AndreaBozzo/ceres-open-data-index huggingface dataset:
Bahamas: 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 0.0008 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0008 million km (98.4185%)
- Unpaved roads: 0.0 million km (0.0819%)

A total of 0.0002 million km (18.4815%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0008 million km
(532.967%) 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)
British Virgin 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: 0.0001 million km (87.1986%)
- Unpaved roads: 0.0 million km (0.0%)

A total of 0.0 million km (0.0485%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0001 million km
(179932.2646%) 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)
Guyana: 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 0.001 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0006 million km (53.1587%)
- Unpaved roads: 0.0003 million km (31.8745%)

A total of 0.0002 million km (23.6671%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0009 million km
(359.2887%) 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)
Saint Pierre and Miquelon: 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 (98.9993%)
- Unpaved roads: 0.0 million km (0.0%)

A total of 0.0 million km (1.5457%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0 million km
(6404.688%) 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)
Kuwait: 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 0.0049 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0045 million km (92.2961%)
- Unpaved roads: 0.0 million km (0.0%)

A total of 0.0015 million km (31.7431%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0045 million km
(290.7593%) 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)
Svalbard and Jan Mayen 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 0.0 million km of arterial roads are mapped in OSM.

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

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

PlanetScope-derived predictions provide surface information for
0.0 million km
(nan%) 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)
Botswana: 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 0.0087 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.007 million km (80.2143%)
- Unpaved roads: 0.0013 million km (14.9362%)

A total of 0.0001 million km (1.1705%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0083 million km
(8128.8112%) 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)
Mozambique: 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 0.0164 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0091 million km (55.3348%)
- Unpaved roads: 0.0064 million km (38.8474%)

A total of 0.003 million km (18.1801%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0154 million km
(518.0523%) 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)