Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment

Abstract

Although advancements in remote sensing technology have facilitated quick capture and identification of the source and location of oil spills in water bodies, the presence of other biogenic elements (lookalikes) with similar visual attributes hinder rapid detection and prompt decision making for emergency response. To date, different methods have been applied to distinguish oil spills from lookalikes with limited success. In addition, accurately modeling the trajectory of oil spills remains a challenge. Thus, we aim to provide further insights on the multi-faceted problem by undertaking a holistic review of past and current approaches to marine oil spill disaster reduction as well as explore the potentials of emerging digital trends in minimizing oil spill hazards. The scope of previous reviews is extended by covering the inter-related dimensions of detection, discrimination, and trajectory prediction of oil spills for vulnerability assessment. Findings show that both optical and microwave airborne and satellite remote sensors are used for oil spill monitoring with microwave sensors being more widely used due to their ability to operate under any weather condition. However, the accuracy of both sensors is affected by the presence of biogenic elements, leading to false positive depiction of oil spills. Statistical image segmentation has been widely used to discriminate lookalikes from oil spills with varying levels of accuracy but the emergence of digitalization technologies in the fourth industrial revolution (IR 4.0) is enabling the use of Machine learning (ML) and deep learning (DL) models, which are more promising than the statistical methods. The Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the most used machine learning algorithms for oil spill detection, although the restriction of ML models to feed forward image classification without support for the end-to-end trainable framework limits its accuracy. On the other hand, deep learning models’ strong feature extraction and autonomous learning capability enhance their detection accuracy. Also, mathematical models based on lagrangian method have improved oil spill trajectory prediction with higher real time accuracy than the conventional worst case, average and survey-based approaches. However, these newer models are unable to quantify oil droplets and uncertainty in vulnerability prediction. Considering that there is yet no single best remote sensing technique for unambiguous detection and discrimination of oil spills and lookalikes, it is imperative to advance research in the field in order to improve existing technology and develop specialized sensors for accurate oil spill detection and enhanced classification, leveraging emerging geospatial computer vision initiatives.

The Gulf of Mexico in trouble: Big data solutions to climate change science

“…All ocean basins are experiencing sea level rise and warming due to climate change and global warming, so predicting and understanding the sea-level rise is also done using the machine learning approaches (Roshni et al, 2019;Morovati et al, 2021;Nieves et al, 2021;Tur et al, 2021). Remote sensing data is quite helpful in detecting the oil spills by analyzing satellite images manually, however machine learning models can also help automate the detection and tracking of oil spills (Estes and Senger, 1971;Kubat et al, 1998;Shamsudeen, 2020). While remote sensing data is used to detect oil spills for a larger area, oil spill detection in confined areas like ports is also carried out with the help of aerial vehicles, thermal infrared images and a trained convolutional neural networks (De Kerf et al, 2020).…”

Sustainable Marine Ecosystems: Deep Learning for Water Quality Assessment and Forecasting

“…Similar approach is presented in [71] where a CNN architecture is used to detect oil spills within the Bohai Bay of China. Further research on this topic, that extremely affect the water quality, can be obtained from this review [88]. Finally, the authors in [48] review two DL frameworks that carry out ocean remote-sensing-image classifications.…”

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