Thailand GIS data
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Financial services: banks, ATMs, post offices, money transfer agents, and microfinance branches tagged via `amenity`. Useful for financial-inclusion mapping, cash-distribution planning, and economic analysis. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity - Part of the country exports on HDX by HOT
Points of interest: public-facing amenities, shops, tourist attractions, and landmarks tagged via `amenity`, `shop`, `tourism`, or `man_made`. Useful for general POI reference, urban analysis, and basemap detail. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, shop, tourism, man_made - Part of the country exports on HDX by HOT
Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).
GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.
Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Thailand Changwate Boundaries provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. They are sourced from GISTDA (Geo-Informatics and Space Technology Development Agency) Thailand. These were published in December 2022. Other administrative boundaries for this country are also available: Country Amphoe
Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Thailand Country Boundary provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. They are sourced from GISTDA (Geo-Informatics and Space Technology Development Agency) Thailand. These were published in December 2022. Other administrative boundaries for this country are also available: Changwate Amphoe
Airports and aviation infrastructure: airports, heliports, helipads, runways, terminals, and emergency landing sites tagged via `aeroway`, `building`, or `emergency`. Useful for logistics, evacuation routing, and aerial-response planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: aeroway, building, emergency - Part of the country exports on HDX by HOT
Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Thailand Amphoe Boundaries provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. They are sourced from GISTDA (Geo-Informatics and Space Technology Development Agency) Thailand. These were published in December 2022. Other administrative boundaries for this country are also available: Country Changwate
geoBoundaries ADM2 boundaries for Thailand: 928 units, representing 2019. Source: Royal Thai Survey Department, OCHA ROAP.
**UNOSAT code: FL20210928THA** This map illustrates satellite-detected surface waters in Nakhon Ratchasima province, Thailand as observed from a Kompsat-5 image acquired on 6 Oct 2021 at 18:24 local time. Within the analyzed area of about 1,000 km2 , about 9 km2 of lands appear to be flooded. The water extent appears to have decreased and based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the district of Non Sung with ~ 1,100 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
**UNOSAT code TC20220928THA, GDACS Id: 1000922** This map illustrates satellite-detected surface waters in Chaiyaphum and Nakhon Ratchasima Provinces, Thailand as observed from a RCM-2 image acquired on 4 October 2022 at 06:08 local time. Within the extent of this map of 1,100 km², about 240 km² of lands appear to be flooded. Based on Worldpop population data and the detected surface waters in the map extent, about 24,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
**UNOSAT code: AC20210705THA** This map illustrates the damage assessment after the blast of the 5th July 2021 in Bang Phli Yai sub-district, Bang Phli district, Samut Prakan province, Thailand. UNITAR - UNOSAT identified 328 potentially damaged structures of which 13 are destroyed and 315 are severely or moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).
**UNOSAT code TC20220928THA, GDACS Id: 1000922** This map illustrates satellite-detected surface waters in Ubon Ratchathani Province, Thailand as observed from a GeoEye-1 image acquired on 20 October 2022 at 10:35 local time. Within the analysis extent of this map, about 120 km² of lands appear to be flooded. Based on Worldpop population data and the detected surface waters within the analysis extent, approximately 32,000 people are potentially exposed to or living close to flooded areas along with 11,330 structures and approximately 400 km roads potentially damaged by flooding. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).
This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes: - macroregion (admin-1 including region) - region (admin-2 including state, province, department, governorate) - macrocounty (admin-3 including arrondissement) - county (admin-4 including prefecture, sub-prefecture, regency, canton, commune) - localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb) The dataset also contains human settlement points and polygons for: - localities (city, town, and village) - neighbourhoods (borough, macrohood, neighbourhood, microhood) The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names. Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.
**UNOSAT code: AC20210705THA** Status: Damaged structures observed Action: Completed Analysis: United Nations Satellite Centre (UNOSAT) Destroyed structures observed nearby the blast epicenter as of 7 July 2021; Widespread damage and damaged roofs observed within 1 km radius zone from the blast epicenter as of 7 & 9 July 2021; No damaged roofs observed beyond 2 km radius from the blast epicenter as of 7 & 9 July 2021.
