The Auto Arborist Dataset, published at CVPR 2022.
Usage of this dataset is subject to terms. See the dataset webpage for more info.
== Contents ==
The dataset consists of CSVs containing ~4.5M basic processed tree records from 23 cities and tfrecords that contain imagery for a subset of the trees.
README - Info about the dataset.
tree_locations/ - Contains the parsed and cleaned up tree inventories of each
city in the dataset, along with train/test splits per city. The CSVs
have the following columns:
- IDX: An identifier for the row which is unique to the city.
- SHAPE_LNG: The longitude of the tree.
- SHAPE_LAT: The latitude of the tree.
- GENUS: The lowercase genus of the tree.
- TAXONOMY_ID: A unique integer ID corresponding to the genus. Note that this
is indexed from 0.
tfrecords/ - Contains train/test TFRecord files with one aerial and blurred
street level image per available tree for all cities available in this release
version. The trees are a subset of the trees in tree_locations/.
The tfrecords have the following layout:
tree/
id: bytes. An ID for the tree that is unique across the release dataset.
tree_locations_idx: bytes. An ID which links to the tree_locations/ CSV IDX
for the tree.
city: bytes. The city where the tree is located.
latitude: float. The ground truth latitude of the tree.
longitude: float. The ground truth longitude of the tree.
genus/
label: int64. Holds the ground truth label number representing the tree’s
genus.
genus: bytes. The ground truth genus of the tree.
image/
aerial/
encoded: bytes. An encoded aerial JPEG image of the tree approximately
centered on its trunk.
streetlevel/
encoded: bytes. An encoded street level JPEG image of the tree.
Non-vegetation pixels are blurred.
capturetime: bytes. The month and year that the street level image was captured.
bbox/: float_lists. Represent tree detection bounding boxes (based on Open
Images) as regions scaled from [0, 1], with (0,0) representing
the top-left corner of the image.
xmin
xmax
ymin
ymax
center/:
x: int64. Represents an approximate (but noisy) location for the horizontal
center pixel of the tree in the image.
y: int64. This is always set to half of the image height. It is provided
for convenience.
== Citations ==
If you use the Auto Arborist dataset for a research publication, please consider citing:
"The Auto Arborist Dataset: A Large-Scale Benchmark for Multiview Urban Forest Monitoring Under Domain Shift." Sara Beery, Guanhang Wu, Trevor Edwards, Filip Pavetic, Bo Majewski, Shreyasee Mukherjee, Stanley Chan, John Morgan, Vivek Rathod, Jonathan Huang, CVPR 2022
Alternatively, you may provide this BibTeX citation:
@inproceedings{beery2022autoarborist,
title={The Auto Arborist Dataset: A Large-Scale Benchmark for Multiview Urban Forest Monitoring Under Domain Shift.},
author={Sara Beery and Guanhang Wu and Trevor Edwards and Filip Pavetic and Bo Majewski and Shreyasee Mukherjee and Stanley Chan and John Morgan and Vivek Rathod and Jonathan Huang},
booktitle={CVPR},
year={2022}
}
== Updates and Feedback ==
We may post updates about the project and dataset on our Google Group: https://groups.google.com/g/auto-arborist
We would love to hear back from you if you have questions or suggestions or success stories relating to this dataset. You can reach out to us at: auto-arborist+managers@googlegroups.com.
We have used a combination of automatic and manual methods to blur out non-vegetation pixels in our dataset in order to protect privacy. However, if you find any private or otherwise objectionable content, please contact us at the email above so that we may remove it.
