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
- A train/val/test split to replicate or compare against the results in our paper
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 |
- Images are stored in the
imagesdirectory as TIFF files. - Each image has an associated CSV file in the
csvdirectory containing tree locations in 2D pixel coordinates. - Each image has an associated GeoJSON file in the
jsondirectory 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 that were used in our paper.
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. "Individual Tree Detection in Large-Scale Urban Environments using High-Resolution Multispectral Imagery." arXiv:2208.10606 [cs], Oct. 2022.
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.
This work is licensed under a Creative Commons Attribution 4.0 International License.