AndreaBozzo/ceres-open-data-index huggingface dataset:
Buildings of Ukraine

Building footprints across the region: 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](https://www.openstreetmap.org), a free, collaborative world map
- Tags: [building](https://wiki.openstreetmap.org/wiki/Key:building)
- Part of the [exports grid on HDX](https://data.humdata.org/organization/hot/) by [HOT](https://www.hotosm.org/)


Tags: facilities-infrastructure, geodata
Organization: Humanitarian OpenStreetMap Team (HOT)
Waterways of Ukraine

Water features across the region: 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](https://www.openstreetmap.org), a free, collaborative world map
- Tags: [waterway](https://wiki.openstreetmap.org/wiki/Key:waterway), [water](https://wiki.openstreetmap.org/wiki/Key:water), [natural](https://wiki.openstreetmap.org/wiki/Key:natural)
- Part of the [exports grid on HDX](https://data.humdata.org/organization/hot/) by [HOT](https://www.hotosm.org/)


Tags: geodata, hydrology, rivers
Organization: Humanitarian OpenStreetMap Team (HOT)
Waterways of Palestine, State of

Water features across the region: 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](https://www.openstreetmap.org), a free, collaborative world map
- Tags: [waterway](https://wiki.openstreetmap.org/wiki/Key:waterway), [water](https://wiki.openstreetmap.org/wiki/Key:water), [natural](https://wiki.openstreetmap.org/wiki/Key:natural)
- Part of the [exports grid on HDX](https://data.humdata.org/organization/hot/) by [HOT](https://www.hotosm.org/)


Tags: geodata, hydrology, rivers
Organization: Humanitarian OpenStreetMap Team (HOT)
Railways of Palestine, State of

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](https://www.openstreetmap.org), a free, collaborative world map
- Tags: [railway](https://wiki.openstreetmap.org/wiki/Key:railway)
- Part of the [exports grid on HDX](https://data.humdata.org/organization/hot/) by [HOT](https://www.hotosm.org/)


Tags: facilities-infrastructure, geodata, railways, transportation
Organization: Humanitarian OpenStreetMap Team (HOT)
Health Facilities of Palestine, State of

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](https://www.openstreetmap.org), a free, collaborative world map
- Tags: [amenity](https://wiki.openstreetmap.org/wiki/Key:amenity), [healthcare](https://wiki.openstreetmap.org/wiki/Key:healthcare)
- Part of the [exports grid on HDX](https://data.humdata.org/organization/hot/) by [HOT](https://www.hotosm.org/)


Tags: geodata, health, health facilities
Organization: Humanitarian OpenStreetMap Team (HOT)
Financial Services of Palestine, State of

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](https://www.openstreetmap.org), a free, collaborative world map
- Tags: [amenity](https://wiki.openstreetmap.org/wiki/Key:amenity)
- Part of the [exports grid on HDX](https://data.humdata.org/organization/hot/) by [HOT](https://www.hotosm.org/)


Tags: economics, geodata
Organization: Humanitarian OpenStreetMap Team (HOT)
Buildings of Palestine, State of

Building footprints across the region: 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](https://www.openstreetmap.org), a free, collaborative world map
- Tags: [building](https://wiki.openstreetmap.org/wiki/Key:building)
- Part of the [exports grid on HDX](https://data.humdata.org/organization/hot/) by [HOT](https://www.hotosm.org/)


Tags: crisis-opt-israel-hostilities, facilities-infrastructure, geodata
Organization: Humanitarian OpenStreetMap Team (HOT)
Buildings of South Sudan

Building footprints across the region: 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](https://www.openstreetmap.org), a free, collaborative world map
- Tags: [building](https://wiki.openstreetmap.org/wiki/Key:building)
- Part of the [exports grid on HDX](https://data.humdata.org/organization/hot/) by [HOT](https://www.hotosm.org/)


Tags: facilities-infrastructure, geodata
Organization: Humanitarian OpenStreetMap Team (HOT)
Djibouti: School Geolocation and Connectivity Status

Location, name, education level, and connectivity status for 428 schools in Djibouti, collected and validated by Giga and Djibouti Ministère de l’Éducation Nationale et de la Formation Professionnelle (MENFOP).

