论文标题
卫星监测地面塑料废物
Satellite Monitoring of Terrestrial Plastic Waste
论文作者
论文摘要
塑料废物是一种很难监测的重要环境污染物。我们创建了一个神经网络系统，以分析Sentinel-2卫星数据的光谱，空间和时间成分，以识别废物的地面聚集。该系统在大陆规模上工作。我们评估了印度尼西亚的性能，并检测到374个废物聚集，是公共数据库中发现的站点数量的两倍以上。在东南亚十二个国家 /地区部署的同一系统随后确定了996个垃圾场。对于每个检测到的站点，我们会通过时间和交叉引用其他数据集监视废物站点的占地面积，以生成物理和社交元数据。 19％的检测废物位置位于水道200 m以内。许多地点直接坐在河岸上，海洋泄漏的风险很高。
Plastic waste is a significant environmental pollutant that is difficult to monitor. We created a system of neural networks to analyze spectral, spatial, and temporal components of Sentinel-2 satellite data to identify terrestrial aggregations of waste. The system works at continental scale. We evaluated performance in Indonesia and detected 374 waste aggregations, more than double the number of sites found in public databases. The same system deployed across twelve countries in Southeast Asia identifies 996 subsequently confirmed waste sites. For each detected site, we algorithmically monitor waste site footprints through time and cross-reference other datasets to generate physical and social metadata. 19% of detected waste sites are located within 200 m of a waterway. Numerous sites sit directly on riverbanks, with high risk of ocean leakage.
