AndreaBozzo/ceres-open-data-index huggingface dataset:
South Africa - 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
China: 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
China **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 6.1163 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.6539 million km (10.6909%)
- Unpaved roads: 0.0775 million km (1.2663%)

About 5.385 million km
(88.0428%) of road surface information is missing in OSM.

To reduce this gap, the Mapillary-derived road surface dataset provides an additional
0.012 million km
(0.2231%) 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)
Somalia - 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
Solomon Islands - 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
Sierra Leone - 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
Serbia - 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
Senegal - 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
Saudi Arabia - 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
Rwanda - 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
Russian Federation - 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
Switzerland: 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
Switzerland **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.2896 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.0825 million km (28.4811%)
- Unpaved roads: 0.0436 million km (15.0571%)

About 0.1635 million km
(56.4618%) 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
(0.3696%) 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)
Romania - 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
Qatar - 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
Poland - 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
Philippines - 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
Peru - 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
Paraguay - 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
Papua New 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
Panama - 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
Philippines: 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
Philippines **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.4963 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.0815 million km (16.4302%)
- Unpaved roads: 0.0513 million km (10.3289%)

About 0.3635 million km
(73.241%) of road surface information is missing in OSM.

To reduce this gap, the Mapillary-derived road surface dataset provides an additional
0.0024 million km
(0.6728%) 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)
Pakistan - 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
North Macedonia - 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
Nigeria - 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
Niger - 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
Nicaragua - 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
Netherlands (Kingdom of the) - 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