electricsheepafrica/africa-satellite-detected-water-extents-from-9-to-13-november-2024-over-nigeria

Satellite detected water extents from 9 to 13 November 2024 over Nigeria

Publisher: United Nations Satellite Centre (UNOSAT) · Source: HDX · License: cc-by-sa · Updated: 2026-02-17

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Abstract

UNOSAT code: FL20240902NGA, GDACS ID: 1102720 This map illustrates cumulative satellite-detected water using VIIRS in Nigeria between 09 to 13 November 2024. Within the cloud free analysed areas of about 900,000 km², a total of about 22,000 km² of lands appear to be affected with flood waters. Maximum flood water extent appears to have receded of about 5,700 km²
since the period between 27 to 31 October 2024. Based on Worldpop population data and the maximum flood water extent, about 5.8 million people remain potentially exposed or live close to flooded areas.

The pixelwise water fraction from VIIRS, using a 5-day composite at 375 m spatial resolution, indicates potential floodwater coverage ranging from 0% to 100%. This large-scale analysis is intended for guidance purposes and has not yet been validated with ground truth data or higher- resolution analysis. The population exposure
analysis is based on floodwaters observed only in cloud-free areas, so the total number of people exposed may be underestimated.

This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-02-17. Geographic scope: NGA.

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

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Dataset Characteristics

Domain	Natural hazards and disaster risk
Unit of observation	First-level administrative unit observations
Rows (total)	39
Columns	25 (22 numeric, 3 categorical, 0 datetime)
Train split	31 rows
Test split	7 rows
Geographic scope	NGA
Publisher	United Nations Satellite Centre (UNOSAT)
HDX last updated	2026-02-17

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Variables

Geographic — state (Abia, Osun, Kogi), total_population_in_area (range 2548426.3661–216544909.3309), maximum_floodwater_extent_in_cloud_free_area_5_9_sep_2024 (range 0.0–13918.0), population_exposed_to_maximum_flood_extent_in_cloud_free_area_5_9_sep_2024 (range 0.0–2666144.0), maximum_floodwater_extent_in_cloud_free_area_14_18_sep_2024 (range 1.0–24045.0) and 17 others.

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-17).

Other — area (range 3671.48–909932.82).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-satellite-detected-water-extents-from-9-to-13-november-2024-over-nigeria")
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
state	object	0.0%	Abia, Osun, Kogi
area	float64	2.6%	3671.48 – 909932.82 (mean 47891.2011)
total_population_in_area	float64	2.6%	2548426.3661 – 216544909.3309 (mean 11397100.4911)
maximum_floodwater_extent_in_cloud_free_area_5_9_sep_2024	float64	2.6%	0.0 – 13918.0 (mean 732.5263)
population_exposed_to_maximum_flood_extent_in_cloud_free_area_5_9_sep_2024	float64	2.6%	0.0 – 2666144.0 (mean 140323.3684)
maximum_floodwater_extent_in_cloud_free_area_14_18_sep_2024	float64	2.6%	1.0 – 24045.0 (mean 1265.5263)
population_exposed_to_maximum_flood_extent_in_cloud_free_area_14_18_sep_2024	float64	2.6%	429.0 – 4766463.0 (mean 250866.4737)
maximum_floodwater_extent_in_cloud_free_area_19_23_sep_2024	float64	2.6%	0.0 – 23849.0 (mean 1255.2105)
population_exposed_to_maximum_flood_extent_in_cloud_free_area_19_23_sep_2024	float64	2.6%	65.0 – 4776015.0 (mean 251369.2105)
maximum_floodwater_extent_in_cloud_free_area_26_30_sep_2024	float64	2.6%	1.9414 – 26107.5585 (mean 1374.082)
population_exposed_to_maximum_flood_extent_in_cloud_free_area_26_30_sep_2024	float64	2.6%	1025.54 – 5340914.72 (mean 281100.7747)
analysed_area_in_cloud_free_area_27_31_oct_2024_km2	float64	2.6%	3659.0 – 896969.0 (mean 47208.8947)
analyzed_area_percentage_of_total_area_27_31_oct_2024	float64	2.6%	0.4109 – 1.0001 (mean 0.9625)
total_population_in_cloud_free_area_27_31_oct_2024	float64	2.6%	1320929.0 – 208070207.0 (mean 10951063.5263)
maximum_floodwater_extent_in_cloud_free_area_27_31_oct_2024_km2	float64	2.6%	2.0 – 27288.0 (mean 1436.2105)
population_exposed_to_maximum_flood_extent_in_cloud_free_area_27_31_oct_2024	float64	2.6%	1021.0 – 5767878.0 (mean 303572.5263)
analysed_area_in_cloud_free_area_09_13_nov_2024_km2	float64	2.6%	3670.0 – 909628.0 (mean 47875.1579)
analyzed_area_percentage_of_total_area_09_13_nov_2024	float64	2.6%	0.9935 – 1.0001 (mean 0.9994)
total_population_in_cloud_free_area_09_13_nov_2024	float64	2.6%	2540877.0 – 216422300.0 (mean 11390647.3684)
maximum_floodwater_extent_in_cloud_free_area_09_13_nov_2024_km2	float64	2.6%	16.0 – 21542.0 (mean 1133.7895)
population_exposed_to_maximum_flood_extent_in_cloud_free_area_09_13_nov_2024	float64	2.6%	8101.0 – 5813327.0 (mean 305964.5789)
variation_of_population_exposed_27_31_oct_2024_and_09_13_nov_2024_km2	float64	2.6%
variation_of_maximum_flood_water_extent_between_27_31_oct_2024_and_09_13_nov_2024_km2	float64	2.6%
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-17

