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The visual similarity of oil slick and other elements, known as look-alike, affects the reliability of synthetic aperture radar (SAR) images for marine oil spill detection. So far, detection and discrimination of oil spill and look-alike are still limited to the use of traditional machine learning algorithms and semantic segmentation deep learning models with limited accuracy. Thus, this study developed a novel deep learning oil spill detection model using computer vision instance segmentation Mask-Region-based Convolutional Neural Network (Mask R-CNN) model. The model training was conducted using transfer learning on the ResNet 101 on COCO as backbone in combination with Feature Pyramid Network (FPN) architecture for feature extraction at 30 epochs with 0.001 learning rate. Testing of the model was conducted using the least training and validation loss value on the withheld testing images. The model's performance was evaluated using precision, recall, specificity, IoU, F1-measure and overall accuracy values. Ship detection and segmentation had the highest performance with overall accuracy of 98.3%. The model equally showed a higher accuracy for oil spill and look-alike detection and segmentation although oil spill detection outperformed look-alike with overall accuracy values of 96.6% and 91.0% respectively. The study concluded that the deep learning instance segmentation model performs better than conventional machine learning models and deep learning semantic segmentation models in detection and segmentation. [ABSTRACT FROM AUTHOR]

Proper pipeline route selection is an integral component of a typical oil exploration and transportation project. Improperly selected routes could have severe consequences including pipe failures, oil spillage, and environmental disasters. Consequently, technologies like geographic information systems (GIS) are increasingly being used to facilitate the oil pipeline route selection procedure—especially for onshore routing projects. Surprisingly, not much has been documented on the application of GIS to offshore pipeline routing. With recent discoveries on the merits of offshore oil exploration, it is necessary to extend the analytical capabilities of GIS to the unique offshore domain. However, concerns have been raised regarding the limitations of GIS in accurately prioritizing diverse selection criteria in typical multi-criteria decision-making (MCDM) problems like route selection. Consequently, this paper addresses the offshore/subsea pipeline routing constraint using a hybrid decision support system (DSS), which integrates a GIS and fuzzy logic-based approximate reasoning (AR) models for optimal performance. The resultant spatial decision support system (SDSS) was successfully applied to a case study in Malaysia. The AR algorithm calculated the significance level of the multiple criteria using various fuzzy linguistic variables and membership functions. The aggregated priority ranking from different pipeline routing experts showed that the overall influence of the environmental criteria (61.4%) significantly exceeded that of other equally important criteria in the study area. These rankings were inputted into the SDSS to simulate various probable routes. Final results accurately highlighted an optimal route, which places a premium on the protection of environmental features in the subsea study area—in alignment with the preferences of majority of the experts. [ABSTRACT FROM AUTHOR]

This study develops an oil spill environmental vulnerability model for predicting and mapping the oil slick trajectory pattern in Kota Tinggi, Malaysia. The impact of seasonal variations on the vulnerability of the coastal resources to oil spill was modelled by estimating the quantity of coastal resources affected across three climatic seasons (northeast monsoon, southwest monsoon and pre-monsoon). Twelve 100 m3 (10,000 splots) medium oil spill scenarios were simulated using General National Oceanic and Atmospheric Administration Operational Oil Modeling Environment (GNOME) model. The output was integrated with coastal resources, comprising biological, socio-economic and physical shoreline features. Results revealed that the speed of an oil slick (40.8 m per minute) is higher during the pre-monsoon period in a southwestern direction and lower during the northeast monsoon (36.9 m per minute). Evaporation, floating and spreading are the major weathering processes identified in this study, with approximately 70% of the slick reaching the shoreline or remaining in the water column during the first 24 h (h) of the spill. Oil spill impacts were most severe during the southwest monsoon, and physical shoreline resources are the most vulnerable to oil spill in the study area. The study concluded that variation in climatic seasons significantly influence the vulnerability of coastal resources to marine oil spill. Image 1 • Oil spill coastal sensitivity maps were developed considering climatic variations. • GNOME model was used for the prediction of the oil spill trajectory. • Geographical Information System Kernel density spatial analysis tool is significant for the development of vulnerability map. • The novel coastal environmental vulnerability maps are particularly useful for decision making in a changing climate. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Oil spills are a global phenomenon with impacts that cut across socio-economic, health, and environmental dimensions of the coastal ecosystem. However, comprehensive assessment of oil spill impacts and selection of appropriate remediation approaches have been restricted due to reliance on laboratory experiments which offer limited area coverage and classification accuracy. Thus, this study utilizes multispectral Landsat 8-OLI remote sensing imagery and machine learning models to assess the impacts of oil spills on coastal vegetation and wetland and monitor the recovery pattern of polluted vegetation and wetland in a coastal city. The spatial extent of polluted areas was also precisely quantified for effective management of the coastal ecosystem. Using Johor, a coastal city in Malaysia as a case study, a total of 49 oil spill (ground truth) locations, 54 non-oil-spill locations and Landsat 8-OLI data were utilized for the study. The ground truth points were divided into 70% training and 30% validation parts for the classification of polluted vegetation and wetland. Sixteen different indices that have been used to monitor vegetation and wetland stress in literature were adopted for impact and recovery analysis. To eliminate similarities in spectral appearance of oil-spill-affected vegetation, wetland and other elements like burnt and dead vegetation, Support Vector Machine (SVM) and Random Forest (RF) machine learning models were used for the classification of polluted and nonpolluted vegetation and wetlands. Model optimization was performed using a random search method to improve the models' performance, and accuracy assessments confirmed the effectiveness of the two machine learning models to identify, classify and quantify the area extent of oil pollution on coastal vegetation and wetland. Considering the harmonic mean (F1), overall accuracy (OA), User's accuracy (UA), and producers' accuracy (PA), both models have high accuracies. However, the RF outperformed the SVM with F1, OA, PA and UA values of 95.32%, 96.80%, 98.82% and 95.11%, respectively, while the SVM recorded accuracy values of F1 (80.83%), OA (92.87%), PA (95.18%) and UA (93.81%), respectively, highlighting 1205.98 hectares of polluted vegetation and 1205.98 hectares of polluted wetland. Analysis of the vegetation indices revealed that spilled oil had a significant impact on the vegetation and wetland, although steady recovery was observed between 2015-2018. This study concludes that Chlorophyll Vegetation Index, Modified Difference Water Index, Normalized Difference Vegetation Index and Green Chlorophyll Index vegetation indices are more sensitive for impact and recovery assessment of both vegetation and wetland, in addition to Modified Normalized Difference Vegetation Index for wetlands. Thus, remote sensing and Machine Learning models are essential tools capable of providing accurate information for coastal oil spill impact assessment and recovery analysis for appropriate remediation initiatives. [ABSTRACT FROM AUTHOR]