arXiv:2609.13332v1 Announce Type: new
Abstract: Automated landslide segmentation on Mars is one of the important tasks for understanding its surface processes, and all will aid in future space explor...
By Leo Thomas Ramos, Sidike Paheding, Abel A. Reyes-Angulo, Rajaneesh A., Sajinkumar K. S., Angel D. Sappa, Thomas Oommen
The paper presents a method that leverages sparse expert point annotations from historical benthic surveys to improve dense segmentation of marine imagery. By using these points as visual prompts for the SAM2 foundation model and introducing a mechanism to filter out unreliable points, the authors generate high‑quality pseudo‑ground‑truth masks that train more accurate fine‑grained semantic segmentation models. The approach is validated on public benthic datasets and a new benchmark featuring real‑world sparse annotations, aiming to enable scalable ecological analysis.
By Cesar Borja, Breck A. McCollum, Jarret E. Byrnes, Kenneth Sebens, Ana C. Murillo
arXiv:2505.15147v3 Announce Type: replace
Abstract: Remote sensing images (RSIs) capture both natural and human-induced changes on the Earth's surface. Semantic segmentation (SS) of RSIs enables the...
By Quanwei Liu, Tao Huang, Jiaqi Yang, Wei Xiang
The paper introduces PhySeg, a SAM-based segmentation framework that uses a lightweight refinement module to progressively restore fine-grained details via multi-scale feature fusion, improving phytoplankton segmentation accuracy. It also presents a newly annotated phytoplankton dataset with instance-level masks, offering a valuable resource for future research. Experimental results confirm the effectiveness of the proposed module, and the code is publicly available on GitHub.
By Jialin Li, Jinkun Jiang, Yuezun Li, Junyu Dong
The paper proposes a data-driven protocol that uses multispectral Landsat‑8 imagery and connected‑component analysis to characterize fire‑region size distributions for active wildfire segmentation. Three segmentation architectures—U‑Net, DeepLabV3+, and SegFormer—are evaluated under different SWIR‑based spectral configurations, with U‑Net showing the strongest robustness and SWIR2 consistently delivering the best results. The study highlights the importance of both spectral band selection and architectural design for robust satellite‑based active wildfire mapping, especially when training on low fire‑pixel density images.
By Matheus F. Kovaleski, Cristiano Premebida, Jo\~ao Ruivo Paulo
arXiv:2604. 10094v2 Announce Type: replace-cross Abstract: Anthropogenic methane (CH4) point sources are critical drivers of near-term climate forcing, safety hazards, and system-inefficiencies.
By Vishal V. Batchu, Michelangelo Conserva, Alex Wilson, Anna M. Michalak, Varun Gulshan, Philip G. Brodrick, Andrew K. Thorpe, Christopher V. Arsdale