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Informative Article | Data & Knowledge Engineering | India | Volume 11 Issue 1, January 2022
Leveraging Satellite Imagery Data Analytics and Deep Learning for Real-Time Monitoring of Offshore Oil Spills
Abstract: Marine ecosystems, coastal neighborhoods, and economies face grave risks from oil spills in offshore areas. Prompt identification and swift action are key in effectively managing and lessening the impact of these ecological catastrophes. This paper introduces a method for the real-time surveillance of offshore oil spills through the analysis of satellite imagery data and the application of deep learning algorithms. It leverages sophisticated data analytics techniques, such as image preprocessing, the extraction of pertinent features, and the merging of data, to refine the satellite imagery's utility and informational value. To automatically identify and outline oil spills in the refined satellite images, a deep learning model based on Convolutional Neural Networks (CNNs) is crafted. This model undergoes training with a vast collection of annotated satellite photos, covering a wide array of oil spill situations and atmospheric conditions. The model?s ability to generalize and its adaptability to diverse geographic territories are enhanced through the implementation of transfer learning strategies. This monitoring solution combines the CNN model that has been trained with a pipeline processing geospatial data, facilitating the ongoing analysis of new satellite image feeds. Upon detecting an oil spill, this system issues alerts and dispenses crucial details like the spill's location, size, and predicted path, aiding in the swift coordination of response initiatives. The framework established here can be adapted for use in other areas, such as the surveillance of harmful algal blooms, sea debris, and unlawful fishing activities, thus fostering practices that support the sustainable stewardship of our oceans.
Keywords: Offshore oil spills, satellite imagery, data analytics, deep learning, Convolutional Neural Networks, real-time monitoring, environmental monitoring, disaster management
Edition: Volume 11 Issue 1, January 2022,
Pages: 1616 - 1627