electricsheepafrica/africa-climada-wildfire-dataset

Wildfire: Humanitarian Response Plan (HRP) Countries Hazard Data for Disaster Risk Assessment

Publisher: ETH Zürich - Weather and Climate Risks · Source: HDX · License: cc-by · Updated: 2025-09-05

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Abstract

Global wildfire dataset at 4km resolution, based on MODIS satellite data 2000-2021 (cf https://firms.modaps.eosdis.nasa.gov).

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-09-05. Geographic scope: AFG, BFA, BDI, CMR, CAF, TCD, COL, COD, and 15 others.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	First-level administrative unit observations
Rows (total)	411
Columns	9 (3 numeric, 6 categorical, 0 datetime)
Train split	328 rows
Test split	82 rows
Geographic scope	AFG, BFA, BDI, CMR, CAF, TCD, COL, COD, and 15 others
Publisher	ETH Zürich - Weather and Climate Risks
HDX last updated	2025-09-05

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Variables

Geographic — country_name (Nigeria, Afghanistan, Colombia), admin1_name (Centre, North, Adamawa), latitude (range -25.9531–51.3497), longitude (range -81.5417–98.758).

Outcome / Measurement — value (range 0.0–7692.0).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-04).

Other — aggregation (sum), indicator (wildfire, #indicator+name).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-climada-wildfire-dataset")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
country_name	object	0.0%	Nigeria, Afghanistan, Colombia
admin1_name	object	0.0%	Centre, North, Adamawa
latitude	float64	0.2%	-25.9531 – 51.3497 (mean 13.6371)
longitude	float64	0.2%	-81.5417 – 98.758 (mean 14.7502)
aggregation	object	0.2%	sum
indicator	object	0.0%	wildfire, #indicator+name
value	float64	0.2%	0.0 – 7692.0 (mean 1007.0195)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-04

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Numeric Summary

Column	Min	Max	Mean	Median
latitude	-25.9531	51.3497	13.6371	10.5252
longitude	-81.5417	98.758	14.7502	24.6559
value	0.0	7692.0	1007.0195	400.0

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Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. 3 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from ETH Zürich - Weather and Climate Risks and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• This dataset spans 23 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_climada_wildfire_dataset,
  title     = {Wildfire: Humanitarian Response Plan (HRP) Countries Hazard Data for Disaster Risk Assessment},
  author    = {ETH Zürich - Weather and Climate Risks},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/climada-wildfire-dataset},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.