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.

我们介绍了一种使用街道级全景图像和用于树实例匹配的全局优化框架通过地理坐标更新较旧树库存的方法。直到2000年代初，清单中树木的地理位置都是使用街道地址记录的，而较新的清单则使用GPS。我们的方法使用地理坐标对较旧的清单进行了翻新，以便将它们与较新的清单联系起来，以促进对树木死亡率等的长期研究。使该问题具有挑战性的是每个街道地址的树木数量不同，图像，如果从多个图像和遮挡物来看，则树的位置不明确。为了解决此分配问题，我们（i）使用深度学习在Google街景全景图中检测树木，（ii）将每棵树的多视图检测合并为单个表示，（iii）并使用全局优化方法将检测到的树与每个街道地址的给定树进行匹配。实验$>50000$ 美国加利福尼亚州5个城市的树木表明，我们能够为38％的街道树分配地理坐标，这是对大规模研究街道树的生态系统服务价值的良好起点。