Explainable oil spill detection using UAV imagery

Gelayol Golcarenarenji; Eslam Amer; Alaa Mohasseb; Tamer Elboghdadly

doi:10.1109/MIUCC66482.2025.11196871

Explainable oil spill detection using UAV imagery

Gelayol Golcarenarenji, Eslam Amer, Alaa Mohasseb, Tamer Elboghdadly

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

Oil and gas extraction significantly contributes to environmental pollution, making fast and accurate oil spill detection vital for protecting ecosystems and human health. UAV-based aerial imaging offers a practical solution by providing high-resolution, real-time monitoring with better flexibility and lower costs. UAVs can reach remote areas, process data quickly, and reduce risks to humans. When combined with Artificial Intelligence (AI), these systems become effective tools for rapid oil spill detection and response. However, current AI models often struggle with a trade-off between real-time performance and accuracy in real-world scenarios. To address this, we propose a lightweight and accurate CNN-based model for oil spill detection using UAV imagery. Using the publicly available Oil Spill Drone Dataset, the model achieved 95.4% accuracy and 41.14 FPS on the Jetson AGX Orin platform at 15W power mode, demonstrating its effectiveness in real-world oil spill detection missions

Original language	English
Title of host publication	Proceedings of 5th International Mobile, Intelligent, and Ubiquitous Computing Conference 17/09/25 → 18/09/25 Cairo, Egypt
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	6
ISBN (Electronic)	9798331539221
ISBN (Print)	9798331539238
DOIs	https://doi.org/10.1109/MIUCC66482.2025.11196871
Publication status	Published - 21 Oct 2025
Event	5th International Mobile, Intelligent, and Ubiquitous Computing Conference - Misr International University, Cairo, Egypt Duration: 17 Sept 2025 → 18 Sept 2025 Conference number: 5 https://www.aconf.org/conf_217003.2025_International_Mobile,_Intelligent,_and_Ubiquitous_Computing_Conference_(MIUCC).html

Conference

Conference	5th International Mobile, Intelligent, and Ubiquitous Computing Conference
Abbreviated title	MIUCC
Country/Territory	Egypt
City	Cairo
Period	17/09/25 → 18/09/25
Internet address	https://www.aconf.org/conf_217003.2025_International_Mobile,_Intelligent,_and_Ubiquitous_Computing_Conference_(MIUCC).html

Keywords

Oil spills
Image segmentation
UAV
Deep learning
Explainability

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/MIUCC66482.2025.11196871

Explainable_Oil_Spill_Detection_Using_UAV_ImageryAccepted author manuscript (Post-print), 1.63 MBLicence: CC BY

Cite this

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title = "Explainable oil spill detection using UAV imagery",

abstract = "Oil and gas extraction significantly contributes to environmental pollution, making fast and accurate oil spill detection vital for protecting ecosystems and human health. UAV-based aerial imaging offers a practical solution by providing high-resolution, real-time monitoring with better flexibility and lower costs. UAVs can reach remote areas, process data quickly, and reduce risks to humans. When combined with Artificial Intelligence (AI), these systems become effective tools for rapid oil spill detection and response. However, current AI models often struggle with a trade-off between real-time performance and accuracy in real-world scenarios. To address this, we propose a lightweight and accurate CNN-based model for oil spill detection using UAV imagery. Using the publicly available Oil Spill Drone Dataset, the model achieved 95.4\% accuracy and 41.14 FPS on the Jetson AGX Orin platform at 15W power mode, demonstrating its effectiveness in real-world oil spill detection missions",

keywords = "Oil spills, Image segmentation, UAV, Deep learning, Explainability",

author = "Gelayol Golcarenarenji and Eslam Amer and Alaa Mohasseb and Tamer Elboghdadly",

note = "No ISSN; 5th International Mobile, Intelligent, and Ubiquitous Computing Conference , MIUCC ; Conference date: 17-09-2025 Through 18-09-2025",

