{"ID":2857085,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.10163","arxiv_id":"2510.10163","title":"SSeg: Active Sparse Point-Label Augmentation for Semantic Segmentation","abstract":"Semantic segmentation is essential for automating remote sensing analysis in fields like ecology. However, fine-grained analysis of complex aerial or underwater imagery remains an open challenge, even for state-of-the-art models. Progress is frequently hindered by the high cost of obtaining the dense, expert-annotated labels required for model supervision. While sparse point-labels are easier to obtain, they introduce challenges regarding which points to annotate and how to propagate the sparse information. We present SSeg, a novel framework that addresses both issues. SSeg first employs an active sampling strategy to guide annotators, maximizing the value of their point labels. Then, it propagates these sparse labels with a hybrid approach leveraging both the best of SAM2 and superpixel-based methods. Experiments on two diverse monitoring datasets demonstrate SSeg's benefits over state-of-the-art approaches. Our main contribution is a simple but effective interactive annotation tool integrating our algorithms. It enables ecology researchers to leverage foundation models and computer vision to efficiently generate high-quality segmentation masks to process their data.","short_abstract":"Semantic segmentation is essential for automating remote sensing analysis in fields like ecology. However, fine-grained analysis of complex aerial or underwater imagery remains an open challenge, even for state-of-the-art models. Progress is frequently hindered by the high cost of obtaining the dense, expert-annotated...","url_abs":"https://arxiv.org/abs/2510.10163","url_pdf":"https://arxiv.org/pdf/2510.10163v2","authors":"[\"Cesar Borja\",\"Carlos Plou\",\"Ruben Martinez-Cantin\",\"Ana C. Murillo\"]","published":"2025-10-11T10:56:48Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}