Myanmar GIS data
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**UNOSAT code: EQ20250328MMR, GDACS ID: 1474477** This map shows the locations where damage was detected based on a very high resolution satellite image collected 30 March 2025 when compared to very high resolution satellite imagery from 23 March 2025. The USGS shake map indicates the intensity of the earthquake measured 7.5-8.0 in this area. Across the site, 17 damaged buildings within the cultural site were observed and an additional 9 buildings have possible damage. The wall surrounding Mandalay is damaged in multiple locations. 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: EQ20250328MMR, GDACS ID: 1474477** The map shows no visible damage within the World Heritage Site when analysed using a very high resolution image collected 3 April 2025 when compared to imagery from May 2023. The USGS shake map indicates the intensity of the earthquake measured 5.5-6.5 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: EQ20250328MMR, GDACS ID: 1474477** This map shows the locations where damage was detected based on a very high resolution satellite image collected 30 March 2025 when compared to very high resolution satellite imagery from 24 February 2025 and 23 March 2025. The USGS shake map indicates the intensity of the earthquake measured 8.5 in this area. Across the site, 18 damaged buildings within the property boundary, and 25 damaged buildings within the buffer zone. An additional 2 buildings in the property boundary and 5 buildings in the buffer zone have possible damage. Part of a wall to the south of the Innwa area was also damaged. 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: EQ20250328MMR, GDACS ID: 1474477** This map shows the locations where damage was detected based on a very high resolution satellite image collected 03 April 2025 when compared to very high resolution satellite imagery from 23 March 2025. The USGS shake map indicates the intentsity of the earthquake measured 8.5 in this area. Across the site, 34 damaged buildings within the cultural site were observed and an additional 7 buildings have possible damage. 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: EQ20250328MMR, GDACS ID: 1474477** This map shows the locations where damage was detected based on a very high resolution satellite image collected 30 March 2025 when compared to very high resolution satellite imagery from 23 March 2025. The USGS shake map indicates the intentsity of the earthquake measured 8.5 in this area. Across the site, 6 damaged buildings within the cultural site were observed and an additional 5 buildings have possible damage. 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: EQ20250328MMR, GDACS ID: 1474477** The map shows no visible damage within the cultural site when analysed using a very high resolution image collected 3 April 2025 when compared to Pleiades satellite series. The USGS shake map indicates the intensity of the earthquake measured 5.5-6.5 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: EQ20250328MMR, GDACS ID: 1474477** This map shows the locations where damage was detected based on a very high resolution satellite image collected 03 April 2025 when compared to very high resolution satellite imagery from 23 March 2025. The USGS shake map indicates the intentsity of the earthquake measured 8.0 - 8.5 in this area. Across the site 11 damaged cultural heritage locations were observed and an additional 3 have possible damage. 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: EQ20250328MMR, GDACS ID: 1474477** This map illustrates the potentially damaged structures/buildings affected by the March 28, 2025, earthquake in Mandalay. The analysis focuses on a part of Monywa Township, Monywa District, Sagaing Region, where damage was detected using a WorldView-2 very high-resolution satellite image acquired on April 25, 2025. UNOSAT identified 27 damaged structures and 170 potentially damaged. This is a preliminary analysis and has not yet been validated in the field. Ground verification is encouraged, and feedback can be sent to the United Nations Satellite Centre (UNOSAT).
**UNOSAT code: EQ20250328MMR, GDACS ID: 1474477** This application provides geospatial information on ongoing satellite based-assessment related with the 7.7M magnitude Saigaing earthquake in Myanmar & Thailand on the 28 March 2025. Important note: The boundaries and names shown, and the designations used on this map do not imply official endorsement or acceptance by the United Nations. The United Nations Satellite Centre -UNOSAT is not responsible for the misuse or misrepresentation of the map.
**UNOSAT code: EQ20250328MMR, GDACS ID: 1474477** This report provides a comprehensive damage assessment based on satellite analysis and mapping efforts carried out by UNOSAT and other agencies such as Copernicus-EMS and SERTIT to support emergency operations following the M7.7 Sagaing earthquake of 28 March 2025.
Myanmar polylines shapefile based on 'Myanmar District Boundaries MIMU v9.4' (Polygons of Myanmar District boundaries (Admin2), based on the latest MIMU Pcode version 9.4 . )
**ADAM ID: **sm\_us7000qddu Magnitude 5.4 earthquake at 10.0 depth occurred on Jul 18 2025 13km NE of Kyaukmyaung. The epicentre of the earthquake was located at latitude 22.6861 longitude 96.0157. It impacted 608891 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.
**ADAM ID: **sm\_us6000q5ps Magnitude 5.5 earthquake at 7.673 depth occurred on Apr 13 2025 3km SW of Phayasu. The epicentre of the earthquake was located at latitude 21.1538 longitude 96.0055. It impacted 821425 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.
**ADAM ID: **FL-20250804-MMR-00 A flood occurred on Aug 04 2025 in Myanmar. It affected an estimated 1494689 people and 750433 ha of cropland. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.
**ADAM ID: **FL-20250606-MMR-00 A flood occurred on Jun 06 2025 in Myanmar. It affected an estimated 354837 people and 245494 ha of cropland. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.
**ADAM ID: **sm\_us6000syg0 Magnitude 5.2 earthquake at 10.0 depth occurred on May 18 2026 22km S of Kyauktan. The epicentre of the earthquake was located at latitude 16.4431 longitude 96.3164. It impacted 4002128 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.
**ADAM ID: **sm\_us6000s65b Magnitude 5.9 earthquake at 62.69 depth occurred on Feb 03 2026 32km W of Sidoktaya. The epicentre of the earthquake was located at latitude 20.4657 longitude 93.9572. It impacted 70339 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.
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 Myanmar **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.3263 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0411 million km (12.5997%) - Unpaved roads: 0.0178 million km (5.4678%) About 0.2674 million km (81.9325%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0043 million km (1.6254%) 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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**ADAM ID: 1000970\_6** Cyclone (category 3) during the period May 11 2023-May 14 2023 in Myanmar. It impacted 2643150 people.
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.
Sea ports, harbours and ferry terminals. Useful for maritime logistics, supply-chain analysis, and coastal access 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: industrial=port, harbour, port, amenity=ferry_terminal, building=ferry_terminal - Part of the country exports on HDX by HOT
Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. 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, healthcare - Part of the country exports on HDX by HOT
A gridded raster showing the spatial distribution of population across Myanmar, dated 2030. Cell values represent population counts. Published by WorldPop (University of Southampton).
Gridded raster of population distribution for Myanmar, published by WorldPop (University of Southampton). Each cell value measures the population count within that grid cell. The data cover Myanmar for the year 2030.