Asia GIS data

# 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)

# Thailand Earthquakes: Damage Proxy Maps, Mar 2025 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage proxy maps for Thailand 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 ## Maps in this dataset - **EOS-RS Damage Proxy Map: Chiang Mai, Thailand, Earthquakes, 29 Mar 2025, v0.9 ** (superseded) - **EOS-RS Damage Proxy Map: Bangkok, Thailand, Earthquakes, 29 Mar 2025, v0.9 ** - **EOS-RS Damage Proxy Map: Chiang Rai, Thailand, Earthquakes, 29 Mar 2025, v0.7 ** (superseded) **More map details and files:** **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab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)

# India Bangladesh Cyclone Amphan: Damage and Flood Proxy Maps, May 2020 The Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and Caltech, in collaboration with the Earth Observatory of Singapore (EOS), produced these damage and flood proxy maps for India Bangladesh Cyclone Amphan (May 2020) 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 - **ARIA-SG Damage Proxy Map: India and Bangladesh, Cyclone Amphan, 22 May 2020, v0.4 ** (superseded) - **ARIA-SG Damage Proxy Map: India and Bangladesh, Cyclone Amphan, 22 May 2020, v0.4 ** (superseded) - **ARIA-SG Damage Proxy Map: Bangladesh and India, Cyclone Amphan, 22 May 2020, v0.4 ** - **ARIA-SG Damage Proxy Map: Bangladesh and India, Cyclone Amphan, 22 May 2020, v0.4 ** - **ARIA-SG Flood Proxy Map: India, Cyclone Amphan, 22 May 2020, v0.8 ** - **ARIA-SG Flood Proxy Map: India and Bangladesh, Cyclone Amphan, 22 May 2020, v0.4 ** (superseded) - **ARIA-SG Flood Proxy Map: Bangladesh and India, Cyclone Amphan, 22 May 2020, v0.4 ** (superseded) **More map details and files:**

# ARIA Flood Proxy Map: India, Bangladesh, 2019 Floods This map shows areas that are likely flooded in northeast India and Bangladesh due to heavy rains brought by the monsoon season, shown by light blue pixels of 30 m (Copernicus Sentinel-1) and 25 m (ALOS-2) pixel size, and is a preliminary result. The map extents are indicated with red and yellow polygons. This map should be used as a guidance to identify areas that are likely flooded, and may be less reliable over urban and vegetated areas. Derived from synthetic aperture radar data acquired by the Copernicus Sentinel-1 satellites operated by the European Space Agency (ESA) before (15 May) and during (14 Jul) the event, and by the ALOS-2 satellite operated by the Japanese Aerospace Exploration Agency (JAXA) before (25 Mar) and during (15 Jul) the event. Analysed by the ARIA-SG team at the Earth Observatory of Singapore in collaboration with NASA/JPL-Caltech. For more information, please visit **More map details and files:**

Emergency GIS union of the Bangladesh - Subnational Administrative Boundaries administrative level 2 (district or "zila") features and Myanmar District Boundaries MIMU v9.3 administrative level 2 (district) features - shapefile and gazetteer.

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central Asia**. It was last updated on July 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * xx201304_ML1 Most likely food security outcome for July-September 2013 * xx201304_ML2 Most likely food security outcome for October-December 2013 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central Asia**. It was last updated on February 11, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * centralasia201304_ML1 Most likely food security outcome for January-March 2015 * centralasia201304_ML2 Most likely food security outcome for April-June 2015 Where xx is one of the region codes listed above. Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central Asia**. It was last updated on November 13, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * CA201304_ML1 Most likely food security outcome for October-December 2014 * CA201304_ML2 Most likely food security outcome for January-March 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central Asia**. It was last updated on June 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * xx201304_ML1 Most likely food security outcome for April-June 2013 * xx201304_ML2 Most likely food security outcome for July-September 2013 Where xx is one of the region codes listed above. Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central Asia**. It was last updated on August 01, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * CA201304_ML1 Most likely food security outcome for July-September 2014 * CA201304_ML2 Most likely food security outcome for October-December 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central Asia**. It was last updated on January 19, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * xx201401_ML1 Most likely food security outcome for January-March 2014 * xx201401_ML2 Most likely food security outcome for April-June 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central Asia**. It was last updated on April 30, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * centralasia201304_ML1 Most likely food security outcome for April-June 2015 * centralasia201304_ML2 Most likely food security outcome for July-September 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central Asia**. It was last updated on June 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * xx201304_ML1 Most likely food security outcome for April-June 2013 * xx201304_ML2 Most likely food security outcome for July-September 2013 Where xx is one of the region codes listed above. Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central Asia**. It was last updated on July 31, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * centralasia201304_ML1 Most likely food security outcome for July-September 2015 * centralasia201304_ML2 Most likely food security outcome for October-December 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **East Africa**. It was last updated on January 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * xx201304_ML1 Most likely food security outcome for January-March 2013 * xx201304_ML2 Most likely food security outcome for April-June 2013 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

