AndreaBozzo/ceres-open-data-index huggingface dataset:
EM-DAT - Country Profiles, Congo

# Aggregated figures for natural hazard related events in EM-DAT: *Congo*

Documentation on the Country Profiles [available here](https://doc.emdat.be/docs/data-accessibility/#the-humanitarian-data-exchange-hdx-em-dat-country-profiles)

How to cite the EM-DAT Project [here](https://doc.emdat.be/docs/introduction/#how-to-cite)

Main dataset on HDX: [EM-DAT - Country Profiles](https://data.humdata.org/dataset/emdat-country-profiles)

More on the EM-DAT database :  [website](https://www.emdat.be) / [data portal](https://public.emdat.be)

Each line corresponds to a given combination of **year, country, disaster subtype** and reports figures for :

- number of disasters
- total number of people affected
- total number of deaths
- economic losses (original value and adjusted)

---

Tags: affected population, economics, fatalities, hxl, natural disasters
Organization: Centre for Research on the Epidemiology of Disasters
EM-DAT - Country Profiles, Uganda

# Aggregated figures for natural hazard related events in EM-DAT: *Uganda*

Documentation on the Country Profiles [available here](https://doc.emdat.be/docs/data-accessibility/#the-humanitarian-data-exchange-hdx-em-dat-country-profiles)

How to cite the EM-DAT Project [here](https://doc.emdat.be/docs/introduction/#how-to-cite)

Main dataset on HDX: [EM-DAT - Country Profiles](https://data.humdata.org/dataset/emdat-country-profiles)

More on the EM-DAT database :  [website](https://www.emdat.be) / [data portal](https://public.emdat.be)

Each line corresponds to a given combination of **year, country, disaster subtype** and reports figures for :

- number of disasters
- total number of people affected
- total number of deaths
- economic losses (original value and adjusted)

---

Tags: affected population, economics, fatalities, hxl, natural disasters
Organization: Centre for Research on the Epidemiology of Disasters
EM-DAT - Country Profiles, Somalia

# Aggregated figures for natural hazard related events in EM-DAT: *Somalia*

Documentation on the Country Profiles [available here](https://doc.emdat.be/docs/data-accessibility/#the-humanitarian-data-exchange-hdx-em-dat-country-profiles)

How to cite the EM-DAT Project [here](https://doc.emdat.be/docs/introduction/#how-to-cite)

Main dataset on HDX: [EM-DAT - Country Profiles](https://data.humdata.org/dataset/emdat-country-profiles)

More on the EM-DAT database :  [website](https://www.emdat.be) / [data portal](https://public.emdat.be)

Each line corresponds to a given combination of **year, country, disaster subtype** and reports figures for :

- number of disasters
- total number of people affected
- total number of deaths
- economic losses (original value and adjusted)

---

Tags: affected population, economics, fatalities, hxl, natural disasters
Organization: Centre for Research on the Epidemiology of Disasters
EM-DAT - Country Profiles, Cuba

# Aggregated figures for natural hazard related events in EM-DAT: *Cuba*

Documentation on the Country Profiles [available here](https://doc.emdat.be/docs/data-accessibility/#the-humanitarian-data-exchange-hdx-em-dat-country-profiles)

How to cite the EM-DAT Project [here](https://doc.emdat.be/docs/introduction/#how-to-cite)

Main dataset on HDX: [EM-DAT - Country Profiles](https://data.humdata.org/dataset/emdat-country-profiles)

More on the EM-DAT database :  [website](https://www.emdat.be) / [data portal](https://public.emdat.be)

Each line corresponds to a given combination of **year, country, disaster subtype** and reports figures for :

- number of disasters
- total number of people affected
- total number of deaths
- economic losses (original value and adjusted)

---

Tags: affected population, economics, fatalities, hxl, natural disasters
Organization: Centre for Research on the Epidemiology of Disasters
EM-DAT - Country Profiles, Azerbaijan

