论文标题
使用多光谱卫星图像估算芝加哥的树皮和树冠高度
Estimating Chicago's tree cover and canopy height using multi-spectral satellite imagery
论文作者
论文摘要
有关城市树冠的信息对于缓解气候变化[1]以及改善生活质量[2]至关重要。城市植树计划面临缺乏有关城市树冠的水平和垂直尺寸的最新数据。我们提出了一条管道，该管道利用LiDAR数据作为地面真相，然后训练多任务机器学习模型，以使用多源的多光谱卫星图像为芝加哥的案例研究生成城市区域的树覆盖和冠层高度的可靠估计。
Information on urban tree canopies is fundamental to mitigating climate change [1] as well as improving quality of life [2]. Urban tree planting initiatives face a lack of up-to-date data about the horizontal and vertical dimensions of the tree canopy in cities. We present a pipeline that utilizes LiDAR data as ground-truth and then trains a multi-task machine learning model to generate reliable estimates of tree cover and canopy height in urban areas using multi-source multi-spectral satellite imagery for the case study of Chicago.
