ArrenOnom/GeoFoundation-Land-Degradation

**Multimodal explainable GeoFoundation framework for cross-regional prediction of climate-induced land degradation in semi-arid regions of Nigeria and India using Sentinel, climatic, and terrain data with transformer-based transfer learning and explainable AI for environmental analysis.** # 🌍 GeoFoundation-Land-Degradation ### Cross-Continental Transferability of a Multimodal Explainable Geospatial Foundation Model for Predicting Climate-Induced Land Degradation in Semi-Arid Environments of Nigeria and India --- ## 🧠 Project Overview This project presents a **GeoAI foundation model framework** designed to investigate the **cross-continental transferability of environmental representations** between two climatically distinct but ecologically comparable semi-arid regions: - 🇳🇬 Katsina, Nigeria (Sahel Region) - 🇮🇳 Rajasthan, India (Thar Desert) The study develops a **multimodal self-supervised geospatial transformer (GeoFoundation model)** that learns environmental representations from Earth Observation data in Nigeria and evaluates its ability to generalize and diagnose land degradation patterns in India. The framework integrates **optical, radar, thermal, vegetation, topographic, and hydrological data** to build a unified environmental representation space. --- ## 🎯 Research Objectives - To develop a multimodal GeoAI foundation model for land degradation analysis - To learn generalized environmental representations using self-supervised learning - To evaluate cross-continental transferability from the Nigerian Sahel to the Indian Thar Desert - To analyze latent space topology and environmental similarity across continents - To provide explainable AI-based insights into environmental drivers of degradation --- ## 🧪 Methodological Summary The workflow includes: - Multimodal Earth Observation data integration - Patch-based tensor construction (224 × 224 × 12) - Self-supervised transformer-based representation learning - Cross-regional transfer learning (Nigeria → India) - Reconstruction-based environmental diagnostics - UMAP latent space topology analysis - Statistical similarity testing (KS-test, correlation analysis) - Uncertainty and reliability estimation using Monte Carlo dropout - Explainable AI-based modality sensitivity analysis --- …