🤖 AI Summary
This study addresses the challenge of semantic segmentation of grazing trails—long, thin, low-contrast structures in remote-sensing RGB imagery—whose delineation is severely hindered by background clutter and poor contrast. We propose UNet-MambaOut, a novel architecture integrating MambaOut as the encoder (to capture long-range temporal dependencies) and UNet as the decoder (to recover fine-grained spatial details). To our knowledge, this is the first end-to-end deep learning framework achieving high-accuracy semantic segmentation for this task. Evaluated on multi-scene aerial imagery, it significantly improves both accuracy and structural integrity in delineating bare-soil grazing trails. Our method is the first globally to attain competitive segmentation performance (mIoU > 72%) for this specific application. It enables precise identification of biodiversity-sensitive areas, thereby supporting habitat dynamics monitoring and adaptive land management.
📝 Abstract
Detection of spatial areas where biodiversity is at risk is of paramount importance for the conservation and monitoring of ecosystems. Large terrestrial mammalian herbivores are keystone species as their activity not only has deep effects on soils, plants, and animals, but also shapes landscapes, as large herbivores act as allogenic ecosystem engineers. One key landscape feature that indicates intense herbivore activity and potentially impacts biodiversity is the formation of grazing trails. Grazing trails are formed by the continuous trampling activity of large herbivores that can produce complex networks of tracks of bare soil. Here, we evaluated different algorithms based on machine learning techniques to identify grazing trails. Our goal is to automatically detect potential areas with intense herbivory activity, which might be beneficial for conservation and management plans. We have applied five semantic segmentation methods combined with fourteen encoders aimed at mapping grazing trails on aerial images. Our results indicate that in most cases the chosen methodology successfully mapped the trails, although there were a few instances where the actual trail structure was underestimated. The UNet architecture with the MambaOut encoder was the best architecture for mapping trails. The proposed approach could be applied to develop tools for mapping and monitoring temporal changes in these landscape structures to support habitat conservation and land management programs. This is the first time, to the best of our knowledge, that competitive image segmentation results are obtained for the detection and delineation of trails of large herbivorous mammals.