From Google Maps to a Fine-Grained Catalog of Street trees
Steve Branson, Jan Dirk Wegner, David Hall, Nico Lang, Konrad, Schindler, Pietro Perona

TL;DR
This paper presents an automated pipeline using publicly available Google Maps imagery and CNNs to detect, identify species, and track changes in thousands of urban street trees efficiently at a city scale.
Contribution
It introduces a novel, fully automated method for large-scale urban tree mapping and change detection using publicly accessible imagery and machine learning techniques.
Findings
Detected over 70% of street trees in Pasadena.
Achieved >80% accuracy in species recognition for 40 species.
Correctly detected and classified changes in over 90% of cases.
Abstract
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…
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