Bangladesh GIS data

**UNOSAT code: FL20240502BGD, GDACS ID: 1102700** This map illustrates satellite-detected surface waters in Chhatak Upazila, Sunamganj District, Sylhet division, as observed from a GLOBAL satellite image acquired on 26 June 2024 at 05:58 local time. Within the analysed area, about 20 km² of land appears to be flooded. Furthermore, within the analysed area, about 3,350 structures are identified as potentially affected by the floods (30% of the total structures in the analysed area), along with about 40 km of roads, as of 26 June 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: FL20240502BGD, GDACS ID: 1102700** This map illustrates satellite-detected surface waters in Sunamganj Sadar and Bishwambarpur Upazilas, Sunamganj District, Sylhet Division, as observed from a Pleiades satellite image acquired on 26 June 2024 at 11:01 local time. Within the analysed area, about 120 km² of land appears to be flooded. Furthermore, within the analysed area, about 25,800 structures are identified as potentially affected by the floods (42% of the total structures in the analysed area), along with about 260 km of roads, as of 26 June 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: FL20240502BGD, GDACS ID: 1102700** This map illustrates the aggregated floods using Sentinel-1 images acquired between 22 to 29 June 2024 and between 4 to 11 July 2024 using an automated analysis with machine learning method. Within the analysed area of about 140,000 km², a total of about 8,700 km² of land appear to be affected by flood waters. Based on Worldpop spatial demographic data, about 7.7 million people are exposed or living close to flooded areas. Between these two periods, the floodwaters have increased about 2,900 km2. The increase has been mainly observed within Rangpur and Rajshahi divisions accordingly. 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). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to the backscattering properties of the radar signal.

**UNOSAT code: FL20240502BGD, GDACS ID: 1102700** This map illustrates the aggregated floods using Sentinel-1 images acquired between 16 to 23 July 2024 and between 4 to 11 July 2024 using an automated analysis with a machine learning method. Within the analysed area of about 140,000 km², a total of about 8,530 km² of land appears to be affected by flood waters. Based on Worldpop spatial demographic data, about 8 million people are exposed or living close to flooded areas. Between these two periods, the floodwaters have increased about 130 km2. The increase has been mainly observed within the Rajshahi, Sylhet, Rangpur and Dhaka divisions accordingly. 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). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to the backscattering properties of the radar signal.

**UNOSAT code: FL20240825BGD** This map illustrates the aggregated floods using Sentinel-1 images acquired between 18 to 26 August 2024 and between 28 August & 4 September 2024 using an automated analysis with a machine learning method. Within the analysed area of about 140,000 km², a total of about 8,100 km² of land appears to be affected by flood waters. Based on Worldpop spatial demographic data, about 6.2 million people are exposed or living close to flooded areas. Over these two periods, floodwaters receded by approximately 8,700 km². 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). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to the backscattering properties of the radar signal.

**UNOSAT code: FL20220525BGD** This map illustrates satellite-detected surface waters in Rangpur Division, Bangladesh as observed from a Sentinel-1 images acquired on 21 Jun. 2022 at 18:05 local time and using an automated analysis with machine learning method. Within the analyzed area of about 12,400 km2, about 1,200 km2 of lands appear to be flooded. 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 Kurigram with ~537,000 people, and Gaibandha with ~355,000 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: FL20220525BGD** This map illustrates satellite-detected surface waters in Sylhet and Sunamganj Districts, Sylhet Division, Bangladesh as observed from a Chaohu-1 image acquired on 25 May 2022 at 22:23 local time. Within the analyzed area of about 730 km2, about 420 km2 of lands appear to be flooded. In this area, about 300 km2 of croplands and 70 km2 of herbaceous wetland appear to be likely affected by the flood waters. Based on Worldpop population data and the detected surface waters, about 307,000 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: FL20210729BGD** This map illustrates satellite-detected surface waters in Chittagong division, Bangladesh as observed from a Sentinel-1 image acquired on 30 July 2021 at 05:33 local time Within the analyzed area of about 18,700 km2 and, about 852 km2 of lands appear to be flooded. The water extent appears to have decreased of about 18km2 since 27 July 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 Cox's Bazar with ~425,000 people, Chittagong with ~392,300 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.

A massive fire swept through three IOM-managed sites on 22 March 2021 displacing Rohingya refugees and causing catastrophic damage in the world’s largest refugee camp, Cox’s Bazar, Bangladesh.

NPM Bangladesh has produced a number of tools based on its regular data collection activities. The package of July 2019 is based on NPM Site Assessment 15 (as of 24 June 2019) and NPM most updated drone imagery (as of 23 January 2019). Here below, the complete package by camp: SW Map package KMZ file Drone image The full image and shapefiles are available at this link.

The majhee block system represents an important aspect of communities within the Rohingya refugees settled in collective or camp-like settings in Cox's Bazar district. A majhee is a community leader belonging to the Rohingya refugee population, while a block is the area for which he is responsible. The NPM majhee blocks mapping exercise was first conducted during NPM Baseline 9, between 5 and 20 February 2018. As part of the majhee interview process (key informants), enumerators walked the perimeter of each majhee block with guidance from the KI. As they walked, the field team traced their path, marking up the boundary on the tablet or paper map. Upon returning to NPM office, details of these boundaries were finalized on the paper maps. The paper maps were then received by the NPM digitizing team. This team carefully digitized the Mahjee zone perimeters in GIS, using high-resolution NPM UAV imagery as an underlying reference. Boundaries were assigned the NPM Block_ID attribute, which represents a unique identifier for each Site Assessment Location. In this manner, boundaries can be uniquely linked back to a mahjee. The majhee blocks mapping exercise is now embedded in NPM regular assessment activities (baseline and site assessment), hence regularly updated with a frequency of approximately three weeks. The majhee block system is not an official form of governance. The scope of this exercise is purely descriptive and not prescriptive. Names and boundaries adopted in this exercise do not imply official endorsement or acceptance by IOM.

