# From Google Maps to a Fine-Grained Catalog of Street trees

arXiv: https://arxiv.org/abs/1910.02675 · HTML: https://mlsanity.com/paper/1910.02675/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2019.

## Built on by (most cited first, 15 of 15)

- [Tree-D Fusion: Simulation-Ready Tree Dataset from Single Images with Diffusion Priors](https://mlsanity.com/paper/2407.10330/) (2024)
- [Individual Tree Detection in Large-Scale Urban Environments using High-Resolution Multispectral Imagery](https://mlsanity.com/paper/2208.10607/) (2022)
- [Weakly Supervised Instance Attention for Multisource Fine-Grained Object Recognition with an Application to Tree Species Classification](https://mlsanity.com/paper/2105.10983/) (2021)
- [Identifying Wetland Areas in Historical Maps using Deep Convolutional Neural Networks](https://mlsanity.com/paper/2108.04107/) (2021)
- [Bayesian Modeling of Zero-Shot Classifications for Urban Flood Detection](https://mlsanity.com/paper/2503.14754/) (2025)
- [Automatic Large Scale Detection of Red Palm Weevil Infestation using Aerial and Street View Images](https://mlsanity.com/paper/2104.02598/) (2021)
- [Urban Tree Species Classification Using Aerial Imagery](https://mlsanity.com/paper/2107.03182/) (2021)
- [3D point cloud segmentation using GIS](https://mlsanity.com/paper/2108.06306/) (2021)
- [Automatic Quantification and Visualization of Street Trees](https://mlsanity.com/paper/2201.06569/) (2022)
- [Mining Field Data for Tree Species Recognition at Scale](https://mlsanity.com/paper/2408.15816/) (2024)
- [DashCam Video: A complementary low-cost data stream for on-demand forest-infrastructure system monitoring](https://mlsanity.com/paper/2508.11591/) (2025)
- [MapAnything: Evaluating Monocular Metric Depth Models for 3D Urban Asset Localization](https://mlsanity.com/paper/2509.14839/) (2025)
- [Expert Knowledge-Guided Decision Calibration for Accurate Fine-Grained Tree Species Classification](https://mlsanity.com/paper/2601.16498/) (2026)
- [StreetTree: A Large-Scale Global Benchmark for Fine-Grained Tree Species Classification](https://mlsanity.com/paper/2602.19123/) (2026)
- [The Spectrascapes Dataset: Street-view imagery beyond the visible captured using a mobile platform](https://mlsanity.com/paper/2604.13315/) (2026)

## Abstract (arXiv)

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.