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Numeric Summary

Column	Min	Max	Mean	Median
area	3671.48	909932.82	47891.2011	22085.55
total_population_in_area	2548426.3661	216544909.3309	11397100.4911	5201788.2147
maximum_floodwater_extent_in_cloud_free_area_5_9_sep_2024	0.0	13918.0	732.5263	95.0
population_exposed_to_maximum_flood_extent_in_cloud_free_area_5_9_sep_2024	0.0	2666144.0	140323.3684	39449.0
maximum_floodwater_extent_in_cloud_free_area_14_18_sep_2024	1.0	24045.0	1265.5263	217.0
population_exposed_to_maximum_flood_extent_in_cloud_free_area_14_18_sep_2024	429.0	4766463.0	250866.4737	53594.5
maximum_floodwater_extent_in_cloud_free_area_19_23_sep_2024	0.0	23849.0	1255.2105	232.5
population_exposed_to_maximum_flood_extent_in_cloud_free_area_19_23_sep_2024	65.0	4776015.0	251369.2105	71773.0
maximum_floodwater_extent_in_cloud_free_area_26_30_sep_2024	1.9414	26107.5585	1374.082	283.5685
population_exposed_to_maximum_flood_extent_in_cloud_free_area_26_30_sep_2024	1025.54	5340914.72	281100.7747	73117.75
analysed_area_in_cloud_free_area_27_31_oct_2024_km2	3659.0	896969.0	47208.8947	21963.0
analyzed_area_percentage_of_total_area_27_31_oct_2024	0.4109	1.0001	0.9625	1.0
total_population_in_cloud_free_area_27_31_oct_2024	1320929.0	208070207.0	10951063.5263	5035779.0
maximum_floodwater_extent_in_cloud_free_area_27_31_oct_2024_km2	2.0	27288.0	1436.2105	224.0
population_exposed_to_maximum_flood_extent_in_cloud_free_area_27_31_oct_2024	1021.0	5767878.0	303572.5263	93476.0

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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. 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 United Nations Satellite Centre (UNOSAT) 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{hdx_africa_satellite_detected_water_extents_from_9_to_13_november_2024_over_nigeria,
  title     = {Satellite detected water extents from 9 to 13 November 2024 over Nigeria},
  author    = {United Nations Satellite Centre (UNOSAT)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/satellite-detected-water-extents-from-9-to-13-november-2024-over-nigeria},
  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.