Bangladesh buildings and infrastructure data

Building footprints across the area: every feature tagged `building=*`, from residential homes to hospitals, schools, and places of worship. Useful for population estimation, damage assessment, and urban 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: building - Part of the country exports on HDX by HOT

Education facilities: schools, kindergartens, colleges, and universities tagged via `amenity` or `building`. Useful for school-access mapping, education planning, and resource allocation. 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, building - Part of the country exports on HDX by HOT

Satellite imagery derived shelter footprints of Rohingya refugee settlements in Cox's Bazar District, Bangladesh. Based on best available imagery acquired in February 2018.

In early 2019, UNICEF and the Cox's Bazar WASH sector identified a need to comprehensively map key WASH infrastructure using physical unique identifier (UUID) tags, in order to produce a database containing information about each piece of infrastructure across the camps. The objectives of this effort were to facilitate accurate, consistent spatial identification of unique infrastructure by all interested actors, to provide a baseline ‘snapshot’ of WASH infrastructure functionality at the time of coding, and to establish a common dataset to be used by all partners. The core infrastructure being covered by this exercise includes tubewells, latrines, and bathing facilities.

Kutupalong Rohingya Refugee Camp, Bangladesh. The structure footprint covers the camps directly affected by the fire of the 22nd March 2021 (Camp 8E, Camp 8W and Camp 9). The footprint is based on high resolution drone imagery from March 2020 (IOM) and has been processed by REACH/UNOSAT. The footprint includes all digitised structures and so includes bridges, shelters and smaller structures, such as latrines etc. This footprint is an early release of the full structure/shelter footprint for all camps and has not been QCed. Users should use accordingly. The full, QCed, post-processed structure/shelter footprint for all camps will be released in mid-April.

Summary: This file represents digitization work carried out by UNOSAT and REACH using IOM-NPM drone imagery from April 2021. All fields should be indicative, the 'Type' field indicates whether the structure was thought to be fully build at the time imagery was taken. Limitations: Digitised areas show covered spaces within the camps. These may be latrines, tubewells, bathing stations, shelters or larger structures (many of which are multiple shelter joined under one continuous roof). This is also the case in previous 'shelter' footprints. Bridges are not included in this dataset, but many can be found in the previous 2019 footprint. Imagery is known to have horizontal offset of up to a few meters in some areas but is generally accurate. Work is ongoing by REACH to manually update the footprints to ensure that they accurately represent features in the field. As future iterations and improvements to the footprints are made, additional versions will be released. The work has not yet been fully ground truthed and is based on expert interpretation of UAV imagery. In addition to relying on imagery interpretation, the footprints are bound by the limitations present in the UAV images that were utilized. Credits: Processing UNOSAT / REACH (2021) Drone Imagery Inputs, IOM-NPM (April 2021)

**UNOSAT code TC20230510BGD, GDACS Id: 1000970** This map illustrates satellite-based damage assessment in Saint Martin’s Island Cox’Bazar District, Chattogram Division detected by using a Pleiades very high-resolution satellite image acquired on 15 May 2023. Within the analyzed area, UNOSAT identified 211 affected and potentially affected structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents about 17% of the total number of structures affected in Saint Martin’s Island. 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 map illustrates satellite-detected possible damaged shelter structures in Kutupalong Refugee Camp, Palong Khali Union and Cox Bazar District, Chittagong Division, Bangladesh. The UNITAR-UNOSAT analysis used Pleiades satellite imagery acquired the 6th and the 7th June 2017 as post-images. The UNITAR-UNOSAT analysis identified 1,105 possible damaged shelter structures within the extent of the camp. Kindly note that the number of possible shelter damaged structures could have been under or overestimated in some areas due to the cloud cover and the delimitation of the camp extent. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR – UNOSAT.

This map illustrates satellite-detected potentially affected shelters in Nayapara refugee camp in Teknaf Union, Cox s Bazar District, and Chittagong Division of Bangladesh. About 12,500 people are registred in this camp. The UNITAR-UNOSAT analysis used a Pleiades satellite image acquired the 05th of June 2017 and could identify 560 possible damaged shelter structures within the extent of the camp. Kindly note that the number of damaged shelters could have been underestimated as some groups of shelter structures might be identified as one structure.This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

This map illustrates satellite-detected potentially affected shelters in Leda refugee camp in Nhilla Union, Cox s Bazar District, and Chittagong Division of Bangladesh. About 18,350 people are registered in this camp. The UNITAR-UNOSAT analysis used a Pleiades satellite image acquired 05 June 2017 and could identify 215 possible damaged shelter structures within the extent of the camp. Kindly note that the number of damaged shelters could have been underestimated as some groups of shelter structures might be identified as one structure. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

**UNOSAT code: FL20240502BGD, GDACS ID: 1001064** This map illustrates potentially damaged structures as observed by using a Pléiades satellite image acquired on 4 June 2024 at 10:31 Local Time. Within the analysis extent, UNOSAT identified 1,100 potentially damaged structures. 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: FL20240502BGD, GDACS ID: 1001064** This map illustrates potentially damaged structures as observed by using a Pléiades satellite image acquired on 4 June 2024 at 10:31 Local Time. Within the analysis extent, UNOSAT identified 30 damaged structures and 270 potentially damaged structures. 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: FL20240502BGD, GDACS ID: 1001064** This map illustrates potentially damaged structures as observed by using a Pléiades satellite image acquired on 4 June 2024 at 10:31 Local Time. Within the analysis extent, UNOSAT identified 64 damaged structures and 1,484 potentially damaged structures. 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: FL20240502BGD, GDACS ID: 1001064** This map illustrates potentially affected structures as observed by using a WorldView-2 satellite image acquired on 10 June 2024 at 10:42 Local Time. Within the analysis extent, UNOSAT identified 45 damaged structures and 185 potentially damaged structures. 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).

AI-derived building footprint polygons for Bangladesh from Microsoft's Global ML Building Footprints, in 53 quadkey tiles of line-delimited GeoJSON, uploaded 2026-02-23.