electricsheepafrica/africa-deforestation-all

Nigerian Forestry Datasets

Publisher: Code for Africa · Source: OpenAfrica · License: cc-by · Updated: 2023-11-30

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Abstract

Nigerian forest-related data sets and analyses

Each row in this dataset represents geolocated point observations. Temporal coverage is indicated by the date column(s). Geographic scope: Africa (multiple countries).

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Geolocated point observations
Rows (total)	27,766
Columns	10 (4 numeric, 5 categorical, 1 datetime)
Train split	22,212 rows
Test split	5,553 rows
Geographic scope	Africa (multiple countries)
Publisher	Code for Africa
OpenAfrica last updated	2023-11-30

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Variables

Geographic — lat (range 4.5104–8.3938), lon (range 2.8749–12.191), iso (NGA).

Temporal — date.

Identifier / Metadata — cartodb_id (range 2787332.0–2815097.0), esa_source (HDX), esa_processed (2026-04-27).

Other — the_geom (0101000020E6100000919845CDFC4F1740FADDE82122C21B40, 0101000020E61000006F1DD25E33F31A4061444F8888E81240, 0101000020E61000001CA34CD503131840052217DEDDFD1240), the_geom_webmercator (0101000020110F0000FC592E199DCC2341EB47882E0DA22741, 0101000020110F000099D37C487EE32641310AE7FFBC132041, 0101000020110F0000E71D4C24407224419349D336EB252041), gadm2 (range 119084.0–119835.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-deforestation-all")
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
cartodb_id	int64	0.0%	2787332.0 – 2815097.0 (mean 2801214.5)
the_geom	object	0.0%	0101000020E6100000919845CDFC4F1740FADDE82122C21B40, 0101000020E61000006F1DD25E33F31A4061444F8888E81240, 0101000020E61000001CA34CD503131840052217DEDDFD1240
the_geom_webmercator	object	0.0%	0101000020110F0000FC592E199DCC2341EB47882E0DA22741, 0101000020110F000099D37C487EE32641310AE7FFBC132041, 0101000020110F0000E71D4C24407224419349D336EB252041
lat	float64	0.0%	4.5104 – 8.3938 (mean 5.9393)
lon	float64	0.0%	2.8749 – 12.191 (mean 5.8028)
iso	object	0.0%	NGA
gadm2	int64	0.0%	119084.0 – 119835.0 (mean 119623.5012)
date	datetime64[ns]	0.0%
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-27

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Numeric Summary

Column	Min	Max	Mean	Median
cartodb_id	2787332.0	2815097.0	2801214.5	2801214.5
lat	4.5104	8.3938	5.9393	5.8937
lon	2.8749	12.191	5.8028	5.7851
gadm2	119084.0	119835.0	119623.5012	119675.0

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Curation

Raw data was downloaded from OpenAfrica 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. 1 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 Code for Africa and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{openafrica_africa_deforestation_all,
  title     = {Nigerian Forestry Datasets},
  author    = {Code for Africa},
  year      = {2023},
  url       = {https://open.africa/dataset/nigerian-forestry-datasets},
  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.