[Giga](https://giga.global/) is an ITU–UNICEF joint initiative to connect every school to the internet and every young person to information, opportunity and choice. Data was sourced from Giga Maps, the largest open and live global map of schools and their connectivity status: [maps.giga.global](https://maps.giga.global)

Tags: education, education facilities-schools
Organization: Giga
Market Functionality Index (MFI)

The Market Functionality Index (MFI) is designed by WFP to support the ‘Market assessment and risk identification’ and 'Market situation monitoring' processes of cash-based assistance operations. The full package helps WFP make informed transfer modality decisions and detect changes in market functionality over time.
The technical guidance provides the rationale for the MFI and describes the key dimensions that a market assessment should tackle: 1) Assortment of essential goods, 2) Availability, 3) Price, 4) Resilience of supply chains, 5) Market competition, 6) Infrastructure, 7) Services, 8) Food quality, and 9) Access and protection. It also describes the procedure to calculate the index.
The practical guidance provides simple steps for conducting a market assessment using the MFI package, including the questionnaires in several languages and other tools for data management and data exchange.
Sampling guidance provides guidance on the sampling methods and related practical decisions in a trader survey.
The FAQ document is organized in eight thematic areas: a) MFI package, b) goals and use; c) trader survey; d) goods relevant for the assessment; e) sampling; f) data collection; g) end-to-end data pipeline, and h) responsibilities.
The Market Functionality Index for Nutrition (MFI-N) is a new assessment tool that measures markets’ capacity to supply nutritious, diverse, safe, and fortified foods essential for healthy diets, while identifying critical bottlenecks. Implemented alongside WFP’s corporate Market Functionality Index (MFI), the MFI-N generates nutrition-specific insights directly relevant to programmatic decision-making. This document summarizes evidence from seven pilot countries. 

Tags: food security, markets
Organization: WFP - World Food Programme
Venezuela (Bolivarian Republic of) - Subnational Administrative Boundaries

Venezuela (Bolivarian Republic of) administrative level 0-3 boundaries (COD-AB) dataset version 01.  
  
This dataset is structured into 3 levels:  
- Admin 1: 25 State  
- Admin 2: 335 Municipality  
- Admin 3: 1134 Parish (estados, municipios, parroquias)  
  
  
Dates associated with this dataset:  
- 17 January 2025: dataset reviewed for accuracy and completeness  
- 23 February 2021: valid for use by the humanitarian community  
- 02 May 2018: last edit to the dataset before publishing  
- 02 May 2018: boundaries created by the source  
  
Contributed by [OCHA Venezuela](https://data.humdata.org/organization/ochavenezuela).  
Quality assured, configured, and published by [OCHA Field Information Services (FIS)](https://data.humdata.org/organization/ocha-fiss) and [HDX](https://data.humdata.org/organization/hdx).  
  
Part of the dataset: [Global Subnational Administrative Boundaries](https://data.humdata.org/dataset/cod-ab-global)

Tags: administrative boundaries-divisions, crisis-venezuela-earthquake, gazetteer, geodata
Organization: OCHA Field Information Services Section (FISS)
Venezuela (Bolivarian Republic of): Planet Road Surface, Width, and Passability Data

This dataset provides AI-derived road surface type, road width, road surface change
(unpaved to paved), and a Humanitarian Passability Index (HPI) using
PlanetScope satellite imagery (2020 and 2024).

The analysis focuses on arterial roads classified in OSM as motorway, trunk,
primary, and secondary, including their link classes. These road types form
the backbone of long-distance, high-capacity transportation and national connectivity.

For more details on methods:
https://arxiv.org/abs/2512.04092


Relationship to the Previous Mapillary-Based Dataset

HeiGIT previously released a global dataset based on Mapillary street-level imagery.

- The Mapillary dataset provides detailed ground-level information but has uneven coverage.
- The PlanetScope-based dataset provides near-complete and globally consistent coverage along arterial roads.


