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Geocoding of trees from street addresses and street-level images

Laumer, Daniel and Lang, Nico and van Doorn, Natalie and Mac Aodha, Oisin and Perona, Pietro and Wegner, Jan Dirk (2020) Geocoding of trees from street addresses and street-level images. ISPRS Journal of Photogrammetry and Remote Sensing, 162 . pp. 125-136. ISSN 0924-2716. https://resolver.caltech.edu/CaltechAUTHORS:20200221-143924520

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Abstract

We introduce an approach for updating older tree inventories with geographic coordinates using street-level panorama images and a global optimization framework for tree instance matching. Geolocations of trees in inventories until the early 2000s where recorded using street addresses whereas newer inventories use GPS. Our method retrofits older inventories with geographic coordinates to allow connecting them with newer inventories to facilitate long-term studies on tree mortality etc. What makes this problem challenging is the different number of trees per street address, the heterogeneous appearance of different tree instances in the images, ambiguous tree positions if viewed from multiple images and occlusions. To solve this assignment problem, we (i) detect trees in Google street-view panoramas using deep learning, (ii) combine multi-view detections per tree into a single representation, (iii) and match detected trees with given trees per street address with a global optimization approach. Experiments for trees in 5 cities in California, USA, show that we are able to assign geographic coordinates to 38% of the street trees, which is a good starting point for long-term studies on the ecosystem services value of street trees at large scale.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.isprsjprs.2020.02.001DOIArticle
https://arxiv.org/abs/2002.01708arXivDiscussion Paper
ORCID:
AuthorORCID
Mac Aodha, Oisin0000-0002-5787-5073
Perona, Pietro0000-0002-7583-5809
Wegner, Jan Dirk0000-0002-0290-6901
Additional Information:© 2020 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. Received 8 November 2019, Revised 4 February 2020, Accepted 4 February 2020, Available online 21 February 2020. This project was supported by funding from the Hasler Foundation, the US Department of Agriculture-Forest Service, and the Swiss National Science Foundation scientific exchange grant IZSEZ0 185641.
Funders:
Funding AgencyGrant Number
Hasler FoundationUNSPECIFIED
Department of AgricultureUNSPECIFIED
Swiss National Science Foundation (SNSF)IZSEZ0 185641
Subject Keywords:Geocoding; Global optimization; Deep learning; Image interpretation; Object detection; Faster R-CNN; Urban areas; Street trees; Tree inventories; City scale; Google Street View
Record Number:CaltechAUTHORS:20200221-143924520
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200221-143924520
Official Citation:Daniel Laumer, Nico Lang, Natalie van Doorn, Oisin Mac Aodha, Pietro Perona, Jan Dirk Wegner, Geocoding of trees from street addresses and street-level images, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 162, 2020, Pages 125-136, ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2020.02.001. (http://www.sciencedirect.com/science/article/pii/S0924271620300356)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:101469
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:21 Feb 2020 22:47
Last Modified:26 May 2020 17:12

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