AndreaBozzo/ceres-open-data-index huggingface dataset:
Côte d'Ivoire: 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 0.0126 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0091 million km (72.3946%)
- Unpaved roads: 0.003 million km (24.1239%)

A total of 0.0 million km (0.3811%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0121 million km
(25324.8992%) 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)
Gibraltar: 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 0.0 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0 million km (83.5781%)
- Unpaved roads: 0.0 million km (0.0%)

A total of 0.0 million km (5.6462%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0 million km
(1480.2584%) 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)
Marshall Islands: 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 0.0001 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0 million km (84.4549%)
- Unpaved roads: 0.0 million km (1.7104%)

A total of 0.0 million km (73.5939%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0001 million km
(117.0821%) 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)
Guinea-Bissau: 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 0.0009 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0006 million km (71.9488%)
- Unpaved roads: 0.0002 million km (25.606%)

A total of 0.0001 million km (6.0343%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0009 million km
(1616.668%) 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)
Vanuatu: 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 0.0003 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0002 million km (67.2081%)
- Unpaved roads: 0.0001 million km (27.2966%)

A total of 0.0001 million km (42.4715%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0003 million km
(222.5136%) 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)
Zambia: 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 0.0116 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0086 million km (74.3678%)
- Unpaved roads: 0.0025 million km (21.3081%)

A total of 0.0012 million km (10.3707%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0111 million km
(922.5639%) 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)
American Samoa - Education Indicators

Education indicators for American Samoa.

Contains data from the UNESCO Institute for Statistics [bulk data service](http://data.uis.unesco.org) covering the following categories: SDG 4 Global and Thematic (made 2026 February), Other Policy Relevant Indicators (made 2026 February), Demographic and Socio-economic (made 2026 February)

Tags: demographics, education, indicators, socioeconomics, sustainable development, sustainable development goals-sdg
Organization: UNESCO
Armenia - Education Indicators

Education indicators for Armenia.

Contains data from the UNESCO Institute for Statistics [bulk data service](http://data.uis.unesco.org) covering the following categories: SDG 4 Global and Thematic (made 2026 February), Other Policy Relevant Indicators (made 2026 February), Demographic and Socio-economic (made 2026 February)

Tags: demographics, education, indicators, socioeconomics, sustainable development, sustainable development goals-sdg
Organization: UNESCO
Argentina - Education Indicators

Education indicators for Argentina.

Contains data from the UNESCO Institute for Statistics [bulk data service](http://data.uis.unesco.org) covering the following categories: SDG 4 Global and Thematic (made 2026 February), Other Policy Relevant Indicators (made 2026 February), Demographic and Socio-economic (made 2026 February)

Tags: demographics, education, indicators, socioeconomics, sustainable development, sustainable development goals-sdg
Organization: UNESCO
United Arab Emirates - Education Indicators

Education indicators for United Arab Emirates.

Contains data from the UNESCO Institute for Statistics [bulk data service](http://data.uis.unesco.org) covering the following categories: SDG 4 Global and Thematic (made 2026 February), Other Policy Relevant Indicators (made 2026 February), Demographic and Socio-economic (made 2026 February)

Tags: demographics, education, indicators, socioeconomics, sustainable development, sustainable development goals-sdg
Organization: UNESCO
Andorra - Education Indicators

Education indicators for Andorra.

Contains data from the UNESCO Institute for Statistics [bulk data service](http://data.uis.unesco.org) covering the following categories: SDG 4 Global and Thematic (made 2026 February), Other Policy Relevant Indicators (made 2026 February), Demographic and Socio-economic (made 2026 February)

Tags: demographics, education, indicators, socioeconomics, sustainable development, sustainable development goals-sdg
Organization: UNESCO
Falkland Islands (Malvinas): 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 0.0005 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0004 million km (79.1143%)
- Unpaved roads: 0.0001 million km (19.0551%)

A total of 0.0 million km (0.1344%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0004 million km
(73038.6551%) 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)