Geospatial Deep Learning for Environmental Feature Extraction

Abstract

The growing availability of high-resolution geospatial data has created a pressing need for advanced computational methods that can extract meaningful environmental features. Traditional approaches often struggle to capture the complex spatial relationships and variations present in such data. To address these challenges, we introduce a novel framework that integrates a spatially-aware deep learning architecture. Our model uses a multi-layer attention mechanism to learn localized spatial representations, allowing it to detect both broad geographic patterns and small-scale anomalies. We also integrate a knowledge-driven approach that draws on cartographic details, ecological zones, and socio-demographic data to guide the learning process through structured guidance and adaptive context. This combined method improves the model’s accuracy and ability to generalize while staying aligned with geographical reasoning. Experimental results indicate that our method surpasses existing approaches in predictive accuracy and interpretability, highlighting its potential to advance computational geography via innovative analytical techniques. The proposed model demonstrates strong generalization capabilities, referring to its ability to effectively learn and adapt to diverse geospatial contexts, including different geographical regions, environmental features, and sensor data types. This allows the model to perform robustly even in scenarios with limited or unbalanced training data, making it highly adaptable to real-world environmental applications.