Accuracy and Data Quality

The PlanetScope surface model achieved 89.2% accuracy, outperforming OSM surface tags (64.7%).


Summary of Road Statistics

Approximately 0.0274 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0248 million km (90.5907%)
- Unpaved roads: 0.0008 million km (2.9575%)

A total of 0.0059 million km (21.4178%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0256 million km
(436.7774%) of this missing data.


AI-Derived Attributes Included

- DL Surface Prediction 2020 – Paved / Unpaved / Unknown
- DL Surface Prediction 2024 – Paved / Unpaved / Unknown
- Road Surface Change (2020-2024) – Indicator of transitions from unpaved to paved
- Road Width Class (1-3):
  Class 1 (<3.5 m): light vehicles only; high blockage risk
  Class 2 (3.5-5.5 m): single-lane heavy truck; alternating flow
  Class 3 (>5.5 m): two-lane heavy truck; unimpeded flow

- Humanitarian Passability Index (HPI)
  Combines road surface and width into a logistical accessibility class:
  Score 1 (highest passability) to Score 6 (lowest passability)


Included OSM Attributes

- Road Segment ID
- highway classification
- surface tag
- OSM road length (meters)


Geographic Attributes

- country_iso_a2
- continent


This dataset integrates OSM attributes with deep-learning predictions generated
from PlanetScope imagery (2020 and 2024).

Explore more HeiGIT datasets on HDX:
https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology

More information:
https://heigit.org/

Contact: communications@heigit.org

Tags: crisis-venezuela-earthquake, development, humanitarian access, indicators, logistics, openstreetmap, poverty, roads, rural, services, socioeconomics, sustainable development, sustainable development goals-sdg, transportation, urban
Organization: HeiGIT (Heidelberg Institute for Geoinformation Technology)
Venezuela (Bolivarian Republic of): Copernicus Global Human Settlement Layer (GHSL)

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.

Tags: crisis-venezuela-earthquake, facilities-infrastructure, populated places-settlements, population
Organization: Copernicus
Financial Services of Ukraine

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](https://www.openstreetmap.org), a free, collaborative world map
- Tags: [amenity](https://wiki.openstreetmap.org/wiki/Key:amenity)
- Part of the [exports grid on HDX](https://data.humdata.org/organization/hot/) by [HOT](https://www.hotosm.org/)


Tags: economics, geodata
Organization: Humanitarian OpenStreetMap Team (HOT)
Venezuela (Bolivarian Republic of) - Age and Gender Structures (2015-2030)

Estimates of total number of people per grid square broken down by gender and age groupings (including 0-1 and by 5-year up to 90+) for Venezuela (Bolivarian Republic of), R2025A version v1. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are estimated number of male, female or both in each age group per grid square.&nbsp;  
  
More information can be found in the [Release Statement](https://data.worldpop.org/repo/prj/Global_2015_2030/R2025A/doc/Global2_Release_Statement_R2025A_v1.pdf)  
  
Please note that these data represent 2025 Alpha release versions, constructed in September 2025  
  
**File Descriptions:**  
  
_{iso} {gender} {age group} {year} {type} {resolution}.tif_  
  
_iso_  
  
Three-letter country code  
  
_gender_  
  
m = male, f= female, t = both genders  
  
_age group_  
  
*   00 = age group 0 to 12 months  
*   01 = age group 1 to 4 years  
*   05 = age group 5 to 9 years  
*   90 = age 90 years and over  
  
_year_  
  
Year that the population represents  
  
_type_  
  
CN = [Constrained](https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained/)  
  
_resolution_  
  
Resolution of the data e.q. 100m = 3 arc (approximately 100m at the equator)

Tags: baseline population, crisis-venezuela-earthquake, demographics, gender and age disaggregated data-gadd, geodata
Organization: WorldPop
Venezuela (Bolivarian Republic of) - Spatial Distribution of Population (2015-2030)

Estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.  
  
