Emily Dorne - Using Satellite Imagery to Identify Harmful Algal Blooms and Protect Public Health

SciFi (Cyanobacteria Finder) is an open-source Python package that uses Sentinel-2 satellite imagery and machine learning to detect harmful algal blooms in small inland water bodies

The system achieves similar accuracy to existing Sentinel-3 based tools while providing 10x more coverage of lakes across the US due to Sentinel-2’s higher resolution (10-30m vs 300-500m)

Decision trees (Light GBM) were chosen as the ML model due to their quick training time, interpretability, and strong performance without requiring GPUs

The system filters for water pixels only and uses a 2000m bounding box around sampling points to avoid false positives from land pixels

Visual examples and an interactive explorer tool help build user trust and understanding of the model’s predictions compared to raw metrics alone

SciFi is designed to augment, not replace, manual sampling by helping water quality managers better allocate resources and prioritize high-risk areas

The model performs best at detecting low and high severity cases, which aligns well with the primary use cases of ruling out low-risk areas and identifying urgent situations

Model validation using out-of-sample datasets was crucial for identifying when the model was getting correct predictions for wrong reasons

The system caches satellite imagery locally and can generate predictions within seconds to support regular monitoring

SciFi is not meant for fully automated decisions but rather to support human-in-the-loop workflows for water quality management and public health interventions