Deep Learning-Based Object Detection for Environmental Monitoring Using Big Data

Wenbo Lin^*

• Gansu Industrial Vocational and Technical College, Tianshui, China

Recent advances in artificial intelligence have transformed the way we analyze complex environmental data. However, high-dimensionality, spatiotemporal variability, and heterogeneous data sources continue to pose major challenges. In this work, we introduce the Environmental Graph-Aware Neural Network (EGAN), a novel framework designed to model and analyze large-scale, multi-modal environmental datasets. EGAN constructs a spatiotemporal graph representation that integrates physical proximity, ecological similarity, and temporal dynamics, and applies graph convolutional encoders to learn expressive spatial features. These are fused with temporal representations using attention mechanisms, enabling the model to dynamically capture relevant patterns across modalities. The framework is further enhanced by domaininformed learning strategies that incorporate physics-based constraints, meta-learning for regional adaptation, and uncertainty-aware predictions. Extensive experiments on four benchmark datasets demonstrate that our approach achieves state-of-the-art performance in environmental object detection, segmentation, and scene understanding, making it a robust and interpretable tool for real-world environmental monitoring applications.