GuidedSAM‑Plume is an open‑source, expert‑guided framework that enhances the interactive segmentation of volcanic plumes and clouds using the zero‑shot, spatial prompt‑based capabilities of SAM2. It was validated on a challenging dataset with dedicated segmentation metrics and volcanological descriptors, showing that minimal user input can significantly improve performance over fully automatic solutions while maintaining reliable derived parameters. The framework requires no retraining, lowering adoption barriers, and its re‑annotated dataset, produced under expert guidance, offers a more standardized release for broader environmental science applications.
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...
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.
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...
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.
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.
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.
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:2608.29609v1 Announce Type: new Abstract: Semantic segmentation is a crucial task for understanding Mars, the most Earth-like planet in our solar system. However, it is challenging because the...
arXiv:2609.21241v1 Announce Type: cross Abstract: Producing accurate annotations for deep learning based image segmentation is both costly and labor intensive. This challenge is especially evident in...
CoralscapesV2 is an expanded dataset for coral reef visual scene understanding, increasing the number of fine‑grained classes from 39 to 95 and adding 65,000 exhaustive fish instance masks. It supports panoptic segmentation by providing high‑quality semantic and instance labels across diverse, unconstrained reef imagery. The dataset serves as a challenging benchmark for modern segmentation models and enables broader applications such as benthic cover mapping and automated fish‑reef interaction analysis.
arXiv:2606. 15786v1 Announce Type: cross Abstract: The advent of large pretrained foundation models for computer vision has significantly improved the efficiency of visual data interpretation.
The paper introduces CASID, a large-scale climate‑aware satellite image dataset for land‑cover semantic segmentation, comprising 980 5,000×5,000‑pixel images from 30 Asian regions across four climate zones (temperate monsoon, subtropical monsoon, tropical monsoon, and tropical rainforest). It is the first dataset to explicitly address domain gaps caused by climatic diversity, providing detailed analyses of image features, textures, category distributions, spectral values, and object shapes for each climate domain. Extensive experiments on CASID benchmark state‑of‑the‑art segmentation and unsupervised domain adaptation methods, establishing a robust baseline for future research.