mlsanityData to 14 Oct 2026

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

arXiv 2504.12121 ↗First posted 16 Apr 2025cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 60 references that are in the map. First posted on arXiv 16 Apr 2025.

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16 of 60 references.

References · 16

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4 more
  1. 2016Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysiscited by 93
  2. 2020Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenescited by 19
  3. 2022CitySurfaces: City-Scale Semantic Segmentation of Sidewalk Materialscited by 8
  4. 2023Neighborhood Attention Makes the Encoder of ResUNet Stronger for Accurate Road Extractioncited by 1

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