Upload README.md

README.md

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+---
+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:
+
+```text
+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
+
+```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/<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:
+
+```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.
+