This Archive contains shapefiles for FEWS NET Food Security Outlook for **Central Asia**. It was last updated on April 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: * xx201404_ML1 Most likely food security outcome for April-June 2014 * xx201404_ML2 Most likely food security outcome for July-September 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: * 66 = water * 88 = parks, forests, reserves * 99 = missing data (usually urban centers)

Facebook and Columbia University - CIESIN provide the High Resolution Settlement Layer as the world's most accurate population datasets. More info can be found here: https://dataforgood.fb.com/tools/population-density-maps/ These maps are the distribution of human population spanning Pakistan and India. Each of the 13 TIFF files is a 10 x 10 degree tile (the lower latitude coordinate and longitude coordinates are in the file name). A VRT file is also included.

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Australia, New Zealand, China, and Mongolia for nominal year 2015 at 30 meter resolution (GFSAD30AUNZCNMOCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30AUNZCNMOCE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. Each GFSAD30AUNZCNMOCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. Known Issues * Note overlapping tiles: The following tile also covers part of another tile in GFSAD30SEACE (Indonesia). Please ignore the Indonesian data in the following tile: GFSAD30AUNZCNMOCE_2015_S20E120_001_2017286154500.tif * Additional known issues, including constraints and limitations, are provided on page 22 of the ATBD.

Following publication of the World Map of the Satus of Human-induced Land Degradation (GLASOD), at scale 1:10M, the need for more detailed and more country-specific degradation assessment became apparent. In 1993, the members of the Asian Network on Problem Soils recommended the preparation of a qualitative assessment for South and Southeast Asian at a scale of 1:5 million. The assessment was carried out using a physiographic base map, compiled according to the SOTER methodology, and a slightly modified GLASOD methodology. The information was stored in a digital database and linked to a GIS enabling preparation of thematic outputs in the form of maps, graphs and tables.

This harmonized set of soil parameter estimates for the Indo-Gangetic Plains (IGP) of India, at scale 1:1 000 000, has been derived from soil and terrain data collated in SOTER format by staff of the National Bureau of Soil Survey and Land Use Planning (NBSS and LUP) at Nagpur, India. The data set has been prepared for use in the project on "Assessment of soil organic carbon stocks and change at ... national scale" (GEFSOC), which has IGP-India as one of its four case study areas (see http://www.nrel.colostate.edu/projects/gefsoc-uk/). The land surface of IGP-India has been characterized using 36 unique SOTER units, corresponding with 497 polygons. The major soils of these units have been described using 36 profiles, selected by national soil experts as being representative for these units. The associated soil analytical data have been derived from soil survey reports. Gaps in the measured soil profile data have been filled using a scheme of taxotransfer rules. Parameter estimates are presented by soil unit for fixed depth intervals of 0.2 m to 1 m depth for: organic carbon, total nitrogen, pH(H2O), CECsoil, CECclay, base saturation, effective CEC, aluminum saturation, CaCO3 content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity of saturated paste (ECe), bulk density, content of sand, silt and clay, content of coarse fragments, and available water capacity(-33 to-1500 kPa). These attributes have been identified as being useful for agro-ecological zoning, land evaluation, crop growth simulation, modelling of soil carbon stocks and change, and analyses of global environmental change. The current parameter estimates should be seen as best estimates based on the current selection of soil profiles and data clustering procedure; taxotransfer rules have been flagged to provide an indication of the confidence in the derived data. Results are presented as summary files and can be linked to the 1:1M scale SOTER map in a GIS, through the unique SOTER-unit code. The secondary SOTER data set for IGP-India is considered appropriate for exploratory studies at regional scale (greater than1:1M); correlation of soil analytical data should be done more rigorously when more detailed scientific work is considered.

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Southeast and Northeast Asia for nominal year 2015 at 30 meter resolution (GFSAD30SEACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SEACE data product uses the pixel-based supervised classifiers, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SEACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. Known Issues * Certain small islands in the Pacific are not classified and therefore data for these areas are not provided. * Additional known issues, including constraints and limitations, are provided on page 19 of the ATBD.