More information can be found in the [Release Statement](https://data.worldpop.org/repo/prj/Global_2015_2030/R2025A/doc/Global2_Release_Statement_R2025A_v1.pdf)  
  
Please note that these data represent 2025 Alpha release versions, constructed in September 2025

Tags: baseline population, crisis-venezuela-earthquake, geodata
Organization: WorldPop
Venezuela (Bolivarian Republic of) - Internal Displacements (New Displacements) – IDPs

The [Global Internal Displacement Database (GIDD)](http://www.internal-displacement.org/database/displacement-data), maintained by the [Internal Displacement Monitoring Centre (IDMC)](https://www.internal-displacement.org/), provides comprehensive, validated annual estimates of internal displacement worldwide. It defines internally displaced persons (IDPs) in line with the [1998 Guiding Principles](https://www.internal-displacement.org/internal-displacement/guiding-principles-on-internal-displacement/), as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border.


The GIDD tracks two primary metrics: "People Displaced" or population "Stock" figures, which represent the total number of people living in displacement at year-end, and "New Displacement," which counts new displacement incidents (population Flows) rather than individual people, accounting for potential multiple displacements by the same person. This dataset serves as a crucial resource for understanding long-term trends and validated displacement figures globally. For further detailed information and complete API specifications, users are encouraged to consult the official documentation at https://www.internal-displacement.org/database/api-documentation/.


"Internally displaced persons - IDPs" refers to the number of people living in displacement as of the end of each year.


"Internal displacements (New Displacements)" refers to the number of new cases or incidents of displacement recorded, rather than the number of people displaced. This is done because people may have been displaced more than once.


Tags: crisis-venezuela-earthquake, displacement, internally displaced persons-idp, natural disasters
Organization: Internal Displacement Monitoring Centre (IDMC)
Venezuela (Bolivarian Republic of): Google Building Footprints

This large-scale open dataset consists of outlines of buildings derived from high-resolution 50 cm satellite imagery. It contains 1.8B building detections in Africa, Latin America, Caribbean, South Asia and Southeast Asia. The inference spanned an area of 58M km² and was carried out during May 2023.  
  

Each building in this dataset includes a polygon describing its footprint size on the ground and a confidence score indicating how sure this is a building. There is no information about the type of building, its street address, or any details other than its geometry.  
  

The HDX team curates these datasets at [Google Open Buildings (by HDX)](https://data.humdata.org/organization/google-open-buildings) using international boundaries from the UN Geospatial Hub.  
  

For more details, see the official website of the [Open Buildings dataset](https://sites.research.google/gr/open-buildings/).

Tags: crisis-venezuela-earthquake, facilities-infrastructure, geodata
Organization: Google Open Buildings (by HDX)
Populated Places of Uganda

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](https://www.openstreetmap.org), a free, collaborative world map
- Tags: [place](https://wiki.openstreetmap.org/wiki/Key:place), [landuse](https://wiki.openstreetmap.org/wiki/Key:landuse)
- Part of the [exports grid on HDX](https://data.humdata.org/organization/hot/) by [HOT](https://www.hotosm.org/)


Tags: crisis-ebola-bundibugyo-virus-disease, geodata, populated places-settlements
Organization: Humanitarian OpenStreetMap Team (HOT)
Education Facilities of Burundi

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](https://www.openstreetmap.org), a free, collaborative world map
- Tags: [amenity](https://wiki.openstreetmap.org/wiki/Key:amenity), [building](https://wiki.openstreetmap.org/wiki/Key:building)
- Part of the [exports grid on HDX](https://data.humdata.org/organization/hot/) by [HOT](https://www.hotosm.org/)


Tags: education facilities-schools, geodata
Organization: Humanitarian OpenStreetMap Team (HOT)
Airports of Sudan

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](https://www.openstreetmap.org), a free, collaborative world map
- Tags: [aeroway](https://wiki.openstreetmap.org/wiki/Key:aeroway), [building](https://wiki.openstreetmap.org/wiki/Key:building), [emergency](https://wiki.openstreetmap.org/wiki/Key:emergency)
- Part of the [exports grid on HDX](https://data.humdata.org/organization/hot/) by [HOT](https://www.hotosm.org/)


Tags: aviation, facilities-infrastructure, geodata
Organization: Humanitarian OpenStreetMap Team (HOT)