Seg the HAB: Language-Guided Geospatial Algae Bloom Reasoning and Segmentation

TL;DR

This paper presents ALGOS, a vision-language model-based system for automated geospatial algae bloom segmentation and severity estimation, improving monitoring efficiency over traditional manual methods.

Contribution

It introduces ALGOS, combining remote sensing, human evaluation, and fine-tuning of vision-language models for accurate algae bloom segmentation and severity assessment.

Findings

ALGOS achieves high segmentation accuracy.

ALGOS effectively estimates bloom severity levels.

System demonstrates robustness in diverse conditions.

Abstract

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.