Add README.md

README.md

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 ---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- en
+license: cc-by-sa-4.0
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- tabular-classification
+- tabular-regression
+task_ids: []
+tags:
+- africa
+- humanitarian
+- hdx
+- electric-sheep-africa
+- flooding
+- geodata
+- nga
+pretty_name: "Satellite detected water extents from 9 to 13 November 2024 over Nigeria"
 dataset_info:
-  features:
-  - name: state
-    dtype: string
-  - name: area
-    dtype: float64
-  - name: total_population_in_area
-    dtype: float64
-  - name: maximum_floodwater_extent_in_cloud_free_area_5_9_sep_2024
-    dtype: float64
-  - name: population_exposed_to_maximum_flood_extent_in_cloud_free_area_5_9_sep_2024
-    dtype: float64
-  - name: maximum_floodwater_extent_in_cloud_free_area_14_18_sep_2024
-    dtype: float64
-  - name: population_exposed_to_maximum_flood_extent_in_cloud_free_area_14_18_sep_2024
-    dtype: float64
-  - name: maximum_floodwater_extent_in_cloud_free_area_19_23_sep_2024
-    dtype: float64
-  - name: population_exposed_to_maximum_flood_extent_in_cloud_free_area_19_23_sep_2024
-    dtype: float64
-  - name: maximum_floodwater_extent_in_cloud_free_area_26_30_sep_2024
-    dtype: float64
-  - name: population_exposed_to_maximum_flood_extent_in_cloud_free_area_26_30_sep_2024
-    dtype: float64
-  - name: analysed_area_in_cloud_free_area_27_31_oct_2024_km2
-    dtype: float64
-  - name: analyzed_area_percentage_of_total_area_27_31_oct_2024
-    dtype: float64
-  - name: total_population_in_cloud_free_area_27_31_oct_2024
-    dtype: float64
-  - name: maximum_floodwater_extent_in_cloud_free_area_27_31_oct_2024_km2
-    dtype: float64
-  - name: population_exposed_to_maximum_flood_extent_in_cloud_free_area_27_31_oct_2024
-    dtype: float64
-  - name: analysed_area_in_cloud_free_area_09_13_nov_2024_km2
-    dtype: float64
-  - name: analyzed_area_percentage_of_total_area_09_13_nov_2024
-    dtype: float64
-  - name: total_population_in_cloud_free_area_09_13_nov_2024
-    dtype: float64
-  - name: maximum_floodwater_extent_in_cloud_free_area_09_13_nov_2024_km2
-    dtype: float64
-  - name: population_exposed_to_maximum_flood_extent_in_cloud_free_area_09_13_nov_2024
-    dtype: float64
-  - name: variation_of_population_exposed_27_31_oct_2024_and_09_13_nov_2024_km2
-    dtype: float64
-  - name: variation_of_maximum_flood_water_extent_between_27_31_oct_2024_and_09_13_nov_2024_km2
-    dtype: float64
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 6934
-    num_examples: 31
-  - name: test
-    num_bytes: 1649
-    num_examples: 8
-  download_size: 39705
-  dataset_size: 8583
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 31
+    - name: test
+      num_examples: 7
 ---
+
+# Satellite detected water extents from 9 to 13 November 2024 over Nigeria
+
+**Publisher:** United Nations Satellite Centre (UNOSAT) · **Source:** [HDX](https://data.humdata.org/dataset/satellite-detected-water-extents-from-9-to-13-november-2024-over-nigeria) · **License:** `cc-by-sa` · **Updated:** 2026-02-17
+
+---
+
+## 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](https://huggingface.co/electricsheepafrica).*
+
+---
+
+## 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 |
+
+---
+
+## 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).
+
+---
+
+## Quick Start
+
+```python
+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()
+```
+
+---
+
+## 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 |
+
+---
+
+## 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 |
+
+---
+
+## 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.
+
+---
+
+## 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](https://data.humdata.org/dataset/satellite-detected-water-extents-from-9-to-13-november-2024-over-nigeria) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@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)}
+}
+```
+
+---
+
+*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*