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    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 >50000 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.

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    Laumer, Daniel; Lang, Nico; van Doorn, Natalie; Mac Aodha, Oisin; Perona, Pietro; Wegner, Jan Dirk. 2020. Geocoding of trees from street addresses and street-level images. ISPRS Journal of Photogrammetry and Remote Sensing. 162: 125-136.


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    Geocoding, Global optimization, Deep learning, Image interpretation, Object detection, Faster R-CNN, Urban areas, Street trees, Tree inventories, City scale, Google Street View

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