**UNOSAT code: FL20210928THA** This map illustrates satellite-detected surface waters in Central part of Thailand as observed from a Sentinel-1 image acquired on 3 October 2021 at 06:08 local time. Within the analyzed area of about 91,000 km2 , about 3,300 km2 of lands appear to be flooded. The water extent appears to have decreased of about 180 km2 since 27 September 2021. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is 350,000 people mainly located in the Ayutthaya Province with ~ 76,000 People. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
**UNOSAT code: FL20240912THA** This map illustrates satellite-detected water in Doi Tao District, Chiang Mai Province, Thailand, acquired on 6 October 2024 at 13:16 LT. Within the analysed area of approximately 800 km², about 54 km² of land appears to be affected by floodwaters, with around 30 km² of cropland inundated. Based on WorldPop spatial demographic data, approximately 140 people are exposed to or living near the flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).
**UNOSAT code: FL20240912THA** This map illustrates cumulative detected water using multiple satellites and drone in Phayao Province, Thailand from 1 August to 12 October 2024. Within the analysed area of about 6,200 km², a total of about 360 km² of land appears to be affected by flood waters, with around 330 km² of cropland inundated. Based on Worldpop spatial demographic data, about 13,500 people are exposed or living close to flooded areas from 1 August to 12 October 2024. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).
**UNOSAT code: FL20210928THA** This map illustrates satellite-detected surface waters in Sukhothai, Phitsanulok, Kamphaeng Phet, and Uttaradit provinces of Thailand as observed from a Sentinel-1 image acquired on 2 October 2021 at 06:17 local time. Within the analyzed area of about 4,500 km2 , about 450 km2 of lands appear to be flooded. The water extent appears to have increased of about 90 km2 since 28 September 2021. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the district of Mueang Sukhothai with ~ 14,000 people, Sawankhalok with ~ 14,000 people, Khiri Mat with ~ 7,400 people, and Si Samrong with ~ 7,000 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
**UNOSAT code: FL20240912THA** This map illustrates cumulative detected water using multiple satellites in Chiang Rai Province, Thailand from 1 August to 22 October 2024. Within the boundary of Chiang Rai Province of about 11,600 km², a total of about 875 km² of land appears to be affected by flood waters, with around 700 km² of cropland inundated. Based on Worldpop spatial demographic data, about 94,100 people are exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT). This work is the result of collaboration between UNOSAT and GISTDA operating under the agreement of the UNOSAT Rapid Mapping Partners Initiative Programme
**UNOSAT code: FL20240912THA** This map illustrates cumulative satellite-detected water extent from multiple satellites in Chiang Mai and Lamphun Provinces, Thailand, from 5 to 10 October 2024. Within the cloud-free analysed areas of about 4,000 km², about 245 km² of land appears to have been affected by flood waters, with around 70 km² of cropland inundated. Based on Worldpop spatial demographic data, about 228,000 people are exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).
**UNOSAT code: EQ20250328THA, GDACS ID: 1474477** This map shows no visible damage based on a very high resolution satellite image collected 29 March 2025 when compared to very high resolution satellite imagery from 1 February 2025. The USGS shake map indicates the intensity of the earthquake measured 5.0 in this area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).
