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

• URL: http://arxiv.org/abs/2603.04181v1
• Date: Wed, 04 Mar 2026 15:36:52 GMT
• Title: REDNET-ML: A Multi-Sensor Machine Learning Pipeline for Harmful Algal Bloom Risk Detection Along the Omani Coast
• Authors: Ameer Alhashemi,
• Abstract summary: Harmful algal blooms (HABs) can threaten coastal infrastructure, fisheries, and desalination dependent water supplies.<n>This project develops a reproducible machine learning pipeline for HAB risk detection along the Omani coastline.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Fusing Multi- and Hyperspectral Satellite Data for Harmful Algal Bloom Monitoring with Self-Supervised and Hierarchical Deep Learning [0.0]
  We present a self-supervised machine learning framework for detecting and mapping harmful algal bloom severity and speciation.<n>By fusing reflectance data from operational instruments with TROPOMI solar-induced fluorescence (SIF), our framework, called SIT-FUSE, generates HAB severity and speciation products without requiring per-instrument labeled datasets.<n>The framework employs self-supervised representation learning, hierarchical deep clustering to segment phytoplankton concentrations and speciations into interpretable classes, validated against in-situ data from the Gulf of Mexico and Southern California.
  arXiv  Detail & Related papers  (2025-10-03T06:51:19Z)
• Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future [119.88454942558485]
  Underwater object detection (UOD) aims to identify and localise objects in underwater images or videos. In recent years, artificial intelligence (AI) based methods, especially deep learning methods, have shown promising performance in UOD.
  arXiv  Detail & Related papers  (2024-10-08T00:25:33Z)
• Lazy Layers to Make Fine-Tuned Diffusion Models More Traceable [70.77600345240867]
  A novel arbitrary-in-arbitrary-out (AIAO) strategy makes watermarks resilient to fine-tuning-based removal. Unlike the existing methods of designing a backdoor for the input/output space of diffusion models, in our method, we propose to embed the backdoor into the feature space of sampled subpaths. Our empirical studies on the MS-COCO, AFHQ, LSUN, CUB-200, and DreamBooth datasets confirm the robustness of AIAO.
  arXiv  Detail & Related papers  (2024-05-01T12:03:39Z)
• Learning Heavily-Degraded Prior for Underwater Object Detection [59.5084433933765]
  This paper seeks transferable prior knowledge from detector-friendly images. It is based on statistical observations that, the heavily degraded regions of detector-friendly (DFUI) and underwater images have evident feature distribution gaps. Our method with higher speeds and less parameters still performs better than transformer-based detectors.
  arXiv  Detail & Related papers  (2023-08-24T12:32:46Z)
• Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
  Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems. This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
  arXiv  Detail & Related papers  (2023-08-14T16:48:57Z)
• Large-scale Detection of Marine Debris in Coastal Areas with Sentinel-2 [3.6842260407632903]
  Efforts to quantify marine pollution are often conducted with sparse and expensive beach surveys. Satellite data of coastal areas is readily available and can be leveraged to detect aggregations of marine debris containing plastic litter. We present a detector for marine debris built on a deep segmentation model that outputs a probability for marine debris at the pixel level.
  arXiv  Detail & Related papers  (2023-07-05T17:38:48Z)
• Spectral Analysis of Marine Debris in Simulated and Observed Sentinel-2/MSI Images using Unsupervised Classification [0.0]
  This study uses Radiative Transfer Model (RTM) simulated data and data from the Multispectral Instrument (MSI) of the Sentinel-2 mission in combination with machine learning algorithms. The results indicate that the spectral behavior of pollutants is influenced by factors such as the type of polymer and pixel coverage percentage. These insights can guide future research in remote sensing applications for detecting marine plastic pollution.
  arXiv  Detail & Related papers  (2023-06-26T18:46:47Z)
• Hyperspectral Remote Sensing Benchmark Database for Oil Spill Detection with an Isolation Forest-Guided Unsupervised Detector [13.739881592455044]
  Oil spill detection has attracted increasing attention in recent years since marine oil spill accidents severely affect environments, natural resources, and the lives of coastal inhabitants. Most of the existing approaches are based on supervised and semi-supervised frameworks to detect oil spills from hyperspectral images (HSIs) In this study, we make the first attempt to develop an unsupervised oil spill detection method based on isolation forest for HSIs.
  arXiv  Detail & Related papers  (2022-09-28T02:26:42Z)
• Boosting 3D Object Detection by Simulating Multimodality on Point Clouds [51.87740119160152]
  This paper presents a new approach to boost a single-modality (LiDAR) 3D object detector by teaching it to simulate features and responses that follow a multi-modality (LiDAR-image) detector. The approach needs LiDAR-image data only when training the single-modality detector, and once well-trained, it only needs LiDAR data at inference. Experimental results on the nuScenes dataset show that our approach outperforms all SOTA LiDAR-only 3D detectors.
  arXiv  Detail & Related papers  (2022-06-30T01:44:30Z)
• Cyber-resilience for marine navigation by information fusion and change detection [1.7205106391379026]
  Cyber-resilience is an increasing concern in developing autonomous navigation solutions for marine vessels. This paper scrutinizes cyber-resilience properties of marine navigation through a prism with three edges. It proposes a two-stage estimator for diagnosis and mitigation of sensor signals used for coastal navigation.
  arXiv  Detail & Related papers  (2022-02-01T12:56:02Z)
• Signal Processing and Machine Learning Techniques for Terahertz Sensing: An Overview [89.09270073549182]
  Terahertz (THz) signal generation and radiation methods are shaping the future of wireless systems. THz-specific signal processing techniques should complement this re-surged interest in THz sensing for efficient utilization of the THz band. We present an overview of these techniques, with an emphasis on signal pre-processing. We also address the effectiveness of deep learning techniques by exploring their promising sensing capabilities at the THz band.
  arXiv  Detail & Related papers  (2021-04-09T01:38:34Z)
• Learning Selective Sensor Fusion for States Estimation [47.76590539558037]
  We propose SelectFusion, an end-to-end selective sensor fusion module. During prediction, the network is able to assess the reliability of the latent features from different sensor modalities. We extensively evaluate all fusion strategies in both public datasets and on progressively degraded datasets.
  arXiv  Detail & Related papers  (2019-12-30T20:25:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.