Nile River Sudan MultiSite ML Dataset

This repository contains the **Nile Sudan MultiSite ML Dataset**, a machine-learning-ready geospatial dataset generated using Google Earth Engine (GEE) for hydrological and remote sensing analysis in Sudan along the Nile River system. The dataset was developed for supervised machine learning applications related to:- flood mapping,- water-body detection,- hydrological monitoring,- and environmental classification. --- ## Study Area The dataset focuses on selected Nile River regions in Sudan, including areas surrounding the Merowe region and associated hydrological zones. Primary study region:- Merowe Dam region, Sudan --- ## Data Sources The dataset was generated using multiple remote sensing and terrain products available in Google Earth Engine: ### Satellite Data- Sentinel-1 SAR imagery- Sentinel-2 multispectral imagery ### Terrain and Hydrological Data- Digital Elevation Model (DEM)- Terrain derivatives: - elevation - slope - aspect - flow accumulation ### Climate Data- Rainfall products extracted from GEE climate datasets --- ## Dataset Features The dataset includes the following predictor variables: | Feature | Description ||---|---|| NDVI | Normalized Difference Vegetation Index || NDWI | Normalized Difference Water Index || MNDWI | Modified NDWI || VV | Sentinel-1 SAR backscatter || blue | Sentinel-2 blue band || green | Sentinel-2 green band || red | Sentinel-2 red band || nir | Near Infrared band || swir1 | Shortwave Infrared 1 || swir2 | Shortwave Infrared 2 || elevation | Terrain elevation || slope | Terrain slope || aspect | Terrain aspect || flow_acc | Flow accumulation || rainfall_mean | Mean rainfall || rainfall_sum | Total rainfall || label | Classification label || random | Randomized split variable | --- ## Coordinate System The dataset was generated within the Google Earth Engine environment using standard geographic coordinate systems associated with Sentinel products. Coordinate information is stored in:- `.geo` --- ## Temporal Coverage The dataset was generated from multi-temporal satellite observations processed within Google Earth Engine. --- ## Preprocessing Workflow The preprocessing pipeline included:1. Satellite image acquisition2. Cloud filtering and preprocessing3. Spectral index calculation4. Terrain feature extraction5. Rainfall variable integration6. Sample extraction7. Randomized train/test preparation8. CSV export from Google Earth Engine --- ## Machine Learning Applications This dataset is suitable for:- Random Forest classification- XGBoost models- Support Vector Machines (SVM)- Deep learning workflows- Flood susceptibility mapping- Water classification- Environmental monitoring --- ## Software Dataset generation was performed using:- Google Earth Engine (GEE)