# Remote sensing colour image semantic segmentation of trails created by large herbivorous Mammals

Source: [Remote sensing colour image semantic segmentation of trails created by large herbivorous Mammals](<https://www.sota2.com/research/paper/68fa78961e209131cea79207>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:52:53.434Z

Authors: Jose Francisco Diez-Pastor, Francisco Javier Gonzalez-Moya, Pedro Latorre-Carmona, Francisco Javier Perez-Barber\\'ia, Ludmila I.Kuncheva, Antonio Canepa-Oneto, Alvar Arnaiz-Gonz\\'alez, Cesar Garcia-Osorio

Published: 2025-04-16T14:33:57.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2504.12121>)

[PDF](<https://arxiv.org/pdf/2504.12121.pdf>)