Bangladesh GIS data

uav imagery by Geo-Planning for Advanced Development (GPAD), acquired 2026-06-01 to 2026-06-02, 3.5 cm per pixel, published on OpenAerialMap.

uav imagery by Geo-Planning for Advanced Development (GPAD), acquired 2026-09-06 to 2026-09-07, 3.5 cm per pixel, published on OpenAerialMap.

uav imagery by Geo-Planning for Advanced Development (GPAD), acquired 2026-09-06 to 2026-09-07, 3.5 cm per pixel, published on OpenAerialMap.

uav imagery by Geo-Planning for Advanced Development (GPAD), acquired 2026-09-06 to 2026-09-07, 3.5 cm per pixel, published on OpenAerialMap.

uav imagery by Geo-Planning for Advanced Development (GPAD), acquired 2026-08-31 to 2026-09-01, 3.5 cm per pixel, published on OpenAerialMap.

uav imagery by Geo-Planning for Advanced Development (GPAD), acquired 2026-08-30 to 2026-08-31, 3.5 cm per pixel, published on OpenAerialMap.

uav imagery by Geo-Planning for Advanced Development (GPAD), acquired 2026-09-05 to 2026-09-06, 3.5 cm per pixel, published on OpenAerialMap.

satellite imagery by Geo-Planning for Advanced Development (GPAD), acquired 2026-08-28 to 2026-08-29, 3.5 cm per pixel, published on OpenAerialMap.

Here we provide poverty data created using Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates and aggregated mobile phone data applied to geolocated household survey data on poverty from the DHS wealth index (2011), the Progress out of Poverty Index (2014), and household income (2013). Citation: Steele, J. E. et al. Mapping poverty using mobile phone and satellite data. J. R. Soc. Interface 14, 20160690 (2017). Online here: http://rsif.royalsocietypublishing.org/content/14/127/20160690

Open and free data for assessing the human presence on the planet. The Global Human Settlement Layer (GHSL) project produces global spatial information, evidence-based analytics, and knowledge describing the human presence on the planet. The GHSL relies on the design and implementation of spatial data processing technologies that allow automatic data analytics and information extraction from large amounts of heterogeneous geospatial data including global, fine-scale satellite image data streams, census data, and crowd sourced or volunteered geographic information sources. The JRC, together with the Directorate-General for Regional and Urban Policy (DG REGIO) and Directorate-General for Defence Industry and Space (DG DEFIS) are working towards a regular and operational monitoring of global built-up and population based on the processing of Sentinel Earth Observation data produced by European Copernicus space program. In addition, the EU Agency for the Space Programme (EUSPA) undertakes activities related to user uptake of data, information and services.

This dataset contains the following administrative boundaries: ADM0, ADM1, ADM2, ADM3, ADM4. Produced and maintained since 2017, the geoBoundaries Global Database of Political Administrative Boundaries Database www.geoboundaries.org is an open license, standardized resource of boundaries (i.e., state, county) for every country in the world.

NPM Bangladesh has produced a number of tools based on its regular data collection activities and drone flights. The package of January 2019 is based on NPM Baseline Assessment 14 (as of 19 December 2018) 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.

Bangladesh administrative level 0-3 boundaries (COD-AB) dataset version 03. This dataset is structured into 3 levels: - Admin 1: 8 Division - Admin 2: 64 District - Admin 3: 507 Upazila Note: The normal administrative level 3 feature type(s) are Upazila (495 features) and City Corporation (12 units). Dates associated with this dataset: - 01 June 2025: dataset reviewed for accuracy and completeness - 21 May 2023: valid for use by the humanitarian community - 01 June 2025: last edit to the dataset before publishing - 01 January 2010: 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

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.

This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long

# EOS-RS Flood Proxy Map: Bangladesh, Floods, 10 Aug 2023, v0.5 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Flood Proxy Map (FPM) depicting areas that are likely flooded in Bangladesh due to heavy rains. 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 (29 July 2023) and during (10 Aug 2023) the event. The image covers an area indicated by the large white polygon. Each light blue pixel measures about 30 meters across. Preliminary validation was done in selected areas using high resolution optical imagery and news reports. This flood proxy map should be used as a guide to identify areas that are likely flooded, and is less reliable over urban and vegetated areas. The product contains modified Copernicus Sentinel data (2023), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), using the Advanced Rapid Imaging and Analysis (ARIA) system originally developed at NASA's Jet Propulsion Laboratory, California Institute of Technology, and modified at EOS-RS. **More map details and files:** **More map details and files:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Advanced Rapid Imaging and Analysis (ARIA). Contains modified Copernicus Sentinel data (2023).

