KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#dsh #dsh-plugin #ai-agents #developer-tools #typescript #codex #claude-code #mcp #rust #deepseek-harness #ai-agent #cli

urban-tree-detection-data

★ 40 Open GitHub ↗

Dataset for training and evaluating tree detectors in urban environments with aerial imagery

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

## Urban Tree Detection Data ##

This repository provides a dataset for training and evaluating tree detectors in urban environments with aerial imagery.  The dataset includes:

* 256x256 crops of 60 cm aerial imagery from the 2016, 2018, and 2020 NAIP across eight cities in California
* A total of 1,651 images and 95,972 annotated trees 
* Point annotations for all trees visible in the imagery
* Train/val/test splits to replicate or compare against the results in our paper

### Data description ###

The dataset covers eight cities and six climate zones in California across three years.  The following table provides a summary.  The three right-most columns give the number of annotated trees in each year.

| City         | Climate Zone              | Number of Crops |  2016 |  2018 |  2020 |
|--------------|---------------------------|-----------------|-------|-------|-------|
| Bishop       | Interior West             |              10 |     - |     - |   682 |
| Chico        | Inland Valleys            |              99 |     - | 8,187 | 8,164 |
| Claremont    | Inland Empire             |              92 | 4,858 | 4,880 | 4,678 |
| Eureka       | Northern California Coast |              21 |     - |     - | 2,134 |
| Long Beach   | Southern California Coast |             100 | 6,470 | 6,403 | 5,845 |
| Palm Springs | Southwest Desert          |             100 | 4,433 | 4,707 | 4,109 |
| Riverside    | Inland Empire             |              90 | 5,015 | 4,400 | 4,087 |
| Santa Monica | Southern California Coast |              92 | 5,824 | 5,830 | 5,841 |

The bands in the imagery are as follows:

| Band | Description |
|------|-------------|
|    0 | Red         |
|    1 | Green       |
|    2 | Blue        |
|    3 | Near-IR     |

### Data organization ###

* Images are stored in the `images` directory as TIFF files.
* Each image has an associated CSV file in the `csv` directory containing tree locations in 2D pixel coordinates.
* Each image has an associated GeoJSON file in the `json` directory containing geo-referenced tree locations.  Coordinates are stored in the local UTM zone.
* A missing .csv or .json file means that there are no trees in the image.

The files `train.txt`, `val.txt`, and `test.txt` specify the splits using all of the data.  The files `train_socal.txt`, `val_socal.txt`, and `test_socal.txt` specify the splits using the Southern California 2020 subset of the data (only 2020 data from Claremont, Long Beach, Palm Springs, Riverside, and Santa Monica).

### Citation ###

NAIP on AWS was accessed on January 28, 2022 from https://registry.opendata.aws/naip.

If you use this data, please cite our paper:

J. Ventura, C. Pawlak, M. Honsberger, C. Gonsalves, J. Rice, N.L.R. Love, S. Han, V. Nguyen, K. Sugano, J. Doremus, G.A. Fricker, J. Yost, and M. Ritter (2024). [Individual Tree Detection in Large-Scale Urban Environments using High-Resolution Multispectral Imagery.](https://www.sciencedirect.com/science/article/pii/S1569843224002024)  International Journal of Applied Earth Observation and Geoinformation, 130, 103848.

### Acknowledgments ###

This project was funded by CAL FIRE (award number: 8GB18415) the US Forest Service (award number: 21-CS-11052021-201), and an incubation grant from the Data Science Strategic Research Initiative at California Polytechnic State University.

### License ###

This work is licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).

urban-tree-detection-data

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README