Аннотация
This study proposes a deep learning-enhanced excimer laser LiDAR framework for high-resolution Earth surface monitoring through the integration of multi-platform data sources, including UAV measurements, satellite imagery, and ground-based observations. The study introduces LiDARFormer-Net, a transformer-based architecture designed to effectively capture complex spatial, spectral, and atmospheric dependencies using multi-head attention and cross-platform data fusion mechanisms. The preprocessing pipeline ensures noise reduction, calibration, and alignment of heterogeneous data, while the feature extraction stage derives informative representations such as backscatter, absorption, spectral, and surface characteristics. Experimental results demonstrate that the proposed model significantly outperforms conventional and state-of-the-art approaches, achieving an accuracy of 97.32%, an RMSE of 0.053, an MAE of 0.042, and a coefficient of determination of 0.983. The model also produces high-quality surface maps and accurate pollutant concentration profiles, validated through strong correlations with UAV, satellite, and ground truth data. Ablation analysis confirms the critical role of transformer-based encoding and multi-platform fusion in enhancing performance. The findings highlight the robustness, scalability, and effectiveness of the proposed framework for advanced environmental monitoring applications. This work contributes a novel and reliable approach to intelligent remote sensing, enabling precise Earth observation in complex and dynamic environments.