Datasets:

Aki298
/

AML-Johor-Tangkak-Sagil-GEE-dataset

license: other
language:
  - en
pretty_name: Sagil Monthly GEE Environmental Risk Dataset
size_categories:
  - 1K<n<10K
task_categories:
  - tabular-regression
  - time-series-forecasting
tags:
  - earth-engine
  - remote-sensing
  - environmental-monitoring
  - flood-risk
  - drought-risk
  - agriculture
  - malaysia
  - time-series

Sagil Monthly GEE Environmental Risk Dataset

This dataset contains monthly Google Earth Engine environmental observations and deterministic time-series features for agricultural grid cells in Sagil, Tangkak, Johor, Malaysia.

It supports a prototype task:

monthly environmental features at month t
-> one-month-ahead signed flood/drought proxy-risk score at month t+1

The learned score used by the project is an environmental anomaly and directional risk proxy. It is not a verified flood event label, crop-loss label, or calibrated flood/drought probability.

Dataset Summary

Study area: Sagil, Tangkak, Johor, Malaysia
Spatial unit: 1 km x 1 km grid-cell centroid
Current valid grid cells: 49
Monthly period: 2021-01-01 through 2026-06-01
Months per valid grid cell: 66
Processed rows: 3,234
Data type: tabular monthly spatiotemporal environmental data
Primary file for modeling: processed/processed_data.csv

Repository Structure

input/
  coordinates.csv

metadata/
  sample_index.csv
  extraction_manifest.json

raw/
  monthly/
    sagil_1.csv
    sagil_2.csv
    ...

processed/
  processed_data.csv

File Descriptions

`input/coordinates.csv`

Manual coordinate source for the Sagil grid cells.

Important columns:

sample_id
grid_row
grid_col
latitude
longitude
study_area
cell_size_m

Each row represents the centroid of a 1 km x 1 km grid cell. grid_row = 0 is the northernmost row and grid_col = 0 is the westernmost column.

`metadata/sample_index.csv`

Extraction lookup table for raw monthly files.

Important columns:

sample_id
grid_row
grid_col
latitude
longitude
cell_area_m2
first_month
last_month
row_count
raw_csv
extraction_status

Use rows with extraction_status = ok for modeling.

`metadata/extraction_manifest.json`

Extraction run metadata, including:

extraction timestamp
study area
first and last month
month count
source datasets
thresholds
failed samples

`raw/monthly/<sample_id>.csv`

One raw monthly observation file per valid grid cell. Each file contains 66 monthly rows.

Raw observation groups include:

Identifiers: sample_id, grid_row, grid_col, latitude, longitude, month
Sentinel-2 optical indices: ndvi_mean, evi_mean, ndmi_mean, ndwi_mean
Sentinel-2 quality: s2_image_count, s2_valid_pixel_fraction
Sentinel-1 SAR: vv_mean_db, vh_mean_db, vv_minus_vh_db, vv_vh_ratio_linear, s1_image_count
CHIRPS rainfall: rainfall_total_mm, rainfall_mean_daily_mm, rainfall_max_1day_mm, dry_days_count, heavy_rain_days_count
ERA5-Land climate and water variables: temperature_2m_mean_c, relative_humidity_mean_pct, soil_water_layer1_mean, surface_runoff_total_m, evaporation_total_m
Dynamic World probabilities: water_probability_mean, flooded_vegetation_probability_mean, built_probability_mean, dynamicworld_image_count
Terrain and historical extent: elevation_mean_m, elevation_min_m, elevation_max_m, slope_mean_deg, lowland_fraction, max_water_extent_fraction
Missingness flags: optical_missing_flag, sar_missing_flag, climate_missing_flag, any_missing_flag

`processed/processed_data.csv`

Combined model-ready deterministic feature table.

It contains:

all raw monthly observation columns
calendar features: year, month_number, month_sin, month_cos
lag features for selected hydrology, vegetation, SAR, and water variables
rolling mean and rolling sum features
month-to-month difference features

It intentionally does not contain:

learned anomaly scores
learned risk scores
one-month-ahead target columns
model predictions

Those values are generated in the modeling pipeline so that training-only preprocessing and target construction remain leakage-controlled.

Source Datasets

The dataset is derived through Google Earth Engine using:

Sentinel-2 Surface Reflectance Harmonized
Sentinel-1 GRD
CHIRPS Daily Rainfall
ERA5-Land Daily Aggregates
Dynamic World V1
SRTM elevation
JRC Global Surface Water maximum extent

Please consult the original providers' terms for downstream use and redistribution requirements.

Intended Use

This dataset is intended for:

academic machine learning experiments
environmental time-series forecasting prototypes
flood/drought proxy-risk modeling
remote-sensing feature engineering demonstrations
chronological train/test/holdout evaluation

The matching project code constructs a learned signed risk proxy and trains:

persistence baseline
Ridge regression
Random Forest regressor
LSTM sequence model
Transformer encoder sequence model

Not Intended For

This dataset should not be used as:

an official flood warning dataset
a calibrated disaster-probability dataset
verified crop-loss or damage labels
a substitute for field survey, hydrological model output, river gauge data, or official disaster records

Risk Proxy Method

The project builds the target outside processed_data.csv.

For each grid cell i and month t, selected environmental variables form:

x_i,t

Training-fitted preprocessing:

z_i,t = StandardScaler(MedianImputer(x_i,t))

Anomaly magnitude:

anomaly_magnitude = mean((z_i,t - MLP_reconstruct(z_i,t))^2)

Flood/drought direction is estimated from standardized environmental indicators:

flood_direction_score = average of positive wetness and flood indicators
drought_direction_score = average of dry and vegetation-stress indicators

Signed proxy score:

signed_risk_score = anomaly_magnitude * direction_sign

where:

direction_sign = +1 for flood-directed anomaly
direction_sign = -1 for drought-directed anomaly
direction_sign = 0 for neutral or ambiguous anomaly

Forecasting target:

target_risk_score_t_plus_1 = signed_risk_score at month t+1

Temporal Split Used In The Project

The project code uses target-month splitting:

train:   target_month <= 2025-05-01
test:    2025-06-01 <= target_month <= 2026-05-01
holdout: target_month == 2026-06-01

The June 2026 source row has no July 2026 target and is excluded from supervised evaluation.

Loading Example

from huggingface_hub import snapshot_download
import pandas as pd

local_dir = snapshot_download(
    repo_id="Aki298/AML-Johor-Tangkak-Sagil-GEE-dataset",
    repo_type="dataset",
    local_dir="data",
)

processed = pd.read_csv("data/processed/processed_data.csv")
coordinates = pd.read_csv("data/input/coordinates.csv")

Limitations

The target risk score is self-supervised and proxy-based, not field-verified.
The current release covers one agricultural study area in Sagil, Johor.
The effective independent sample size is smaller than the row count because neighboring grid cells and adjacent months are correlated.
Next-month flood/drought behavior may depend on future rainfall and hydrological conditions that are not known at month t.
Model results should be interpreted as prototype environmental-risk forecasting, not operational disaster forecasting.

Citation

If using this dataset, cite the dataset repository and acknowledge the original Earth observation and climate data providers used through Google Earth Engine.