# Aggregated figures for natural hazard related events in EM-DAT: *Azerbaijan*

Documentation on the Country Profiles [available here](https://doc.emdat.be/docs/data-accessibility/#the-humanitarian-data-exchange-hdx-em-dat-country-profiles)

How to cite the EM-DAT Project [here](https://doc.emdat.be/docs/introduction/#how-to-cite)

Main dataset on HDX: [EM-DAT - Country Profiles](https://data.humdata.org/dataset/emdat-country-profiles)

More on the EM-DAT database :  [website](https://www.emdat.be) / [data portal](https://public.emdat.be)

Each line corresponds to a given combination of **year, country, disaster subtype** and reports figures for :

- number of disasters
- total number of people affected
- total number of deaths
- economic losses (original value and adjusted)

---

Tags: affected population, economics, fatalities, hxl, natural disasters
Organization: Centre for Research on the Epidemiology of Disasters
EM-DAT - Country Profiles, Afghanistan

# Aggregated figures for natural hazard related events in EM-DAT: *Afghanistan*

Documentation on the Country Profiles [available here](https://doc.emdat.be/docs/data-accessibility/#the-humanitarian-data-exchange-hdx-em-dat-country-profiles)

How to cite the EM-DAT Project [here](https://doc.emdat.be/docs/introduction/#how-to-cite)

Main dataset on HDX: [EM-DAT - Country Profiles](https://data.humdata.org/dataset/emdat-country-profiles)

More on the EM-DAT database :  [website](https://www.emdat.be) / [data portal](https://public.emdat.be)

Each line corresponds to a given combination of **year, country, disaster subtype** and reports figures for :

- number of disasters
- total number of people affected
- total number of deaths
- economic losses (original value and adjusted)

---

Tags: affected population, economics, fatalities, hxl, natural disasters
Organization: Centre for Research on the Epidemiology of Disasters
EM-DAT - Country Profiles, State of Palestine

# Aggregated figures for natural hazard related events in EM-DAT: *State of Palestine*

Documentation on the Country Profiles [available here](https://doc.emdat.be/docs/data-accessibility/#the-humanitarian-data-exchange-hdx-em-dat-country-profiles)

How to cite the EM-DAT Project [here](https://doc.emdat.be/docs/introduction/#how-to-cite)

Main dataset on HDX: [EM-DAT - Country Profiles](https://data.humdata.org/dataset/emdat-country-profiles)

More on the EM-DAT database :  [website](https://www.emdat.be) / [data portal](https://public.emdat.be)

Each line corresponds to a given combination of **year, country, disaster subtype** and reports figures for :

- number of disasters
- total number of people affected
- total number of deaths
- economic losses (original value and adjusted)

---

Tags: affected population, economics, fatalities, hxl, natural disasters
Organization: Centre for Research on the Epidemiology of Disasters
EM-DAT - Country Profiles, Lithuania

# Aggregated figures for natural hazard related events in EM-DAT: *Lithuania*

Documentation on the Country Profiles [available here](https://doc.emdat.be/docs/data-accessibility/#the-humanitarian-data-exchange-hdx-em-dat-country-profiles)

How to cite the EM-DAT Project [here](https://doc.emdat.be/docs/introduction/#how-to-cite)

Main dataset on HDX: [EM-DAT - Country Profiles](https://data.humdata.org/dataset/emdat-country-profiles)

More on the EM-DAT database :  [website](https://www.emdat.be) / [data portal](https://public.emdat.be)

Each line corresponds to a given combination of **year, country, disaster subtype** and reports figures for :

- number of disasters
- total number of people affected
- total number of deaths
- economic losses (original value and adjusted)

---

Tags: affected population, economics, fatalities, hxl, natural disasters
Organization: Centre for Research on the Epidemiology of Disasters
EM-DAT - Country Profiles, Thailand

