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We propose a deep learning framework to detect and categorize oil spills in synthetic aperture radar (SAR) images at a large scale. By means of a carefully designed neural network model for image segmentation trained on an extensive…
Detection of oil spills from satellite images is essential for both environmental surveillance and maritime safety. Traditional threshold-based methods frequently encounter performance degradation due to very high false alarm rates caused…
Synthetic Aperture Radar (SAR) is the main instrument utilized for the detection of oil slicks on the ocean surface. In SAR images, some areas affected by ocean phenomena, such as rain cells, upwellings, and internal waves, or discharge…
Oil spill incidents pose severe threats to marine ecosystems and coastal environments, necessitating rapid detection and monitoring capabilities to mitigate environmental damage. In this paper, we demonstrate how artificial intelligence,…
The offshore wind energy sector is expanding rapidly, increasing the need for independent, high-temporal-resolution monitoring of infrastructure deployment and operation at global scale. While Earth Observation based offshore wind…
Successful implementation of oil spill segmentation in Synthetic Aperture Radar (SAR) images is vital for marine environmental protection. In this paper, we develop an effective segmentation framework named DGNet, which performs oil spill…
The high incidence of oil spills in port areas poses a serious threat to the environment, prompting the need for efficient detection mechanisms. Utilizing automated drones for this purpose can significantly improve the speed and accuracy of…
Effective oil spill segmentation in Synthetic Aperture Radar (SAR) images is critical for marine oil pollution cleanup, and proper image representation is helpful for accurate image segmentation. In this paper, we propose an effective oil…
The recent and ongoing expansion of marine infrastructure, including offshore wind farms, oil and gas platforms, artificial islands, and aquaculture facilities, highlights the need for effective monitoring systems. The development of robust…
Segmentation of marine oil spills in Synthetic Aperture Radar (SAR) images is a challenging task because of the complexity and irregularities in SAR images. In this work, we aim to develop an effective segmentation method which addresses…
Implementing precise detection of oil leaks in peak load equipment through image analysis can significantly enhance inspection quality and ensure the system's safety and reliability. However, challenges such as varying shapes of oil-stained…
Offshore wind farms represent a renewable energy source with a significant global growth trend, and their monitoring is strategic for territorial and environmental planning. This study's primary objective is to detect offshore wind plants…
Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years. However, they are usually trained only as single-purpose models to either segment land and water or delineate the…
Snow avalanches present significant risks to human life and infrastructure, particularly in mountainous regions, making effective monitoring crucial. Traditional monitoring methods, such as field observations, are limited by accessibility,…
Crude oil is an integral component of the world economy and transportation sectors. With the growing demand for crude oil due to its widespread applications, accidental oil spills are unfortunate yet unavoidable. Even though oil spills are…
Knowledge about frequency and location of snow avalanche activity is essential for forecasting and mapping of snow avalanche hazard. Traditional field monitoring of avalanche activity has limitations, especially when surveying large and…
In this paper, we explore the possibility of detecting polar lows in C-band SAR images by means of deep learning. Specifically, we introduce a novel dataset consisting of Sentinel-1 images divided into two classes, representing the presence…
Marine oil spills are urgent environmental hazards that demand rapid and reliable detection to minimise ecological and economic damage. While Synthetic Aperture Radar (SAR) imagery has become a key tool for large-scale oil spill monitoring,…
Rapid assessment after a natural disaster is key for prioritizing emergency resources. In the case of landslides, rapid assessment involves determining the extent of the area affected and measuring the size and location of individual…
In situ and remotely sensed observations have potential to facilitate data-driven predictive models for oceanography. A suite of machine learning models, including regression, decision tree and deep learning approaches were developed to…