Adaptive Terrain-Cognizant Multispectral Vision and Distributed Intelligence for Low-Altitude Unmanned Systems in Yunnan Plateau and Canyon Ecosystems

Abstract

The rapid expansion of the low-altitude economy across southwestern China necessitates autonomous unmanned aerial systems capable of sustained operations within deeply dissected topography and hyperdiverse ecosystems. This study presents a unified framework, designated LAVENS, that integrates terrain-cognizant adaptive visual odometry with hierarchical spectral attention networks to simultaneously address navigation robustness and ecological inference accuracy in complex Yunnan terrains. A terrain roughness adaptive factor is embedded within an extended Kalman filtering pipeline to modulate measurement noise covariance in real time, thereby mitigating scale drift during canyon traversal. Complementarily, a hierarchical spectral attention network processes ten-band multispectral tensors through scaled dot-product self-attention to resolve vegetation boundaries under heterogeneous canopy illumination. Edge-cloud orchestration, based on directed acyclic graph scheduling, ensures end-to-end latency remains below 150 milliseconds while conforming to low-altitude airspace traffic management protocols. Extensive validation spanning six altitudinal belts—from tropical rainforest to nival rock—demonstrates that the proposed methodology reduces absolute trajectory error by a mean of 41.3 percent relative to current open-source SLAM libraries, and improves mean intersection-over-union for canopy species segmentation by 18.7 percent over conventional encoder-decoder architectures. These findings establish a scalable software and hardware architecture for ecological logistics and infrastructure inspection within the low-altitude economic corridor of the Yunnan plateau.