Up-to-date catalogs of the urban tree population are important for
municipalities to monitor and improve quality of life in cities. Despite much
research on automation of tree mapping, mainly relying on dedicated airborne
LiDAR or hyperspectral campaigns, trees are still mostly mapped manually in
practice. We present a fully automated tree detection and species recognition
pipeline to process thousands of trees within a few hours using publicly
available aerial and street view images of Google MapsTM. These data provide
rich information (viewpoints, scales) from global tree shapes to bark textures.
Our work-flow is built around a supervised classification that automatically
learns the most discriminative features from thousands of trees and
corresponding, public tree inventory data. In addition, we introduce a change
tracker to keep urban tree inventories up-to-date. Changes of individual trees
are recognized at city-scale by comparing street-level images of the same tree
location at two different times. Drawing on recent advances in computer vision
and machine learning, we apply convolutional neural networks (CNN) for all
classification tasks. We propose the following pipeline: download all available
panoramas and overhead images of an area of interest, detect trees per image
and combine multi-view detections in a probabilistic framework, adding prior
knowledge; recognize fine-grained species of detected trees. In a later,
separate module, track trees over time and identify the type of change. We
believe this is the first work to exploit publicly available image data for
fine-grained tree mapping at city-scale, respectively over many thousands of
trees. Experiments in the city of Pasadena, California, USA show that we can
detect > 70% of the street trees, assign correct species to > 80% for 40
different species, and correctly detect and classify changes in > 90% of the
cases.

本文分析了如何利用 Google 地图上的遥感图像进行城市规模的树木检测与物种识别，并提出了一种基于卷积神经网络的分类方法以及一种树木改变监测方法。通过实验证明该方法能够检测出超过 70% 的城市树木，正确地对超过 80% 的 40 种不同类型的树木物种进行分类，以及正确地检测并识别超过 90% 的变化操作。