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High-resolution water mapping supports environmental monitoring and related applications, but accurate pixel-level labels are difficult and costly to produce. Official hydrographic vectors provide sca...
The paper presents a weakly supervised semantic segmentation approach for mapping seagrass habitats using side‑scan sonar imagery. By training a ViT‑based encoder‑decoder with image‑level labels, class activation maps are refined into pseudo‑labels and iteratively self‑trained, achieving high mean intersection‑over‑union scores (up to 89.3 %) without pixel‑level annotations. The method also benefits from self‑supervised pretraining and demonstrates generalizability in field trials.
arXiv:2608. 15790v1 Announce Type: new Abstract: Crevasse mapping from uncrewed aerial vehicle (UAV) imagery matters for glaciological research and for field safety in glaciated terrain.
arXiv:2606. 06524v1 Announce Type: cross Abstract: Accurate and scalable flood mapping remains challenging due to limited ground observations, heterogeneous terrain conditions, and the difficulty of enforcing hydrodynamic consistency within data-driven models.
arXiv:2507. 16849v3 Announce Type: replace-cross Abstract: We propose a vision transformer (ViT)-based deep learning framework to refine disaster-affected area segmentation from remote sensing imagery, aiming to support and enhance the Emergent Value Added Product (EVAP) developed by the Taiwan Space Agency (TASA).
arXiv:2608. 15647v1 Announce Type: cross Abstract: Semantic segmentation of very-high-resolution (VHR) remote sensing imagery increasingly benefits from strong pretrained hierarchical encoders, yet exploiting their multi-stage representations remains difficult.