# Aggregated figures for natural hazard related events in EM-DAT: *Thailand*

Documentation on the Country Profiles [available here](https://doc.emdat.be/docs/data-accessibility/#the-humanitarian-data-exchange-hdx-em-dat-country-profiles)

How to cite the EM-DAT Project [here](https://doc.emdat.be/docs/introduction/#how-to-cite)

Main dataset on HDX: [EM-DAT - Country Profiles](https://data.humdata.org/dataset/emdat-country-profiles)

More on the EM-DAT database :  [website](https://www.emdat.be) / [data portal](https://public.emdat.be)

Each line corresponds to a given combination of **year, country, disaster subtype** and reports figures for :

- number of disasters
- total number of people affected
- total number of deaths
- economic losses (original value and adjusted)

---

Tags: affected population, economics, fatalities, hxl, natural disasters
Organization: Centre for Research on the Epidemiology of Disasters
EM-DAT - Country Profiles, Senegal

# Aggregated figures for natural hazard related events in EM-DAT: *Senegal*

Documentation on the Country Profiles [available here](https://doc.emdat.be/docs/data-accessibility/#the-humanitarian-data-exchange-hdx-em-dat-country-profiles)

How to cite the EM-DAT Project [here](https://doc.emdat.be/docs/introduction/#how-to-cite)

Main dataset on HDX: [EM-DAT - Country Profiles](https://data.humdata.org/dataset/emdat-country-profiles)

More on the EM-DAT database :  [website](https://www.emdat.be) / [data portal](https://public.emdat.be)

Each line corresponds to a given combination of **year, country, disaster subtype** and reports figures for :

- number of disasters
- total number of people affected
- total number of deaths
- economic losses (original value and adjusted)

---

Tags: affected population, economics, fatalities, hxl, natural disasters
Organization: Centre for Research on the Epidemiology of Disasters
EM-DAT - Country Profiles, Russian Federation

# Aggregated figures for natural hazard related events in EM-DAT: *Russian Federation*

Documentation on the Country Profiles [available here](https://doc.emdat.be/docs/data-accessibility/#the-humanitarian-data-exchange-hdx-em-dat-country-profiles)

How to cite the EM-DAT Project [here](https://doc.emdat.be/docs/introduction/#how-to-cite)

Main dataset on HDX: [EM-DAT - Country Profiles](https://data.humdata.org/dataset/emdat-country-profiles)

More on the EM-DAT database :  [website](https://www.emdat.be) / [data portal](https://public.emdat.be)

Each line corresponds to a given combination of **year, country, disaster subtype** and reports figures for :

- number of disasters
- total number of people affected
- total number of deaths
- economic losses (original value and adjusted)

---

Tags: affected population, economics, fatalities, hxl, natural disasters
Organization: Centre for Research on the Epidemiology of Disasters
Pitcairn: 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
Pitcairn **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 (2.2086%)
- Unpaved roads: 0.0 million km (44.762%)

About 0.0 million km
(53.0294%) 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)
Montenegro: 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
Montenegro **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.0315 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.0061 million km (19.2236%)
- Unpaved roads: 0.0038 million km (11.9438%)

About 0.0217 million km
(68.8326%) 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
(0.032%) 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)
Vanuatu: 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
Vanuatu **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.0049 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.0004 million km (7.4681%)
- Unpaved roads: 0.0012 million km (24.7891%)

About 0.0033 million km
(67.7428%) 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
(0.872%) 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)
Kenya: 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
Kenya **Planet** Road Surface.
The corresponding paper is available at:
https://arxiv.org/abs/2512.04092


Road Surface Statistics

Approximately 0.5791 million km of roads are mapped in OSM in this region.

Based on AI-derived estimates:
- Paved roads: 0.023 million km (3.9642%)
- Unpaved roads: 0.1669 million km (28.8242%)

About 0.3892 million km
(67.2116%) of road surface information is missing in OSM.

To reduce this gap, the Mapillary-derived road surface dataset provides an additional
0.0021 million km
(0.5353%) 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)