用视觉语言模型实现藻华自动分割与严重程度评估
Seg the HAB: Language-Guided Geospatial Algae Bloom Reasoning and Segmentation
- 结合遥感图像与语言模型，实现藻华区域精准分割
- 在NASA的CAML数据集上实现高精度严重程度预测
- 适合环境监测、气候研究者使用
气候变化正加剧有害藻华（HAB），尤其是蓝藻的爆发，其通过耗氧、释放毒素和破坏海洋生物多样性威胁水生生态系统与人类健康。传统监测手段如人工采样劳动强度大，时空覆盖有限。近年来，视觉语言模型（VLMs）在遥感领域展现出规模化AI解决方案的潜力，但在图像推理与藻华严重程度量化方面仍面临挑战。本文提出ALGae Observation and Segmentation（ALGOS）系统，融合遥感图像理解与严重程度估计，采用GeoSAM辅助的人工评估进行高质量分割掩膜标注，并基于NASA提供的蓝藻聚合人工标签（CAML）对视觉语言模型进行微调。实验表明，ALGOS在分割与严重程度估计任务上均表现稳健，为实现实用化、自动化蓝藻监测系统铺平道路。
原文摘要 · Abstract (English)
Climate change is intensifying the occurrence of harmful algal bloom (HAB), particularly cyanobacteria, which threaten aquatic ecosystems and human health through oxygen depletion, toxin release, and disruption of marine biodiversity. Traditional monitoring approaches, such as manual water sampling, remain labor-intensive and limited in spatial and temporal coverage. Recent advances in vision-language models (VLMs) for remote sensing have shown potential for scalable AI-driven solutions, yet challenges remain in reasoning over imagery and quantifying bloom severity. In this work, we introduce ALGae Observation and Segmentation (ALGOS), a segmentation-and-reasoning system for HAB monitoring that combines remote sensing image understanding with severity estimation. Our approach integrates GeoSAM-assisted human evaluation for high-quality segmentation mask curation and fine-tunes vision language model on severity prediction using the Cyanobacteria Aggregated Manual Labels (CAML) from NASA. Experiments demonstrate that ALGOS achieves robust performance on both segmentation and severity-level estimation, paving the way toward practical and automated cyanobacterial monitoring systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。