This dataset contains annual, 100m-resolution above ground biomass (AGB) estimates from CTrees, a NASA-affiliated forest-carbon monitoring organization, clipped to this country's boundary from CTrees' global AGB raster. Pixel values are stored as int16, scaled x10 relative to the true value in megagrams per hectare (Mg/ha) -- divide by 10 to get Mg/ha. See the caveats field and resource description for details on how this scale factor is recorded and why it isn't applied automatically by most tools.

Water features across the area: rivers, streams, canals, lakes, ponds, and water bodies tagged with `waterway`, `water`, or `natural=water`. Useful for flood-risk modelling, watershed analysis, and environmental basemap 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: waterway, water, natural - Part of the country exports on HDX by HOT

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

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

Road network across the area tagged `highway=*`: motorways, residential streets, service roads, footways, and trails. Useful for routing, accessibility analysis, and evacuation 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: highway - 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

Settlements and residential areas: named cities, towns, villages, suburbs, and residential land (`place=*` and `landuse=residential`). Useful for demographic context, settlement-level targeting, and population 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: place, landuse - Part of the country exports on HDX by HOT

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

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

Railway lines and stations tagged `railway=*`: rail lines, tram tracks, and stations. Useful for transport accessibility analysis and basemap 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: railway - 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

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

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.

Source: Environment, Forest and Climate Change (EFCC) and Country Investment Plan (CIP) The EFCC-CIP responds to the urgent need to address environmental degradation and to improve the country’s ability to meet the threats posed by climate change. **Data publication:** 2018-01-01 **Contact points:** 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:** Bangladesh Country Investment Plan (CIP) for Environment, Forestry and Climate Change (EFCC) Bangladesh Forest Department BFIS e-Multimedia/FAO/USAID video on Bangladesh Country Investment Plan (CIP) for Environment, Forestry and Climate Change (EFCC)

Accessibility to major cities dataset is modeled as raster-based travel time/cost analysis, computed for the largest cities (>500k habitants) in the country. This 500m resolution raster dataset is part of FAO's Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (or optimal location). **Data publication:** 2021-10-18 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Justeen De Ocampo](mailto:justeen.deocampo@fao.org) **Data lineage:** lineage statement Produced using OpenStreetMap data for roads, railways, rivers; UN Map country border; The HydroSHEDS 15' resolution GRID for the DEM, GHSL - Global Human Settlement Layer. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped raster TIF file for accessibility to major cities (Bangladesh ~ 500m)

Accessibility to major maritime ports is modeled as raster-based travel time/cost analysis. The following ports are included: Port of Dhaka Port of Chittagong Port of Mongla This 500m resolution raster dataset is part of FAO's Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-10-18 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Justeen De Ocampo](mailto:justeen.deocampo@fao.org) **Data lineage:** lineage statement Produced using OpenStreetMap data for roads, railways, rivers; UN Map country border; The HydroSHEDS 15' resolution GRID for the DEM, GHSL - Global Human Settlement Layer. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped raster TIF file for accessibility to ports (Bangladesh ~ 500m)

Accessibility to regional cities dataset is modeled as raster-based travel time/cost analysis, computed for the largest cities surrounding the country. The following cities are included: City - Population Mandalay - 2 442 763 Kolkata - 16 681 589 Patna - 2 803 131 Asansol - 1 317 369 Dhanbad - 1 210 235 This 500m resolution raster dataset is part of FAO's Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (or optimal location). **Data publication:** 2021-10-18 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Justeen De Ocampo](mailto:justeen.deocampo@fao.org) **Data lineage:** lineage statement Produced using OpenStreetMap data for roads, railways, rivers; UN Map country border; The HydroSHEDS 15' resolution GRID for the DEM, GHSL - Global Human Settlement Layer. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped raster TIF file for accessibility to regional cities (Bangladesh ~ 500m)

The raster dataset represents top location score areas suitable for rice storage, filtered by exclusive criteria: access to finance, distance to major roads, and access to IT (mobile broadband connection). Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 10km buffer radius. • Major roads - approx. 2km buffer radius. Access to IT is characterized by applying the mobile broadband coverage map. The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-10-15 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Justeen De Ocampo](mailto:justeen.deocampo@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2020. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for rice final location (Bangladesh - ~ 500 m)

The raster dataset represents top location score areas suitable for vegetable storage, filtered by exclusive criteria: access to finance, distance to major roads, and access to IT (mobile broadband connection). Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 10km buffer radius. • Major roads - approx. 2km buffer radius. Access to IT is characterized by applying the mobile broadband coverage map. The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-10-15 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Justeen De Ocampo](mailto:justeen.deocampo@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2020. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for vegetable final location (Bangladesh - ~ 500 m)