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REDNET-ML: A Multi-Sensor Machine Learning Pipeline for Harmful Algal Bloom Risk Detection Along the Omani Coast

Ameer Alhashemi

TL;DR

This project (REDNET-ML) develops a reproducible machine learning pipeline for HAB risk detection along the Omani coastline using multi sensor satellite data and non leaky evaluation.

Abstract

Harmful algal blooms (HABs) can threaten coastal infrastructure, fisheries, and desalination dependent water supplies. This project (REDNET-ML) develops a reproducible machine learning pipeline for HAB risk detection along the Omani coastline using multi sensor satellite data and non leaky evaluation. The system fuses (i) Sentinel-2 optical chips (high spatial resolution) processed into spectral indices and texture signals, (ii) MODIS Level-3 ocean color and thermal indicators, and (iii) learned image evidence from object detectors trained to highlight bloom like patterns. A compact decision fusion model (CatBoost) integrates these signals into a calibrated probability of HAB risk, which is then consumed by an end to end inference workflow and a risk field viewer that supports operational exploration by site (plant) and time. The report documents the motivation, related work, methodological choices (including label mining and strict split strategies), implementation details, and a critical evaluation using AUROC/AUPRC, confusion matrices, calibration curves, and drift analyses that quantify distribution shift in recent years.

REDNET-ML: A Multi-Sensor Machine Learning Pipeline for Harmful Algal Bloom Risk Detection Along the Omani Coast

TL;DR

This project (REDNET-ML) develops a reproducible machine learning pipeline for HAB risk detection along the Omani coastline using multi sensor satellite data and non leaky evaluation.

Abstract

Harmful algal blooms (HABs) can threaten coastal infrastructure, fisheries, and desalination dependent water supplies. This project (REDNET-ML) develops a reproducible machine learning pipeline for HAB risk detection along the Omani coastline using multi sensor satellite data and non leaky evaluation. The system fuses (i) Sentinel-2 optical chips (high spatial resolution) processed into spectral indices and texture signals, (ii) MODIS Level-3 ocean color and thermal indicators, and (iii) learned image evidence from object detectors trained to highlight bloom like patterns. A compact decision fusion model (CatBoost) integrates these signals into a calibrated probability of HAB risk, which is then consumed by an end to end inference workflow and a risk field viewer that supports operational exploration by site (plant) and time. The report documents the motivation, related work, methodological choices (including label mining and strict split strategies), implementation details, and a critical evaluation using AUROC/AUPRC, confusion matrices, calibration curves, and drift analyses that quantify distribution shift in recent years.
Paper Structure (27 sections, 6 equations, 7 figures, 3 tables, 1 algorithm)

This paper contains 27 sections, 6 equations, 7 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction and Motivation
  2. Project Background and Context
  3. Remote sensing for HAB monitoring
  4. Spectral indices and contextual predictors
  5. Learning from images: detectors as evidence generators
  6. Evaluation pitfalls: leakage and temporal shift
  7. Methodology and Specifications
  8. System specification
  9. Data acquisition and AOI design
  10. Sentinel-2 chipping and feature extraction
  11. Label strategy: trusted labels and label mining
  12. Decision fusion model and operating policy
  13. Implementation Details
  14. Repository organization and reproducibility
  15. Architecture overview
  16. ...and 12 more sections

Figures (7)

  • Figure 1: Study area and Sentinel-2 MGRS tiles used for the HAB fusion model, with plant AOIs (A--D) shown for plant-centric chipping and aggregation.
  • Figure 2: OCI-context composites used by REDNET-ML: MODIS-Aqua chlorophyll-a (left) and SST (right) over the Oman domain (2024 aggregate).
  • Figure 3: REDNET-ML architecture: multi-sensor evidence generation, non-leaky dataset construction, CatBoost decision fusion, and operational risk outputs.
  • Figure 4: REDNET HAB Ops Console (full GUI) used for operational exploration of plant risk, monthly context, and event-level evidence.
  • Figure 5: Pooled ROC and PR curves.
  • ...and 2 more figures