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isbn = "9798331539238",

booktitle = "Proceedings of 5th International Mobile, Intelligent, and Ubiquitous Computing Conference 17/09/25 → 18/09/25 Cairo, Egypt",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

url = "https://www.aconf.org/conf\_217003.2025\_International\_Mobile,\_Intelligent,\_and\_Ubiquitous\_Computing\_Conference\_(MIUCC).html",

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Golcarenarenji, G , Amer, E , Mohasseb, A & Elboghdadly, T 2025, Explainable oil spill detection using UAV imagery. in Proceedings of 5th International Mobile, Intelligent, and Ubiquitous Computing Conference 17/09/25 → 18/09/25 Cairo, Egypt. Institute of Electrical and Electronics Engineers Inc., 5th International Mobile, Intelligent, and Ubiquitous Computing Conference , Cairo, Egypt, 17/09/25. https://doi.org/10.1109/MIUCC66482.2025.11196871

Explainable oil spill detection using UAV imagery. / Golcarenarenji, Gelayol ; Amer, Eslam ; Mohasseb, Alaa et al.
Proceedings of 5th International Mobile, Intelligent, and Ubiquitous Computing Conference 17/09/25 → 18/09/25 Cairo, Egypt. Institute of Electrical and Electronics Engineers Inc., 2025.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Explainable oil spill detection using UAV imagery

AU - Golcarenarenji, Gelayol

AU - Amer, Eslam

AU - Mohasseb, Alaa

AU - Elboghdadly, Tamer

N1 - Conference code: 5

PY - 2025/10/21

Y1 - 2025/10/21

N2 - Oil and gas extraction significantly contributes to environmental pollution, making fast and accurate oil spill detection vital for protecting ecosystems and human health. UAV-based aerial imaging offers a practical solution by providing high-resolution, real-time monitoring with better flexibility and lower costs. UAVs can reach remote areas, process data quickly, and reduce risks to humans. When combined with Artificial Intelligence (AI), these systems become effective tools for rapid oil spill detection and response. However, current AI models often struggle with a trade-off between real-time performance and accuracy in real-world scenarios. To address this, we propose a lightweight and accurate CNN-based model for oil spill detection using UAV imagery. Using the publicly available Oil Spill Drone Dataset, the model achieved 95.4% accuracy and 41.14 FPS on the Jetson AGX Orin platform at 15W power mode, demonstrating its effectiveness in real-world oil spill detection missions

AB - Oil and gas extraction significantly contributes to environmental pollution, making fast and accurate oil spill detection vital for protecting ecosystems and human health. UAV-based aerial imaging offers a practical solution by providing high-resolution, real-time monitoring with better flexibility and lower costs. UAVs can reach remote areas, process data quickly, and reduce risks to humans. When combined with Artificial Intelligence (AI), these systems become effective tools for rapid oil spill detection and response. However, current AI models often struggle with a trade-off between real-time performance and accuracy in real-world scenarios. To address this, we propose a lightweight and accurate CNN-based model for oil spill detection using UAV imagery. Using the publicly available Oil Spill Drone Dataset, the model achieved 95.4% accuracy and 41.14 FPS on the Jetson AGX Orin platform at 15W power mode, demonstrating its effectiveness in real-world oil spill detection missions

KW - Oil spills

KW - Image segmentation

KW - UAV

KW - Deep learning

KW - Explainability

UR - https://miucc.miuegypt.edu.eg/call-for-papers/

U2 - 10.1109/MIUCC66482.2025.11196871

DO - 10.1109/MIUCC66482.2025.11196871

M3 - Conference contribution

SN - 9798331539238

BT - Proceedings of 5th International Mobile, Intelligent, and Ubiquitous Computing Conference 17/09/25 → 18/09/25 Cairo, Egypt

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th International Mobile, Intelligent, and Ubiquitous Computing Conference

Y2 - 17 September 2025 through 18 September 2025

ER -

Explainable oil spill detection using UAV imagery

Abstract

Conference

Keywords

UN SDGs

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