AndreaBozzo/ceres-open-data-index huggingface dataset:
Malaysia: Road Surface Data

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

Surface classification 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
Malaysia **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.4788 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.0763 million km (15.9342%)
- Unpaved roads: 0.0645 million km (13.4757%)

About 0.338 million km
(70.5901%) of road surface information is missing in OSM.

To reduce this gap, the Mapillary-derived road surface dataset provides an additional
0.0023 million km
(0.6814%) of the missing surface information.


Intended Use

This dataset supports transportation planning, infrastructure analysis,
climate emissions assessment, 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**: Predicted length based on DL estimation (meters)
- **DL_mean_timestamp**: Mean timestamp of predictions used


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)
Botswana: Road Surface Data

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

Surface classification 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
Botswana **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.1982 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.0191 million km (9.6392%)
- Unpaved roads: 0.0621 million km (31.3427%)

About 0.117 million km
(59.0181%) of road surface information is missing in OSM.

To reduce this gap, the Mapillary-derived road surface dataset provides an additional
0.0001 million km
(0.0742%) of the missing surface information.


Intended Use

This dataset supports transportation planning, infrastructure analysis,
climate emissions assessment, 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**: Predicted length based on DL estimation (meters)
- **DL_mean_timestamp**: Mean timestamp of predictions used


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)
Kyrgyzstan: Road Surface Data

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

Surface classification 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
Kyrgyzstan **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.0861 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.0084 million km (9.7637%)
- Unpaved roads: 0.0125 million km (14.5367%)

About 0.0652 million km
(75.6995%) of road surface information is missing in OSM.

To reduce this gap, the Mapillary-derived road surface dataset provides an additional
0.0001 million km
(0.1596%) of the missing surface information.


Intended Use

This dataset supports transportation planning, infrastructure analysis,
climate emissions assessment, 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**: Predicted length based on DL estimation (meters)
- **DL_mean_timestamp**: Mean timestamp of predictions used


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)
Senegal: Road Surface Data

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

Surface classification 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
Senegal **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.2436 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.0161 million km (6.6041%)
- Unpaved roads: 0.0944 million km (38.7656%)

About 0.1331 million km
(54.6302%) of road surface information is missing in OSM.

To reduce this gap, the Mapillary-derived road surface dataset provides an additional
0.0001 million km
(0.0694%) of the missing surface information.


Intended Use

This dataset supports transportation planning, infrastructure analysis,
climate emissions assessment, 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**: Predicted length based on DL estimation (meters)
- **DL_mean_timestamp**: Mean timestamp of predictions used


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)
Tunisia: Road Surface Data

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

Surface classification 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
Tunisia **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.2026 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.0238 million km (11.7515%)
- Unpaved roads: 0.0196 million km (9.6552%)

About 0.1592 million km
(78.5933%) of road surface information is missing in OSM.

To reduce this gap, the Mapillary-derived road surface dataset provides an additional
0.002 million km
(1.2286%) of the missing surface information.


Intended Use

This dataset supports transportation planning, infrastructure analysis,
climate emissions assessment, 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**: Predicted length based on DL estimation (meters)
- **DL_mean_timestamp**: Mean timestamp of predictions used


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)
Costa Rica: Road Surface Data

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

Surface classification 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
Costa Rica **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.068 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.0129 million km (18.9713%)
- Unpaved roads: 0.0136 million km (19.989%)

About 0.0415 million km
(61.0397%) of road surface information is missing in OSM.

To reduce this gap, the Mapillary-derived road surface dataset provides an additional
0.0006 million km
(1.361%) of the missing surface information.


Intended Use

This dataset supports transportation planning, infrastructure analysis,
climate emissions assessment, 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**: Predicted length based on DL estimation (meters)
- **DL_mean_timestamp**: Mean timestamp of predictions used


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)
Austria: Road Surface Data

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

Surface classification 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
Austria **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.5122 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.11 million km (21.4685%)
- Unpaved roads: 0.0709 million km (13.8363%)

About 0.3314 million km
(64.6952%) of road surface information is missing in OSM.

To reduce this gap, the Mapillary-derived road surface dataset provides an additional
0.0012 million km
(0.3629%) of the missing surface information.


Intended Use

This dataset supports transportation planning, infrastructure analysis,
climate emissions assessment, 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**: Predicted length based on DL estimation (meters)
- **DL_mean_timestamp**: Mean timestamp of predictions used


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)
Trinidad and Tobago: Road Surface Data

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

Surface classification 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
Trinidad and Tobago **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.0165 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.0053 million km (32.4322%)
- Unpaved roads: 0.0004 million km (2.5412%)

About 0.0107 million km
(65.0266%) of road surface information is missing in OSM.

To reduce this gap, the Mapillary-derived road surface dataset provides an additional
0.0 million km
(0.2412%) of the missing surface information.


Intended Use

This dataset supports transportation planning, infrastructure analysis,
climate emissions assessment, 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**: Predicted length based on DL estimation (meters)
- **DL_mean_timestamp**: Mean timestamp of predictions used


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)