AndreaBozzo/ceres-open-data-index huggingface dataset:
Netherlands (Kingdom of the) - Food Prices

Food Prices for Netherlands (Kingdom of the).

Contains data from the FAOSTAT [bulk data service](https://fenixservices.fao.org/faostat/static/bulkdownloads/datasets_E.json) covering the following categories: Consumer Price Indices, Deflators, Exchange rates, Producer Prices

Tags: food security, indicators
Organization: Food and Agriculture Organization (FAO) of the United Nations
British Virgin Islands: 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
British Virgin Islands **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.0007 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.0003 million km (38.2799%)
- Unpaved roads: 0.0001 million km (20.223%)

About 0.0003 million km
(41.497%) 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
(0.0%) 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)
British Indian Ocean Territory: 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
British Indian Ocean Territory **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.0001 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.0 million km (0.3477%)
- Unpaved roads: 0.0 million km (28.0622%)

About 0.0001 million km
(71.5901%) 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
(0.0%) 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)
Morocco: 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
Morocco **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.5282 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.1088 million km (20.6055%)
- Unpaved roads: 0.0629 million km (11.9155%)

About 0.3564 million km
(67.479%) of road surface information is missing in OSM.

To reduce this gap, the Mapillary-derived road surface dataset provides an additional
0.0008 million km
(0.2314%) 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)
Benin: 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
Benin **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.09 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.0083 million km (9.2005%)
- Unpaved roads: 0.0324 million km (35.9634%)

About 0.0493 million km
(54.8361%) 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
(0.1129%) 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)
Samoa: 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
Samoa **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.0028 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.0006 million km (20.0916%)
- Unpaved roads: 0.0006 million km (21.9824%)

About 0.0016 million km
(57.9261%) 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
(3.6454%) 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)
Seychelles: 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
Seychelles **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.0014 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.0002 million km (10.8132%)
- Unpaved roads: 0.0001 million km (3.9969%)

About 0.0012 million km
(85.1899%) 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
(0.0%) 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)
Belize: 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
Belize **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.0169 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.002 million km (11.5681%)
- Unpaved roads: 0.0149 million km (88.4271%)

About 0.0 million km
(0.0048%) 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
(0.0%) 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)
New Caledonia: 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
New Caledonia **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.0 million km of roads are mapped in OSM in this region.

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

About 0.0 million km
(nan%) 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
(nan%) 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)