AndreaBozzo/ceres-open-data-index huggingface dataset:
Côte d'Ivoire - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
Italy - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
Israel - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
Antigua and Barbuda: 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
Antigua and Barbuda **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.0023 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.0005 million km (22.6332%)
- Unpaved roads: 0.0004 million km (15.5253%)

About 0.0014 million km
(61.8416%) 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
(0.0012%) 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)
Iraq - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
Iran (Islamic Republic of) - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
Indonesia - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
India - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
Honduras - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
Haiti - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
Guyana - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
Guinea-Bissau - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
Pakistan: 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
Pakistan **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.652 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.0758 million km (11.6191%)
- Unpaved roads: 0.0598 million km (9.1793%)

About 0.5164 million km
(79.2016%) of road surface information is missing in OSM.

To reduce this gap, the Mapillary-derived road surface dataset provides an additional
0.0048 million km
(0.9208%) 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)
Guinea - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
Guatemala - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
Ghana - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
Germany - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
Georgia - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
Gambia - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
France - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
Ethiopia - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
Cambodia: 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
Cambodia **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.1226 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.0086 million km (7.0293%)
- Unpaved roads: 0.0133 million km (10.8203%)

About 0.1007 million km
(82.1504%) 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
(1.1915%) 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)
Eritrea - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
El Salvador - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
Egypt - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX
Ecuador - Data on Conflict Events

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days.  
Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532  
Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

Tags: conflict-violence, hxl
Organization: HDX