--- license: other language: - en pretty_name: Sagil Monthly GEE Environmental Risk Dataset size_categories: - 1K 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 ```text 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: ```text 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: ```text 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: ```text extraction timestamp study area first and last month month count source datasets thresholds failed samples ``` ### `raw/monthly/.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: ```text x_i,t ``` Training-fitted preprocessing: ```text z_i,t = StandardScaler(MedianImputer(x_i,t)) ``` Anomaly magnitude: ```text anomaly_magnitude = mean((z_i,t - MLP_reconstruct(z_i,t))^2) ``` Flood/drought direction is estimated from standardized environmental indicators: ```text flood_direction_score = average of positive wetness and flood indicators drought_direction_score = average of dry and vegetation-stress indicators ``` Signed proxy score: ```text signed_risk_score = anomaly_magnitude * direction_sign ``` where: ```text direction_sign = +1 for flood-directed anomaly direction_sign = -1 for drought-directed anomaly direction_sign = 0 for neutral or ambiguous anomaly ``` Forecasting target: ```text 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: ```text 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 ```python 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.