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.
arXiv:2607.27585v2 Announce Type: replace Abstract: As primary consumers in the marine food chain, zooplankton play a crucial role in maintaining marine ecological balance. However, the Segment Anyth...
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:2608.29852v1 Announce Type: new Abstract: Automated maritime surveillance from satellite and aerial imagery requires large, precisely annotated datasets, which remain scarce for the instance-se...
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.
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:2609.18069v1 Announce Type: new Abstract: Underwater semantic segmentation is essential for marine ecosystem monitoring, yet remains challenging due to severe visual degradation. Light absorpti...
The paper introduces AquaOV255, a large-scale underwater segmentation dataset with 255 categories and over 20,000 images, and establishes the UOVSBench benchmark by combining it with five other datasets. It also presents Earth2Ocean, a training‑free open‑vocabulary segmentation framework that adapts terrestrial vision‑language models to underwater scenes using a Geometric‑guided Visual Mask Generator and a Category‑visual Semantic Alignment module. Experiments on UOVSBench show that Earth2Ocean significantly improves performance while keeping inference efficient.
arXiv:2608.24594v1 Announce Type: new Abstract: Submerged kelp forests are vital coastal ecosystems that support marine biodiversity and ecosystem dynamics, yet accurate underwater kelp segmentation...
arXiv:2601. 17039v2 Announce Type: replace-cross Abstract: Mangroves are critical for climate-change mitigation, requiring reliable monitoring for effective conservation.
arXiv:2606. 08206v1 Announce Type: cross Abstract: We present SegmentAnyTreeV2, a sensor- and platform-agnostic framework for semantic and instance segmentation of forest point clouds.
arXiv:2610.29047v1 Announce Type: new Abstract: Understanding the properties of fish populations is increasingly important. Analyzing fish length, width, and mass is integral to maintaining healthy s...
arXiv:2609.10371v1 Announce Type: new Abstract: High-resolution water mapping supports environmental monitoring and related applications, but accurate pixel-level labels are difficult and costly to p...