Remote sensing colour image semantic segmentation of trails created by large herbivorous Mammals
This work addresses the need for mapping and tracking changes in grazing trails for biodiversity monitoring, but it is incremental as it applies existing semantic segmentation models to a new domain-specific dataset.
The study tackled the problem of automatically detecting grazing trails for ecosystem conservation by testing various machine learning methods, achieving the best results with a UNet combined with MambaOut encoder.
Identifying spatial regions where biodiversity is threatened is crucial for effective ecosystem conservation and monitoring. In this stydy, we assessed varios machine learning methods to detect grazing trails automatically. We tested five semantic segmentation models combined with 14 different encoder networks. The best combination was UNet with MambaOut encoder. The solution proposed could be used as the basis for tools aiming at mapping and tracking changes in grazing trails on a continuous temporal basis.