**UNOSAT code: FL20220525BGD** This map illustrates satellite-detected surface waters in Sylhet, Mymensingh, Dhaka, and Chattogram Divisions, Bangladesh as observed from a Sentinel-1 images acquired on 28 May 2022 at 18:04 local time and using an automated analysis with machine learning method. Within the analyzed area of about 12,000 km2, about 4,500 km2 of lands appear to be flooded. In this area, about 3,400 km2 of croplands and 1,000 km2 of herbaceous wetland appear to be likely affected by the flood waters. 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 Sunamganj with ~935,000 people and Kishoreganj with ~779,000 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.

**ADAM ID: **sm\_us6000rpht Magnitude 5.5 earthquake at 10.0 depth occurred on Nov 21 2025 3km SE of Kaliganj. The epicentre of the earthquake was located at latitude 23.8942 longitude 90.5788. It impacted 11224382 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 Bangladesh **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.3072 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0337 million km (10.9845%) - Unpaved roads: 0.0178 million km (5.798%) About 0.2557 million km (83.2175%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0017 million km (0.6463%) 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: 1001034\_4** Cyclone (tropical storm) during the period Nov 16 2023-Nov 17 2023 in India, Bangladesh. It impacted 0 people.

**ADAM ID: 1000976\_2** Cyclone (tropical storm) during the period Jun 09 2023-Jun 10 2023 in Bangladesh, Myanmar, India. It impacted 0 people.

**ADAM ID: 1001029\_6** Cyclone (category 1) during the period Oct 23 2023-Oct 24 2023 in Bangladesh, India, Myanmar. It impacted 3946522 people.

**UNOSAT code: FL20220525BGD** This map illustrates satellite-detected surface waters in Rajshahi, Rangur, Mymensingh, Dhaka and Khulna Divisions, Bangladesh as observed from a Sentinel-1 images acquired on 21 Jun. 2022 at 18:05 local time and using an automated analysis with machine learning method. Within the analyzed area of about 20,400 km2, about 2,950 km2 of lands appear to be flooded. Water extent appears to have increased of about 2,360 km2 since 9 Jun. 2022. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the division of Rajshahi with ~1,335,000 people, Dhaka with 674,000 people,and Mymensingh with ~ 640,000 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.

Cultural and historic places: museums, galleries, theatres, places of worship, monuments, and historic sites tagged via `tourism`, `amenity`, `historic`, or `heritage`. Useful for heritage protection, cultural mapping, and tourism context. 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: tourism, amenity, historic, heritage - Part of the country exports on HDX by HOT

**UNOSAT code: FL20220525BGD** This map illustrates satellite-detected surface waters in Sylhet, Mymensingh, Dhaka, and Chattogram Divisions, Bangladesh as observed from a RCM-1 images acquired on 19 Jun. 2022 at 18:02 local time. Within the analyzed area of about 22,300 km2, about 9,500 km2 of lands appear to be flooded. Water extent appears to have increased of about 2,150 km2 since the period between 25 to 28 May 2022. 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 Sunamganj with ~1,822,000 people and Sylhet with ~1,550,000 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: FL20220525BGD** This map illustrates satellite-detected surface waters in Sylhet, Mymensingh, Dhaka, and Chattogram Divisions, Bangladesh as observed from a Sentinel-1 images acquired on 26 May 2022 at 05:47 local time and using an automated analysis with machine learning method. Within the analyzed area of about 16,000 km2, about 6,800 km2 of lands appear to be flooded. In this area, about 5,400 km2 of croplands and 1,270 km2 of herbaceous wetland appear to be likely affected by the flood waters. 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 Sunamganj with ~1,700,000 people and Sylhet with ~1,400,000 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: FL20210729BGD** This map illustrates satellite-detected surface waters in Chittagong division, Bangladesh as observed from a Sentinel-1 image acquired on 11 August 2021 at 05:48 local time. Within the analyzed area of about 18,700 km2 and, about 754 km2 of lands appear to be flooded. The water extent appears to have decreased of about 119 km2 since 8 August 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 Chittagong with ~ 342,690 people. and Cox's Bazar with ~ 244,000 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.

Land cover of Bangladesh for the year 2015 based on SPOT 6m multispectral image segmentation and interpretation. Land cover classes are defined using Land Cover Classification System (LCCS) and are based on the National Land Representation system (NLRS). **Data publication:** 2018-06-06 **Contact points:** Resource Contact: [Bangladesh Forest Information System](mailto:bfis.rims.fd@gmail.com) Metadata Contact: [Matieu Henry](mailto:Matieu.Henry@fao.org) **Data lineage:** Data quality information not available **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:** Land_Cover_Bangladesh_2015_classes.csv Bangladesh Forest Department

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.

geoBoundaries ADM3 boundaries for Bangladesh: 544 units, representing 2020. Source: Bangladesh Bureau of Statistics (BBS), OCHA ROAP.