AndreaBozzo/ceres-open-data-index huggingface dataset:
Uzbekistan: 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 0.0205 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0199 million km (97.3051%)
- Unpaved roads: 0.0001 million km (0.6872%)

A total of 0.0135 million km (65.6515%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0201 million km
(149.2615%) 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)
Yemen: 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 0.0151 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0142 million km (94.101%)
- Unpaved roads: 0.0002 million km (1.5575%)

A total of 0.0036 million km (23.871%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0144 million km
(400.7307%) 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)
Puerto Rico: 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 0.0042 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0038 million km (91.3445%)
- Unpaved roads: 0.0 million km (0.7906%)

A total of 0.0031 million km (73.9623%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0039 million km
(124.5703%) 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)
India: 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 0.5509 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.479 million km (86.939%)
- Unpaved roads: 0.0287 million km (5.211%)

A total of 0.3796 million km (68.9098%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.5077 million km
(133.7255%) 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)
Egypt: 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 0.0495 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0466 million km (94.2183%)
- Unpaved roads: 0.0007 million km (1.3995%)

A total of 0.031 million km (62.708%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0473 million km
(152.4811%) 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)
Israel: 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 0.0095 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0091 million km (95.9778%)
- Unpaved roads: 0.0 million km (0.4861%)

A total of 0.0045 million km (47.2226%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0092 million km
(204.2745%) 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)
Libya: 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 0.0211 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0196 million km (93.1876%)
- Unpaved roads: 0.0001 million km (0.6791%)

A total of 0.0033 million km (15.8617%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0198 million km
(591.781%) 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)
Hungary: 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 0.0305 million km of arterial roads are mapped in OSM.

Based on AI-derived estimates:
- Paved roads: 0.0299 million km (97.9843%)
- Unpaved roads: 0.0001 million km (0.4639%)

A total of 0.0122 million km (40.0244%)
lack OSM surface tags.

PlanetScope-derived predictions provide surface information for
0.0301 million km
(245.9707%) 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)