Predicting urban tree cover from incomplete point labels and limited background information

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Trees inside cities are important for the urban microclimate, contributing positively to the physical and mental health of the urban dwellers. Despite their importance, often only limited information about city trees is available. Therefore in this paper, we propose a method for mapping urban trees in high-resolution aerial imagery using limited datasets and deep learning. Deep learning has become best-practice for this task, however, existing approaches rely on large and accurately labelled training datasets, which can be difficult and expensive to obtain. However, often noisy and incomplete data may be available that can be combined and utilized to solve more difficult tasks than those datasets were intended for.This paper studies how to combine accurate point labels of urban trees along streets with crowd-sourced annotations from an open geographic database to delineate city trees in remote sensing images, a task which is challenging even for humans. To that end, we perform semantic segmentation of very high resolution aerial imagery using a fully convolutional neural network.The main challenge is that our segmentation maps are sparsely annotated and incomplete. Small areas around the point labels of the street trees coming from official and crowd-sourced data are marked as foreground class. Crowd-sourced annotations of streets, buildings, etc. define the background class. Since the tree data is incomplete, we introduce a masking to avoid class confusion.Our experiments in Hamburg, Germany, showed that the system is able to produce tree cover maps, not limited to trees along streets, without providing tree delineations. We evaluated the method on manually labelled trees and show that performance drastically deteriorates if the open geographic database is not used.

TitelUrban-AI 2023 - Proceedings of the 1st ACM SIGSPATIAL International Workshop on Advances in Urban-AI
RedaktørerOlufemi A. Omitaomu, Ali Mostafavi, Yan Liu
ForlagAssociation for Computing Machinery, Inc.
ISBN (Elektronisk)9798400703621
StatusUdgivet - 2023
Begivenhed1st ACM SIGSPATIAL International Workshop on Advances in Urban-AI, Urban-AI 2023 - Hamburg, Tyskland
Varighed: 13 nov. 2023 → …


Konference1st ACM SIGSPATIAL International Workshop on Advances in Urban-AI, Urban-AI 2023
Periode13/11/2023 → …

Bibliografisk note

Funding Information:
This work was supported by the research grant DeReEco from VILLUM FONDEN (grant number 34306), the PerformLCA project (UCPH Strategic plan 2023 Data+ Pool), and the grant “Risk-assessment of Vector-borne Diseases Based on Deep Learning and Remote Sensing” (grant number NNF21OC0069116) by the Novo Nordisk Foundation.

Publisher Copyright:
© 2023 ACM.

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