**UNOSAT code: FL20210928THA** This map illustrates satellite-detected surface waters in Northeastern part of Thailand as observed from a Sentinel-1 image acquired on 10 Oct 2021 at 06:00 local time and using an automated analysis with Machine learning method. Within the analyzed area of about 60,000 km2 , about 660 km2 of lands appear to be flooded. The water extent appears to have decreased of about 100 km2 since 5 October 2021 and moved toward east part. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the district of Mueang Khon Kaen with ~ 6,300, Kosum Phisai with ~ 6,000 people, and Phimai with ~ 5,300 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
**UNOSAT code: FL20210928THA** This map illustrates satellite-detected surface waters in Northeastern part of Thailand as observed from a Sentinel-1 image acquired on 17 Oct 2021 at 18:20 local time and using an automated analysis with Machine learning method. Within the analyzed area of about 60,000 km2 , about 810 km2 of lands appear to be flooded. The water extent appears to have increased of about 220 km2 since 16 October 2021. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is 80,000 people mainly located in province of Khon Kaen Province with ~ 24,000 and Nakhon Ratchasima with ~ 22,000 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between **paved** and **unpaved** roads across the region. Surface classification is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name Thailand **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 1.086 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.1802 million km (16.5914%) - Unpaved roads: 0.2861 million km (26.3481%) About 0.6197 million km (57.0604%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0105 million km (1.6994%) of the missing surface information. Intended Use This dataset supports transportation planning, infrastructure analysis, climate emissions assessment, and GIS applications. AI Features - **pred_class**: Model-predicted surface class ("paved" or "unpaved") - **pred_label**: Binary label (0 = paved, 1 = unpaved) - **osm_surface_class**: Surface classification derived from OSM tags - **combined_surface_osm_priority**: Combined classification prioritizing OSM - **combined_surface_DL_priority**: Combined classification prioritizing DL prediction - **n_of_predictions_used**: Number of predictions used for estimation - **predicted_length**: Predicted length based on DL estimation (meters) - **DL_mean_timestamp**: Mean timestamp of predictions used OSM Attributes - **name** - **name:en** - **name:* (local language)** - **highway** - **surface** - **smoothness** - **width** - **lanes** - **oneway** - **bridge** - **layer** - **source** Urban Classification Attributes - **continent** - **country_iso_a2** - **urban** (0 = rural, 1 = urban) - **urban_area** - **osm_id** - **osm_type** Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org
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Thailand administrative level 0-3 boundaries (COD-AB) dataset version 01. This dataset is structured into 3 levels: - Admin 1: 77 Province - Admin 2: 928 District - Admin 3: 7425 Sub-district (Tambon) Dates associated with this dataset: - 30 October 2025: dataset reviewed for accuracy and completeness - 22 January 2022: valid for use by the humanitarian community - 01 January 2022: last edit to the dataset before publishing - 01 January 2022: boundaries created by the source Contributed by OCHA Regional Office for Asia and the Pacific (ROAP). Quality assured, configured, and published by OCHA Field Information Services (FIS) and HDX. Part of the dataset: Global Subnational Administrative Boundaries
**UNOSAT code: FL20210928THA** This map illustrates satellite-detected surface waters in Khon Kaen, Chaiyaphum, Nakhon Ratchasima, and Nong Bua Lam Phu provinces, Thailand as observed from a Sentinel-1 image acquired on 5 Oct 2021 at 18:20 local time and using an automated analysis with Machine learning method. Within the analyzed area of about 40,500 km2 , about 770 km2 of lands appear to be flooded. The water extent appears to have decreased of about 70 km2 since 29 September 2021. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the district of Phimai with ~ 10,400 people, Non Sung with ~ 9,000 people, Mueang Chaiyaphum with ~ 6,700 people, Mueang Khon Kaen with ~ 4,500 people, and Chonnabot Thai with ~ 4,300 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons. Fields available: SID: A unique storm identifier (SID) assigned by IBTrACS algorithm. ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss) BASIN: Basin of the current storm position SUBBASIN: Sub-basin of the current storm position NATURE: Type of storm (a combination of the various types from the available sources) NUMBER: Number of the storm for the year (restarts at 1 for each year LAT: Mean position - latitude (a combination of the available positions) LON: Mean position - longitude (a combination of the available positions) WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.
**UNOSAT code: FL20210928THA** Summary Of Findings; Floodwaters observed in 17 provinces as of 2 November 2021; Receding flood waters observed in Central Region as of 2 November 2021; Standing flood waters observed in Sukhothai, Phitsanulok, Kamphaeng Phet, Phichit, Phetchabun, Nakhon Sawan, Uthai Thani, and Kanchanaburi provinces as of 2 November 2021; Increased standing flood waters observed in Chai Nat, Suphan Buri, Sing Buri, Ang Thong, Saraburi, Lop Buri, Ayutthaya, Nakhon Pathom and Pathum Thani provinces as of 2 November 2021.
**UNOSAT code: FL20210928THA** This map illustrates satellite-detected surface waters in Central and Western parts of Thailand as observed from a Sentinel-1 image acquired on 21 October 2021 at 06:09 local time. Within the analyzed area of about 90,000 km2 , about 3,200 km2 of lands appear to be flooded. The water extent appears to have receded of about 70 km2 since 10 October 202. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is 370,000 people mainly located in province of Phra Nakhon Si Ayutthaya with ~ 99,000 people, Suphan Buri with ~ 88,000 people, and Nakhon Sawan with ~ 47,000 people, This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.