# Individual Tree Detection in Large-Scale Urban Environments using High-Resolution Multispectral Imagery

arXiv: https://arxiv.org/abs/2208.10607 · HTML: https://mlsanity.com/paper/2208.10607/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 77 references that are in the map.
- First posted on arXiv 22 Aug 2022.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [From Google Maps to a Fine-Grained Catalog of Street trees](https://mlsanity.com/paper/1910.02675/) (2019)
- [Dual Path Multi-Scale Fusion Networks with Attention for Crowd Counting](https://mlsanity.com/paper/1902.01115/) (2019)
- [Geocoding of trees from street addresses and street-level images](https://mlsanity.com/paper/2002.01708/) (2020)

## Built on by (most cited first, 11 of 11)

- [STEERER: Resolving Scale Variations for Counting and Localization via Selective Inheritance Learning](https://mlsanity.com/paper/2308.10468/) (2023)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [Tree-D Fusion: Simulation-Ready Tree Dataset from Single Images with Diffusion Priors](https://mlsanity.com/paper/2407.10330/) (2024)
- [AI applications in forest monitoring need remote sensing benchmark datasets](https://mlsanity.com/paper/2212.09937/) (2022)
- [Benchmarking Individual Tree Mapping with Sub-meter Imagery](https://mlsanity.com/paper/2311.07981/) (2023)
- [A Comprehensive Review on Tree Detection Methods Using Point Cloud and Aerial Imagery from Unmanned Aerial Vehicles](https://mlsanity.com/paper/2309.16375/) (2023)
- [Trees as Gaussians: Large-Scale Individual Tree Mapping](https://mlsanity.com/paper/2508.21437/) (2025)
- [Predicting urban tree cover from incomplete point labels and limited background information](https://mlsanity.com/paper/2311.11592/) (2023)
- [Mining Field Data for Tree Species Recognition at Scale](https://mlsanity.com/paper/2408.15816/) (2024)
- [Data Augmentation and Resolution Enhancement using GANs and Diffusion Models for Tree Segmentation](https://mlsanity.com/paper/2505.15077/) (2025)
- [Counting Trees from Satellite Imagery with Noisy Supervision](https://mlsanity.com/paper/2606.24786/) (2026)

## Abstract (arXiv)

We introduce a novel deep learning method for detection of individual trees in urban environments using high-resolution multispectral aerial imagery. We use a convolutional neural network to regress a confidence map indicating the locations of individual trees, which are localized using a peak finding algorithm. Our method provides complete spatial coverage by detecting trees in both public and private spaces, and can scale to very large areas. We performed a thorough evaluation of our method, supported by a new dataset of over 1,500 images and almost 100,000 tree annotations, covering eight cities, six climate zones, and three image capture years. We trained our model on data from Southern California, and achieved a precision of 73.6% and recall of 73.3% using test data from this region. We generally observed similar precision and slightly lower recall when extrapolating to other California climate zones and image capture dates. We used our method to produce a map of trees in the entire urban forest of California, and estimated the total number of urban trees in California to be about 43.5 million. Our study indicates the potential for deep learning methods to support future urban forestry studies at unprecedented scales.