Oil Spill Detection and Mitigation

Neueste Paper

• UAV-Based Environmental Monitoring of Rip-Current Indicators Using Wavelet-Derived Texture Features

  Yonatan Ben Avraham, Baruch Binyaminov, Yehudit Aperstein · 4. August 2026

  Rip currents are recurrent coastal natural hazards that threaten beachgoers and create operational challenges for lifeguards and coastal managers. Reliable monitoring from standard RGB (red-green-blue) imagery acquired by unmanned aerial vehicles (UAVs) remains difficult because hazardous channels o…

• Toward Near-Real-Time Marine Oil Spill Detection in SAR Imagery using Quantum-Assisted SVM

  Joseph Strauss, Jyotsna Sharma · 19. Mai 2026

  Marine oil spills require rapid detection to mitigate severe ecological and economic damage. While satellite-based Synthetic Aperture Radar (SAR) provides essential all-weather monitoring, analyzing this data remains challenging. Deep learning models often require massive datasets and incur high lat…

• LEMMA: Laplacian pyramids for Efficient Marine SeMAntic Segmentation

  Ishaan Gakhar, Laven Srivastava, Sankarshanaa Sagaram, Aditya Kasliwal, Ujjwal Verma · 27. März 2026

  Semantic segmentation in marine environments is crucial for the autonomous navigation of unmanned surface vessels (USVs) and coastal Earth Observation events such as oil spills. However, existing methods, often relying on deep CNNs and transformer-based architectures, face challenges in deployment d…

• OilSAM2: Memory-Augmented SAM2 for Scalable SAR Oil Spill Detection

  Shuaiyu Chen, Ming Yin, Peng Ren, Chunbo Luo, Zeyu Fu · 12. März 2026

  Segmenting oil spills from Synthetic Aperture Radar (SAR) imagery remains challenging due to severe appearance variability, scale heterogeneity, and the absence of temporal continuity in real world monitoring scenarios. While foundation models such as Segment Anything (SAM) enable prompt driven segm…

• physfusion: A Transformer-based Dual-Stream Radar and Vision Fusion Framework for Open Water Surface Object Detection

  Yuting Wan, Liguo Sun, Jiuwu Hao, Zao Zhang, Pin LV · 3. März 2026

  Detecting water-surface targets for Unmanned Surface Vehicles (USVs) is challenging due to wave clutter, specular reflections, and weak appearance cues in long-range observations. Although 4D millimeter-wave radar complements cameras under degraded illumination, maritime radar point clouds are s…

• SAR-Based Marine Oil Spill Detection Using the DeepSegFusion Architecture

  Pavan Kumar Yata, Pediredla Pradeep, Goli Himanish, Swathi M · 20. Januar 2026

  Detection of oil spills from satellite images is essential for both environmental surveillance and maritime safety. Traditional threshold-based methods frequently encounter performance degradation due to very high false alarm rates caused by look-alike phenomena such as wind slicks and ship wakes. H…

• Beyond Segmentation: An Oil Spill Change Detection Framework Using Synthetic SAR Imagery

  Chenyang Lai, Shuaiyu Chen, Tianjin Huang, Siyang Song, Guangliang Cheng, Chunbo Luo, Zeyu Fu · 6. Januar 2026

  Marine oil spills are urgent environmental hazards that demand rapid and reliable detection to minimise ecological and economic damage. While Synthetic Aperture Radar (SAR) imagery has become a key tool for large-scale oil spill monitoring, most existing detection methods rely on deep learning-based…