Oil Spill Detection Using Machine Learning and Infrared Images

Kerf, Thomas De; Gladines, Jona; Sels, Seppe; Vanlanduit, Steve

doi:10.3390/rs12244090

Cited by 63 publications

(32 citation statements)

References 34 publications

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“…The accuracy of the SVM classification varies between 85.71 and 99.95%. Another deep learning approach is suggested by [57]. It presents a CNN-based novel framework to detect small oil spills inside a port using a thermal infrared camera mounted on a drone.…”

Section: Comparison To Latest State-of-the-art Techniquesmentioning

confidence: 99%

Recent Advances in Oil-Spill Monitoring Using Drone-Based Radar Remote Sensing

Hammoud¹,

Wehn²

2023

Environmental Sciences

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Oil spills are regrettably common and have socioeconomic implications on communities and disastrous consequences on the marine ecosystem and maritime life. The European Space Agency (ESA) has stated that worldwide spillage exceeds 4.5 million tons of oil annually, where 45% of the amount is due to operative discharges from ships. To alleviate the severity of oil spills and promptly react to such incidents, it is crucial to have oil-spill monitoring systems, which enable an effective contingency plan to dictate the best actions for dealing with oil spills. A quick and efficient intervention requires the (1) detection of oil slicks, (2) thickness estimation, and (3) oil classification. The European Maritime Safety Agency (EMSA) highlighted in 2016 the need to use drones as complementary systems supporting satellite maritime surveillance. While multiple sensors could be used, active radars appear to be prominent for oil spill monitoring. In this chapter, we present recent advances in drone-based radar remote sensing as an effective oil spill monitoring system. It shows from the system-level perspective the capability of radar systems on drones, using high spectral resolution and parallel scanning, to perform the above-required functionalities (1, 2, and 3) and provide valuable information to contain the damage.

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Section: Comparison To Latest State-of-the-art Techniquesmentioning

confidence: 99%

Recent Advances in Oil-Spill Monitoring Using Drone-Based Radar Remote Sensing

Hammoud¹,

Wehn²

2023

Environmental Sciences

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“…The researchers have come to the conclusion that by using UA and thermal infrared cameras, it is possible to detect the fact of oil spills quicker, thus reducing natural pollution and the cost of oil product collection works. Researchers suggest using UAs equipped with cameras with other capture spectra, such as shortwave infrared or hyperspectral imaging cameras (De Kerf, Gladines, Sels, Vanlanduit, 2020;Duan et al, 2020).…”

Section: Uas Historical Experiencementioning

confidence: 99%

Possibilities and Restrictions on the Use of Unmanned Aircraft Systems in Security Work in Latvia

Veinbergs

Kisnica

2021

EIS

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Unmanned aircraft systems (UAS) have become available not only in the military environment, but to every member of society. They can be effectively used in security work, although, if used unprofessionally, UA can pose a threat to the public or the protected object. The aim of the article is to find evidence that the use of UA for the protection of a security object can be of a technical nature. With the introduction of UA as a part of technical equipment for security work, it would be possible to immediately inspect the guarded object and track and/ or detain persons with criminal intent, or transfer the data obtained by the video camera recording system to the State Police. The primary data of the research was collected using the survey method, where the general set of research was compiled by security companies that have received the required license for the provision of Technical Security by the State Police. The experience of using UAV in Latvia and in the international environment has been analysed using the case study method; secondary data has been obtained from statistical databases and scientific literature, as well as from publicly available sources. The results of the research theoretically reflect the effectiveness of the use of UA and UAS in security work, which will provide an opportunity to reasonably conduct practical research in the security industry environment.

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“…These systems are already demonstrating utility in search and rescue operations (Castellano et al 2020b), including from open water (Lygouras et al 2019), flooded areas (Albanese, Sciancalepore, and Costa-Perez 2021), avalanches (Bejiga et al 2017), and dense forest (Yong and Yeong 2018). Additional applications include monitoring forest fires (Zhao et al 2018;Kinaneva et al 2019;, live mapping of floods in support of emergency services (Gebrehiwot et al 2019;Munawar et al 2021a,b;Hashemi-Beni and Gebrehiwot 2021), assessing structural damage (Kang and Cha 2018;Wu et al 2018;Bhowmick, Nagarajaiah, and Veeraraghavan 2020), monitoring chemical spills (Jiao, Jia, and Cai 2019;Ghorbani and Behzadan 2020;De Kerf et al 2020), and monitoring reef (Ridge et al 2020) and coastal sand dune (Choi et al 2017) erosion. In all of these applications, low-latency dissemination of results and accurate geolocation is essential to be useful for decision makers.…”

Section: Introductionmentioning

confidence: 99%

ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and Response with AI

Dávila¹,

VanPelt²,

Lynch³

et al. 2022

Preprint

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Small unmanned aircraft systems (sUAS) are becoming prominent components of many humanitarian assistance and disaster response (HADR) operations. Pairing sUAS with onboard artificial intelligence (AI) substantially extends their utility in covering larger areas with fewer support personnel. A variety of missions, such as search and rescue, assessing structural damage, and monitoring forest fires, floods, and chemical spills, can be supported simply by deploying the appropriate AI models. However, adoption by resourceconstrained groups, such as local municipalities, regulatory agencies, researchers, and indigenous persons, has been hampered by the lack of a cost-effective, readily-accessible baseline platform that can be easily adapted to their unique missions. To fill this gap, we have developed the fully free and open-source ADAPT multi-mission payload for deploying real-time AI and computer vision onboard a sUAS during local and beyond-line-of-site missions. We have emphasized a modular design with low-cost, readily-available components, open-source software, and thorough documentation (https: //kitware.github.io/adapt/). The system integrates an inertial navigation system, high-resolution color camera, computer, and wireless downlink to process imagery and broadcast georegistered analytics back to a ground station. Our goal is to make it easy for the HADR community to build their own copies of the ADAPT payload and leverage the thousands of hours of non-recurring engineering we have devoted to developing and testing this general-purpose capability. In this paper, we detail the development and testing of the ADAPT payload. We also demonstrate the example mission of real-time, in-flight ice segmentation to monitor river ice state and provide more-timely predictions of catastrophic flooding events. We deploy a novel active learning workflow to annotate river ice imagery, train a real-time deep neural network for ice segmentation, and demonstrate operation during field testing.

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Oil Spill Detection Using Machine Learning and Infrared Images

Cited by 63 publications

References 34 publications

Recent Advances in Oil-Spill Monitoring Using Drone-Based Radar Remote Sensing

Recent Advances in Oil-Spill Monitoring Using Drone-Based Radar Remote Sensing

Possibilities and Restrictions on the Use of Unmanned Aircraft Systems in Security Work in Latvia

ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and Response with AI

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