet Propulsion Laboratory ASTER Global Emissivity Dataset Version 4 (GED V4). The two datasets have been integrated to capitalize on the unique strengths of each product's characteristics. The integration steps include adjustment of ASTER GED Version 3 emissivities for vegetation and snow cover variations to produce ASTER GED Version 4, aggregation of ASTER GED Version 4 emissivities from 100 meter resolution to the UWBF 5 kilometer resolution, merging of the five ASTER spectral emissivities with the UWBF emissivity to create CAMEL at 13 hinge points, and extension of the 13 hinge points to high spectral resolution (HSR) utilizing the Principal Component (PC) regression method. Additional details regarding the methodology are available in the User Guide and Algorithm Theoretical Basis Document (ATBD). Provided in the CAM5K30EM product are layers for the CAMEL emissivity, ASTER Normalized Difference Vegetation Index (NDVI), snow fraction derived from MODIS (MOD10), latitude, longitude, CAMEL quality, ASTER quality, and the UWBF emissivity quality information. Known Issues * The following granules are missing from the product: January, February, and August of 2000; June and July of 2001; March of 2002; December of 2003; July of 2010; and February of 2016.
# Indonesia Earthquake Flores: Damage Proxy Maps, Aug 2026 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage proxy maps for Indonesia Earthquake Flores (Aug 2026) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: West of Flores, East Nusa Tenggara Region, Indonesia, Earthquakes, 24 August 2026, v0.9 ** - **EOS-RS Damage Proxy Map: Flores, East Nusa Tenggara Region, Indonesia, Earthquakes, 18 August 2026, v0.9 ** **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2026)
# EOS-RS Damage Proxy Map: Kumamoto, Japan, Earthquakes, 29 July 2026, v0.9 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Damage Proxy Map (DPM) depicting areas that are likely damaged in the Kumamoto Region, Japan due to the Mw6.8 (MJMA 7.1) earthquake on 28 July 2026. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite using a pre-event time series of (04 April 2026 to 16 July 2026) and a post-event acquisition (28 July 2026). The map covers the area indicated by the white polygon. Damage is shown by colored pixels of approximately 30m in size, where yellow to red indicates increasingly significant ground surface change before and after the event. Preliminary validation was conducted using news reports, pictures and videos in selected areas. This map should be used as guidance to identify damaged areas and may be less reliable over vegetated or mountainous areas. Scattered pixels over such areas may be false positives, and a lack of colored pixels does not necessarily indicate an absence of damage. This product contains modified Copernicus Sentinel data (2026), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS). **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2026)
# Venezuela Earthquake Caracas: Damage Proxy Maps, Jun 2026 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage proxy maps for Venezuela Earthquake Caracas (Jun 2026) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: La Guaira, Venezuela, Earthquakes, 25 June 2026, v0.8 ** - **EOS-RS Damage Proxy Map: Yuracuy Region, Venezuela, Earthquakes, 25 June 2026, v0.8 ** **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2026)
# EOS-RS Damage Proxy Map: Hualien County, Taiwan, Landslide, 24 June 2026, v1.2 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Damage Proxy Map (DPM) depicting areas that are likely damaged in Hualien County, Taiwan, due to the landslide that created a barrier lake on 22 June 2026. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite using a pre-event time series of (23 Feb 2026 to 11 June 2026) and a post-event acquisition (23 June 2026). The map covers the area indicated by the white polygon. Damage is shown by colored pixels of approximately 30m in size, where yellow to red indicates increasingly significant ground surface change before and after the event. Preliminary validation was conducted using news reports and pictures in selected areas. This map should be used as guidance to identify damaged areas and may be less reliable over vegetated or mountainous areas. Scattered pixels over such areas may be false positives, and a lack of colored pixels does not necessarily indicate an absence of damage. This product contains modified Copernicus Sentinel data (2026), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS). **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2026)
# Philippines Earthquake Mindanao: Damage Proxy Maps, Jun 2026 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage proxy maps for Philippines Earthquake Mindanao (Jun 2026) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: Mindanao, Philippines, Earthquakes, 16 June 2026, v0.9 ** - **EOS-RS Damage Proxy Map: Mindanao, Philippines, Earthquakes, 13 June 2026, v0.5 ** - **EOS-RS Damage Proxy Map: West Mindanao, Philippines, Earthquakes, 09 June 2026, v0.8 ** **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2026)
# Northern Afghanistan Earthquakes: Damage Proxy Maps, Nov 2025 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage proxy maps for Northern Afghanistan Earthquakes (Nov 2025) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: Samangan Province, Afghanistan, Earthquakes, 05 Nov 2025, v0.9 ** - **EOS-RS Damage Proxy Map: Northern Afghanistan, Earthquakes, 04 Nov 2025, v0.9 ** **More map details and files:** **More map details and files:** leaflet/EOS-RS_202511_Northern_Afghanistan_Earthquakes **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2025)
# Philippines Northern Cebu Earthquakes: Damage Proxy Maps, Oct 2025 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage proxy maps for Philippines Northern Cebu Earthquakes (Oct 2025) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: Northern Cebu, Philippines, Earthquakes, 05 Oct 2025, v0.9 ** - **EOS-RS Damage Proxy Map: Northern Cebu, Philippines, Earthquakes, 02 Oct 2025, v0.5 ** **More map details and files:** **More map details and files:** leaflet/EOS-RS_202510_Philippines_Northern_Cebu_Earthquakes **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2025)
# EOS-RS Damage Proxy Map: Mindanao, Philippines, Earthquakes, 13 Oct 2025, v0.9 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Damage Proxy Map (DPM) depicting areas that are likely damaged in Mindanao, Philippines, due to the Mw7.4 earthquake on 10 Oct 2025 and its aftershocks. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite operated by the European Space Agency (ESA) before (15 Jun 2025 to 01 Oct 2025) and after (13 Oct 2025) the event. The map covers an area indicated by the white polygon. Damage is shown by colored pixels of approximately 30m in size, where yellow to red indicates increasingly significant ground surface change before and after the event. Preliminary validation was conducted using news reports, and ground-level imagery and videos in selected areas. This map should be used as a guidance to identify damaged areas, and may be less reliable over vegetated or mountainous areas. Scattered pixels over vegetated or mountainous areas may be false positives, and a lack of colored pixels over such areas may not mean no damage. This product contains modified Copernicus Sentinel data (2025), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS). **More map details and files:** **More map details and files:** leaflet/EOS-RS_202510_Philippines_Mindanao_Earthquakes **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2025)
# Afghanistan Earthquakes: Damage Proxy Maps, Sep 2025 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage proxy maps for Afghanistan Earthquakes (Sep 2025) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: Kunar Province, Afghanistan, Earthquakes, 1 Sep 2025, v0.7-full ** (superseded) - **EOS-RS Damage Proxy Map: Kunar Province, Afghanistan, Earthquakes, 1 Sep 2025, v0.7-bldg ** **More map details and files:** **More map details and files:** **Legal Disclaimer:**
# EOS-RS Damage Proxy Map: Kazarman, Kyrgyzstan, Landslide, 28 Jul 2025, v0.9 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Damage Proxy Map (DPM) depicting areas that are likely damaged in Kazarman, Kyrgyzstan, due to a landslide. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite operated by the European Space Agency (ESA) before (18 Mar 2025 to 16 Jul 2025) and during (28 Jul 2025) the event. The map covers an area indicated by the white polygon. Damage is shown by colored pixels of 30m in size, where yellow to red indicates increasingly significant ground surface change before and after the event. Preliminary validation was conducted using news reports, ground level and satellite imagery in selected areas. This map should be used as a guidance to identify damaged areas, and may be less reliable over vegetated or mountainous areas. Scattered pixels over vegetated or mountainous areas may be false positives, and a lack of colored pixels over such areas may not mean no damage. The product contains modified Copernicus Sentinel data (2025), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS). **More map details and files:** **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2025)
# EOS-RS Damage Proxy Map: Tajikistan and Afghanistan, Earthquakes, 16 Apr 2025, v0.7 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Damage Proxy Map (DPM) depicting areas that are likely damaged in Tajikistan due to the Mw5.8 earthquake on 13 Apr 2025 and in Afghanistan due to the Mw5.6 earthquake on 15 Apr 2025. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite operated by the European Space Agency (ESA) before (17 Dec 2024 to 04 Apr 2025) and after (16 Apr 2025) the event. The map covers an area indicated by the white polygon. Damage is shown by colored pixels of 30m in size, where yellow to red indicates increasingly significant ground surface change before and after the event. Preliminary validation was conducted using news reports. This map should be used as a guidance to identify damaged areas, and may be less reliable over vegetated or mountainous areas. Scattered pixels over vegetated or mountainous areas may be false positives, and a lack of colored pixels over such areas may not mean no damage. The product contains modified Copernicus Sentinel data (2024-2025), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS). **More map details and files:** **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2024-2025)
# Myanmar Earthquakes: Damage Proxy Maps, Interferograms and Pixel Offset Maps, Mar 2025 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage proxy maps, interferograms and pixel offset maps for Myanmar Earthquakes (Mar 2025) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: Naypyidaw Vicinity, Myanmar, Earthquakes, 05 Apr 2025, v0.9 ** (superseded) - **EOS-RS Damage Proxy Map: Myanmar, Earthquakes, 5 Apr 2025, v0.9 ** - **EOS-RS Damage Proxy Map: Myanmar, Earthquakes, 3 Apr 2025, v0.9 ** - **EOS-RS Damage Proxy Map: Myanmar, Earthquakes, 3 Apr 2025, v0.5 ** (superseded) - **EOS-RS Damage Proxy Map: Naypyidaw Vicinity, Myanmar, Earthquakes, 31 Mar 2025, v0.9 ** - **EOS-RS Pixel Offset Map (Sentinel-2 Data): Myanmar, Earthquakes, Pre-Event: 2025/03/25-27 NORTHING** - **EOS-RS Interferogram (Unwrapped): Myanmar, Earthquakes, 2025/02/16-2025/03/30, v0.1** - **EOS-RS Interferogram (Wrapped): Myanmar, Earthquakes, 2025/02/16-2025/03/30, v0.1** **More map details and files:** **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2024-2025)