India GIS data

geoBoundaries ADM1 boundaries for India: 1 units, representing 2011. Source: DataMeet India community, Election Commission of India.

uav imagery by WELL Labs, acquired 2025-08-31 to 2025-08-31, 3.1 cm per pixel, published on OpenAerialMap.

uav imagery by Bhuvan, acquired 2025-04-22 to 2025-04-23, 2.9 cm per pixel, published on OpenAerialMap.

uav imagery by Bhuvan, acquired 2025-04-22 to 2025-04-23, 2.9 cm per pixel, published on OpenAerialMap.

geoBoundaries ADM2 boundaries for India: 736 units, representing 2021. Source: Pathways Data Pvt. Ltd., lgdirectory.gov.in. Open Data Commons Open Database License 1.0

Natural Earth admin 0 countries ind at 1:10 million scale: public domain cartographic vector data built to make small-scale world, regional and country maps.

geoBoundaries ADM4 boundaries for India: 7152 units, representing 2019. Source: Pathways Data Pvt. Ltd., lgdirectory.gov.in. Open Data Commons Open Database License 1.0

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.

TSR gust footprint for Cyclonic Storm FANI - 2019-May 3 - Credit: Tropical Storm Risk/UCL Issued about 3 hours after it made landfall ~15km to the west of Puri in Odisha state, India. The gust footprint shows the magnitude and extent of the peak winds from FANI at landfall and immediately thereafter. Our model indicates that the peak gust at landfall was 150-160 mph and that gusts of 100 mph and over extend over a swathe width of ~100 km. The wind damage from FANI will almost certainly be severe. This damage will be exacerbated by heavy rains and an expected high storm surge. Unfortunately the forecast wind swathe for FANI is in line with the heavily populated cities of Bhubaneshwar and Cuttack.

**ADAM ID: **sm\_us6000qvpp Magnitude 6.5 earthquake at 10.0 depth occurred on Jul 28 2025 258 km WNW of Sabang, Indonesia. The epicentre of the earthquake was located at latitude 6.6995 longitude 93.1229. It impacted 0 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\_us6000pwz6 Magnitude 5.6 earthquake at 10.0 depth occurred on Mar 05 2025 26km N of Myothit. The epicentre of the earthquake was located at latitude 24.7433 longitude 94.4837. It impacted 353925 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\_us7000qw56 Magnitude 5.9 earthquake at 10.0 depth occurred on Sep 14 2025 17km E of Udalguri. The epicentre of the earthquake was located at latitude 26.761 longitude 92.2473. It impacted 1839955 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: **1001238\_12 Tropical depression during the period Nov 27 2025-Nov 29 2025 in Sri Lanka, India. The center of the storm was located near latitude 10.5 longitude 80.8. 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: **1001232\_11 Tropical storm during the period Oct 26 2025-Oct 29 2025 in India. The center of the storm was located near latitude 16.9 longitude 81.4. 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: **1001220\_2 Tropical storm during the period Oct 03 2025-Oct 03 2025 in India. The center of the storm was located near latitude 21.8 longitude 66.8. 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: **1001218\_6 Tropical storm during the period Oct 01 2025-Oct 02 2025 in India. The center of the storm was located near latitude 19.5 longitude 84.7. 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 India **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 4.8023 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.5281 million km (10.9979%) - Unpaved roads: 0.2874 million km (5.9838%) About 3.9868 million km (83.0183%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0218 million km (0.5461%) 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

No description yet.

No description yet.

No description yet.

No description yet.

No description yet.

**ADAM ID: 1000975\_37** Cyclone (tropical storm) during the period Jun 06 2023-Jun 15 2023 in India, Pakistan. It impacted 0 people.

**ADAM ID: 1001037\_6** Cyclone (tropical storm) during the period Dec 03 2023-Dec 04 2023 in India. It impacted 0 people.

**ADAM ID: **1001326\_5 Tropical storm during the period Sep 22 2026-Sep 23 2026 in India. The center of the storm was located near latitude 17.8 longitude 83.7. 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.

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.

# EOS-RS Damage Proxy Map: Dharali, India, Debris Flow, 05 Aug 2025, v0.9 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Damage Proxy Map (DPM) depicting areas that are likely damaged in Dharali, India, due to debris flow. 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 (07 Apr 2025 to 24 Jul 2025) and after (5 Aug 2025) the event. The map covers an area indicated by the white polygon. Damage is shown by colored pixels of 30m in size, where yellow to red indicates increasingly significant ground surface change before and after the event. Preliminary validation was conducted using news reports, ground-level images and videos in selected areas. This map should be used as a guidance to identify damaged areas, and may be less reliable over vegetated or mountainous areas. Scattered pixels over vegetated or mountainous areas may be false positives, and a lack of colored pixels over such areas may not mean no damage. This product contains modified Copernicus Sentinel data (2025), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS). **More map details and files:** **More map details and files:** leaflet/EOS-RS_202508_India_Dharali_Debris_Flow **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2025)

**ADAM ID: **sm\_us6000u0hz Magnitude 5.1 earthquake at 10.0 depth occurred on Oct 06 2026 36km NW of Bāgeshwar. The epicentre of the earthquake was located at latitude 30.1171 longitude 79.6148. It impacted 535428 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.