DOI: 10.57760/sciencedb.j00001.01312
title: Datasets of spatial extent and multi-source remote sensing feature samples of major global sea surface oil spill events in 2015-2024
title: 2015-2024年全球海上溢油特征多源遥感样本数据集

description: With the increase in the global shipping industry and offshore oil extraction activities, the marine environment faces serious pollution threats, with oil spills being particularly prominent. Oil spills on the sea surface not only cause serious damage to marine ecosystems, but also pose a threat to fishery resources and the livelihoods of coastal communities. Accurate monitoring of the characteristics of sea surface oil spill events is an important task for marine environmental protection and resource management. In this paper, we collect the global major marine oil spill events during the past decade, including the time, location, and type of the oil spill events. The multis-source data are used including Sentinel-1 radar remote sensing data, Sentinel-2 and Landsat-8 optical remote sensing data, and the time and location information of the oil spill events. We employ the support vector machine method to extract the oil spill extent, and construct the global multi-source remote sensing feature sample dataset of sea surface oil spill from 2015 to 2024. The dataset includes spatial extent data of 147 oil spill events, with a total oil spill area of 827.81 km², covering 13 major sea areas around the world. Oil spill sample points are generated, including 142,175 sample points with nine radar features and 71,618 sample points with four optical features. This dataset provides accurate information on the spatial extent of oil spill on the sea surface by matching with historical oil spill events, which is lacking in traditional oil spill inventory data, and could enhance the understanding of the spatial and temporal distribution characteristics of oil pollution. Moreover, the constructed oil spill sample data of optical and radar features could provide remote sensing information of samples at different times and under different geographic environments, which provides high-quality training samples for the development of the intelligent technology of oil spill detection and prediction.
description: With the increase in the global shipping industry and offshore oil extraction activities, the marine environment faces serious pollution threats, with oil spills being particularly prominent. Oil spills on the sea surface not only cause serious damage to marine ecosystems, but also pose a threat to fishery resources and the livelihoods of coastal communities. Accurate monitoring of the characteristics of sea surface oil spill events is an important task for marine environmental protection and resource management. In this paper, we collect the global major marine oil spill events during the past decade, including the time, location, and type of the oil spill events. The multis-source data are used including Sentinel-1 radar remote sensing data, Sentinel-2 and Landsat-8 optical remote sensing data, and the time and location information of the oil spill events. We employ the support vector machine method to extract the oil spill extent, and construct the global multi-source remote sensing feature sample dataset of sea surface oil spill from 2015 to 2024. The dataset includes spatial extent data of 147 oil spill events, with a total oil spill area of 827.81 km², covering 13 major sea areas around the world. Oil spill sample points are generated, including 142,175 sample points with nine radar features and 71,618 sample points with four optical features. This dataset provides accurate information on the spatial extent of oil spill on the sea surface by matching with historical oil spill events, which is lacking in traditional oil spill inventory data, and could enhance the understanding of the spatial and temporal distribution characteristics of oil pollution. Moreover, the constructed oil spill sample data of optical and radar features could provide remote sensing information of samples at different times and under different geographic environments, which provides high-quality training samples for the development of the intelligent technology of oil spill detection and prediction.