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General Article

Enhancing oil spill classification for sustainable environment: Leveraging CNNs and self-attention mechanisms
Lavi Tyagi1
,
Dinesh Singh2*
,
Nitin Goyal3
1Research Scholar, Department of Computer Science and Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonipat (Haryana), India
2Professor, Department of Computer Science and Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonipat (Haryana), India
3Assistant Professor, Department of Computer Science and Engineering, School of Engineering and Technology, Central University of Haryana, Mahendergarh, 123031, Haryana, India
*Corresponding Author
31 March 2026. pp. 24-40
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Abstract

Effective detection and categorization are crucial for avoiding the adverse repercussions of oil spills, which pose a significant environmental hazard. This work presents two Deep Learning (DL) approaches to oil spill categorization: a standard Convolutional Neural Network (CNN) and an improved CNN with a self-attention mechanism. CNN uses deep feature extraction to differentiate between images with and without oil spills. Through enhancement in feature learning, the network takes advantage of the self-attention process, which it also employs to directly and precisely target critical regions of the image. The oil spill dataset comprises 2536 images. These models, both with and without attention mechanisms, demonstrated competitive results in terms of recall, accuracy, precision, and F1 score. The findings show that the self-attention model is more effective than the standard CNN. Potentially helpful for catastrophe management and environmental monitoring, it presents an innovative method to classify oil spills for a sustainable environment.

Keywords
oil spill
oil spill classification
convolutional neural networks
self-attention mechanism
DL
environmental monitoring
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Information
  • Publisher :Sustainable Building Research Center (ERC) Innovative Durable Building and Infrastructure Research Center
  • Publisher(Ko) :건설구조물 내구성혁신 연구센터
  • Journal Title :International Journal of Sustainable Building Technology and Urban Development
  • Volume : 17
  • No :1
  • Pages :24-40
  • Received Date : 2025-08-19
  • Accepted Date : 2025-09-06
  • DOI :https://doi.org/10.22712/susb.20260003
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