Unsustainable fishing practices worldwide pose a major threat to marine resources and ecosystems. Identifying vessels that do not show up in conventional monitoring systems -- known as ``dark vessels'' -- is key to managing and securing the health of marine environments. With the rise of satellite-based synthetic aperture radar (SAR) imaging and modern machine learning (ML), it is now possible to automate detection of dark vessels day or night, under all-weather conditions. SAR images, however, require a domain-specific treatment and are not widely accessible to the ML community. Maritime objects (vessels and offshore infrastructure) are relatively small and sparse, challenging traditional computer vision approaches. We present the largest labeled dataset for training ML models to detect and characterize vessels and ocean structures in SAR imagery. xView3-SAR consists of nearly 1,000 analysis-ready SAR images from the Sentinel-1 mission that are, on average, 29,400-by-24,400 pixels each. The images are annotated using a combination of automated and manual analysis. Co-located bathymetry and wind state rasters accompany every SAR image. We also provide an overview of the xView3 Computer Vision Challenge, an international competition using xView3-SAR for ship detection and characterization at large scale. We release the data (\href{https://iuu.xview.us/}{https://iuu.xview.us/}) and code (\href{https://github.com/DIUx-xView}{https://github.com/DIUx-xView}) to support ongoing development and evaluation of ML approaches for this important application.
翻译:世界各地的不可持续捕捞做法对海洋资源和生态系统构成重大威胁。识别在常规监测系统(称为“达克船”)中未露面的船只是管理和确保海洋环境健康的关键。随着卫星合成孔径雷达(SAR)成像和现代机器学习(ML)的兴起,现在有可能在全天候条件下每天或夜间对暗船进行自动检测;但SAR图像需要针对具体域的处理,并且不能为ML社区广泛获得。海洋物体(船只和近海基础设施)相对小而稀少,对传统的计算机视觉方法提出了挑战。我们提供了最大的标记数据集,用于培训ML模型,以便在SAR图像中检测和定性船舶和海洋结构。xVSAR3SAR3由近1,000个经过分析的合成孔径雷达图像组成。Sentinel-1飞行任务每个平均有29,400比24,400平等素。图像使用自动化和手工分析的组合来附加说明。共同定位的测深方法和风状态观测工具与SAR图像相配套。我们还概述了在THARCV3中进行大规模测量和公布SARSARSA。