== Attributions ==
We are thankful to the following cities for making their data available:
Bloomington, Indiana, USA https://data.bloomington.in.gov/dataset/public-tree-inventory
Boulder, Colorado, USA https://open-data.bouldercolorado.gov/datasets/dbbae8bdb0a44d17934243b88e85ef2b
Buffalo, New York, USA https://data.buffalony.gov/Quality-of-Life/Tree-Inventory/n4ni-uuec/data
Calgary, Alberta, Canada https://maps.calgary.ca/TreeSchedule/ Contains information licensed under the Open Government Licence – City of Calgary. See license: https://data.calgary.ca/stories/s/Open-Calgary-Terms-of-Use/u45n-7awa
Cambridge, Ontario, Canada https://data.waterloo.ca/datasets/cityofcambridge::street-trees Contains information licensed under the Open Government Licence – City of Cambridge. See license: https://maps.cambridge.ca/images/opendata/Open%20data%20licence.pdf
Charlottesville, Virginia, USA https://hub.arcgis.com/datasets/charlottesville::tree-inventory-point Credit to the City of Charlottesville. Derived data in this release is modified from the original version. See license: https://creativecommons.org/licenses/by/4.0/legalcode
Columbus, Ohio, USA https://opendata.columbus.gov/datasets/public-owned-trees
Cupertino, California, USA https://gis-cupertino.opendata.arcgis.com/datasets/Cupertino::trees
Denver, Colorado, USA https://www.denvergov.org/opendata/dataset/city-and-county-of-denver-tree-inventory Credit to City of Denver Open Data Catalog. Under the license terms CC BY 3.0. See data.denvergov.org and www.creativecommons.org for more info.
Edmonton, Alberta, Canada https://data.edmonton.ca/Environmental-Services/Trees-Map/udbt-eiax See license: https://data.edmonton.ca/stories/s/City-of-Edmonton-Open-Data-Terms-of-Use/msh8-if28/
Kitchener, Ontario, Canada https://data.waterloo.ca/datasets/KitchenerGIS::tree-inventory/about Contains information licensed under the Open Government Licence - The Corporation of the City of Kitchener.
Los Angeles, California, USA https://geohub.lacity.org/datasets/266c6255b1fc4ae8b8f100d8696e1fa4_0
Montreal, Quebec, Canada https://donnees.montreal.ca/ville-de-montreal/arbres Derived data in this release is modified from the original version. See license: https://donnees.montreal.ca/licence-d-utilisation
New York, New York, USA https://data.cityofnewyork.us/Environment/2015-Street-Tree-Census-Tree-Data/pi5s-9p35
Pittsburgh, Pennsylvania, USA https://data.wprdc.org/dataset/city-trees Credit to the City of Pittsburgh. Derived data in this release is modified from the original version. See license: https://creativecommons.org/licenses/by/4.0/legalcode
San Francisco, California, USA https://data.sfgov.org/City-Infrastructure/Street-Tree-List/tkzw-k3nq
San Jose, California, USA https://gisdata-csj.opendata.arcgis.com/datasets/7db16e012fe8402db45074cd260c8f4e_510
Santa Monica, California, USA https://data.smgov.net/Public-Assets/Trees-Inventory/w8ue-6cnd Contains information from Santa Monica Open Data which is made available under the ODC Attribution License - https://opendatacommons.org/licenses/by/1-0/.
Seattle, Washington, USA https://data-seattlecitygis.opendata.arcgis.com/datasets/0b8c124ace214943ab0379623937eccb_6
Sioux Falls, South Dakota, USA https://hub.arcgis.com/datasets/cityofsfgis::trees Credit to the City of Sioux Falls. Derived data in this release is modified from the original version. See license: https://creativecommons.org/licenses/by/4.0/legalcode
Surrey, British Columbia, Canada https://data.surrey.ca/dataset/park-specimen-trees Contains information licensed under the Open Government License – City of Surrey. See license: https://data.surrey.ca/pages/open-government-licence-surrey
Vancouver, British Columbia, Canada https://opendata.vancouver.ca/explore/dataset/street-trees/information Contains information licensed under the Open Government Licence – Vancouver. See license: https://opendata.vancouver.ca/pages/licence/
Washington DC, USA https://opendata.dc.gov/datasets/urban-forestry-street-trees/explore Credit to the City of Washington, DC. Derived data in this release is modified from the original version. See license: https://creativecommons.org/licenses/by/4.0/legalcode
Additionally, we are thankful for the Open Images Dataset which was used to help produce tree detection bounding boxes. https://storage.googleapis.com/openimages/web/index.html