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https://google.github.io/auto-arborist/


https://google.github.io/auto-arborist/

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README 

        
The Auto Arborist Dataset, [published at CVPR 2022](https://openaccess.thecvf.com/content/CVPR2022/html/Beery_The_Auto_Arborist_Dataset_A_Large-Scale_Benchmark_for_Multiview_Urban_CVPR_2022_paper.html).



Usage of this dataset is subject to terms. See the [dataset webpage](https://google.github.io/auto-arborist/) 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:

[email protected].



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