Oil Spill Detection and Understanding Using Deep Learning and Large Language Models

Abstract The primary objective of this research work is to harness the advanced capabilities of Artificial Intelligence (AI), specifically Deep Learning (DL) and Large Language Models (LLMs), to develop a comprehensive system for detecting and understanding the causes of oil spills. Our approach involves utilizing deep learning algorithms to detect oil spill incidents from images, extracting relevant factors from these images, and feeding these factors into LLMs to determine the causality of the incidents. This research is motivated by the increasing frequency and environmental impact of oil spill events globally, and the lack of existing mechanisms to accurately monitor and explain these incidents. By enabling rapid detection and causality analysis, this system aims to enhance environmental protection efforts and prevent future oil spills through informed decision-making and timely intervention. The methodology of this study involves several critical steps. We began by utilizing an industrial dataset comprising labeled images of oil spills. Initial preprocessing steps included resizing and normalization of the images, followed by extensive data augmentation to enhance the dataset's robustness. We then employed advanced deep learning models, where images are considered as a grid of cells, with bounding boxes. We trained the Convolutional Neural Networks (CNNs) model to identify oil spill incidents by extracting key features from each image. These factors were then fed into a Large Language Model (LLM), to analyze and determine the underlying causes of the oil spills. The study demonstrates the effectiveness of integrating deep learning and LLMs in environmental monitoring and analysis. Our approach achieved a considerable increase in the accuracy of oil spill detection compared to traditional methods. Additionally, we attained a better accuracy rate in identifying contributory factors to oil spills. These results underscore the ecological importance of promptly identifying and mitigating oil spills, highlighting the system's potential to significantly enhance sustainable resource management strategies. By moving beyond traditional methods that focus solely on visual data, our innovative approach leverages LLMs to conduct a comprehensive analysis of oil spill causality. This integration allows for profound insights into the multifaceted nature of oil spills, addressing an urgent environmental concern with advanced AI methodologies.