Remote sensing technologies and machine learning (ML) algorithms play an increasingly
important role in accurate detection and monitoring of oil spill slicks, assisting scientists in …

Oil spills are one of the most hazardous disasters with significant short-and long-term effects
on fragile marine ecosystems. Synthetic Aperture Radar (SAR) has been considered an …

Oil spill detection has attracted increasing attention in recent years, since marine oil spill
accidents severely affect environments, natural resources, and the lives of coastal …

Four oil spill events over the Indian Ocean including Chennai, Sharjah, Al Khiran and
Mubarak Village are analyzed using Sentinel-1 satellite data. General National Oceanic and …

This article describes the application of machine learning techniques to develop state-of-the-
art detection and prediction system for spatiotemporal events found within remote sensing …

Two object-based image analysis methods are developed for detecting oil spills from known
natural outflows as well as light oil spill events using Sentinel 2 imagery. The methods are …

In recent decades, geophysical and remote sensing monitoring techniques have advanced
to the point where they can be utilized. It is possible to investigate the spatiotemporal mass …

Detecting oil spills in marine environments is crucial for avoiding environmental damage
and facilitating rapid response efforts. In this study, we propose a robust method for oil spill …

A better understanding of the spatiotemporal distribution of water resources is crucial for the
sustainable development of hyper-arid regions. Here, we focus on the Arabian Peninsula …

Abstract Using fine spatial resolution (~ 7.6 m) hyperspectral AVIRIS data collected over the
Deepwater Horizon oil spill in the Gulf of Mexico, we statistically estimated slick lengths …