Marine Debris Detection Using Satellite Images

Dibyaraj Mohapatra, Pritam Majumder, Sworna Kokila M. L.

2025

Abstract

Marine litter, particularly plastic litter, is a significant threat to marine ecosystems, affecting marine life, human health, and coastal economies. Plastic waste constantly accumulating in oceans has serious ecological impacts, such as habitat destruction, bioaccumulation of harmful substances, and interference with marine food webs. Micro plastics, however, are of long-term concern since they are consumed by marine organisms, thus entering human food chains through seafood consumption. This research proposes a deep learning-based approach using satellite imagery for marine debris detection and classification. Utilizing high-resolution remote sensing data, this method offers a cost-effective and scalable solution towards large-scale ocean pollution monitoring. Using convolutional neural networks (CNNs) for feature extraction and segmentation, our model is trained on datasets that are collected for varying ocean conditions, i.e., water depth, seasonal pattern, and geographic location. The generality of our deep learning model enables it to detect various types of trash, i.e., plastic debris, fish netting, and industrial waste, that are not detected by traditional monitoring systems. Through extensive experimentation, our model is observed to be better suited for detecting trash in various bodies of water, i.e., coastal areas, open sea, and estuaries, where the trash patterns vary due to ocean currents and human activities. Our research promotes environmental monitoring and policy-making by an automated and scalable system for the identification of marine waste, thus facilitating ocean management and conservation activities in sustainable ways. Real-time detection, tracking, and identification of marine waste facilitate policymakers, scientists, and conservation organizations to receive actionable information. Mass-scale detection and segregation capability promote an active response in the prevention of marine pollution and conservation of aquatic diversity. The technology is also capable of pollution hotspot detection, facilitating targeted cleanup and long-term mitigation efforts. The incorporation of artificial intelligence and satellite remote sensing in this research promotes a data-driven approach in marine conservation, challenging governments, scientists, and advocacy organizations to collaborate in maintaining ocean ecosystems for future generations.

Paper Citation

in Harvard Style

Mohapatra D., Majumder P. and L. S. (2025). Marine Debris Detection Using Satellite Images. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 657-661. DOI: 10.5220/0013903500004919

in Bibtex Style

@conference{icrdicct`2525,
author={Dibyaraj Mohapatra and Pritam Majumder and Sworna L.},
title={Marine Debris Detection Using Satellite Images},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={657-661},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013903500004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Marine Debris Detection Using Satellite Images
SN - 978-989-758-777-1
AU - Mohapatra D.
AU - Majumder P.
AU - L. S.
PY - 2025
SP - 657
EP - 661
DO - 10.5220/0013903500004919
PB - SciTePress