Seg the HAB: Language-Guided Geospatial Algae Bloom Reasoning and Segmentation
This work addresses the need for automated, scalable monitoring of cyanobacterial blooms to protect aquatic ecosystems and human health, representing an incremental improvement over existing vision-language models.
The paper tackles the problem of monitoring harmful algal blooms (HABs) by introducing ALGOS, a system that combines remote sensing image segmentation with severity estimation, achieving robust performance on both tasks.
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.