AndreaBozzo/ceres-open-data-index huggingface dataset:
Argentina - Human Development Indicators

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities.
The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population.
The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

Tags: demographics, development, education, gender, health, indicators, socioeconomics
Organization: UNDP Human Development Reports Office (HDRO)
United Arab Emirates - Human Development Indicators

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities.
The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population.
The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

Tags: demographics, development, education, gender, health, indicators, socioeconomics
Organization: UNDP Human Development Reports Office (HDRO)
Andorra - Human Development Indicators

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities.
The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population.
The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

Tags: demographics, development, education, gender, health, indicators, socioeconomics
Organization: UNDP Human Development Reports Office (HDRO)
Angola - Human Development Indicators

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities.
The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population.
The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

Tags: demographics, development, education, gender, health, indicators, socioeconomics
Organization: UNDP Human Development Reports Office (HDRO)
Nigeria: 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 0.0619 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0532 million km (85.9411%)
- Unpaved roads: 0.0054 million km (8.789%)

A total of 0.0146 million km (23.5901%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0587 million km
(401.5674%) 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: 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)
Denmark: 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 0.0134 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0133 million km (99.2665%)
- Unpaved roads: 0.0 million km (0.0%)

A total of 0.0 million km (0.0048%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0133 million km
(2071631.2594%) 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: 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)
United States of America: Road Surface Data

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM),
distinguishing between paved and unpaved roads across the region.

Surface information 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:
"United States of America: **Planet** Road Surface".
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 111.7039 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 36.2779 million km (32.4768%)
- Unpaved roads: 13.31 million km (11.9154%)

About 72.6432 million km
(65.0319%) of roads lack surface information in OSM.

To reduce this gap, the Mapillary-derived surface dataset provides an additional
5.0864 million km
(7.0019%) of surface information.


Intended Use

This dataset supports transportation planning, infrastructure assessment,
climate emissions analysis, 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: DL-estimated length (meters)
- DL_mean_timestamp: Mean timestamp of predictions


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

Tags: development, humanitarian access, indicators, logistics, openstreetmap, poverty, roads, services, socioeconomics, sustainable development, sustainable development goals-sdg, transportation, urban
Organization: HeiGIT (Heidelberg Institute for Geoinformation Technology)
Geodata of Damage and Displaced Persons Visible in Satellite Imagery. Kathmandu, Nepal - 27 April 2015

UNITAR/UNOSAT analyzed satellite imagery collected 27 April 2015 by the Pleiades satellite over the city of Kathmandu, Nepal. Damaged structures and the locations of displaced persons were identified. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR/UNOSAT.

Tags: earthquake-tsunami, geodata
Organization: United Nations Satellite Centre (UNOSAT)
China, Hong Kong Special Administrative Region - Human Development Indicators

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities.
The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population.
The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

Tags: demographics, development, education, gender, health, indicators, socioeconomics
Organization: UNDP Human Development Reports Office (HDRO)
Geodata of Damage Assessment of Muchok Area, Western Region, Nepal

This map illustrates satellite-detected damage and destruction in the Muchok area of Nepal. Located roughly two to seven kilometers west of the 25 April 2015 earthquake epicenter, the Muchok area was significantly impacted by this event. Using satellite imagery acquired 29 April 2015 UNITAR / UNOSAT identified a total of 299 affected structures in this area. Approximately 162 of these were destroyed, 66 severely damaged, 38 moderately damaged, and 33 possibly damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.

Tags: earthquake-tsunami, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Jordan: 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 0.0067 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0065 million km (96.7966%)
- Unpaved roads: 0.0 million km (0.0224%)

A total of 0.0038 million km (57.1132%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0065 million km
(169.5213%) 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: 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)