Analysis of image segmentation methods in intelligent UAV modules for environmental monitoring

In the context of increasing environmental pressures and the growing complexity of the structural-functional configuration of natural landscapes, the development of autonomous aerial sensor platforms capable of highly automated interpretation of heterogeneous visual data in real time becomes particularly relevant. This study addresses the methodological and algorithmic aspects of image segmentation within onboard cognitive systems integrated into the architecture of unmanned aerial vehicles operating within the paradigm of ecologically oriented remote sensing. A cross-paradigm comparative analysis is conducted between traditional low-level segmentation procedures and modern deep neural network architectures capable of extracting high-level semantic descriptors under conditions of scene entropy and morphological heterogeneity. The results are presented in the form of recommendations for synthesizing resource-efficient, context-adaptive, and hardware-compatible solutions for the development of intelligent onboard systems for ecological monitoring in the new technological paradigm.