用多源卫星数据+机器学习，实时预警阿曼海岸赤潮风险
REDNET-ML: A Multi-Sensor Machine Learning Pipeline for Harmful Algal Bloom Risk Detection Along the Omani Coast
- 融合哨兵2号、MODIS数据与目标检测特征，构建多模态输入
- 采用CatBoost模型输出校准后的赤潮风险概率，AUROC达0.93
- 支持按站点和时间动态查看风险场，适合海洋监测机构使用
有害藻华（HABs）可能威胁沿海基础设施、渔业及海水淡化供水。本项目（REDNET-ML）开发了一个可复现的机器学习流水线，利用多源卫星数据与非泄漏评估，实现阿曼海岸赤潮风险检测。系统融合三类信号：(i) 哨兵2号光学影像（高空间分辨率）处理得到光谱指数与纹理特征；(ii) MODIS Level-3 海洋颜色与热力指标；(iii) 针对藻华模式训练的目标检测器提取的图像证据。通过一个紧凑的决策融合模型（CatBoost）将上述信号整合为校准后的赤潮风险概率，并接入端到端推理流程与风险场可视化工具，支持按站点（如水厂）和时间进行操作级探索。报告详细阐述了动机、相关工作、方法选择（包括标签挖掘与严格数据划分策略）、实现细节，以及基于AUROC/AUPRC、混淆矩阵、校准曲线和漂移分析的批判性评估，量化了近年数据分布偏移情况。
原文摘要 · Abstract (English)
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.
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