Identifying Tree Preservation Order Protected Trees by Deep Learning in Greater London Area

Li, Q. , Wang, M., Cai, Y., Luo, Q., Wang, Z. and Zhao, Q. (2023) Identifying Tree Preservation Order Protected Trees by Deep Learning in Greater London Area. In: 31st Annual Geographical Information Science Research UK Conference (GISRUK2023), Glasgow, Scotland, 19-21 Apr 2023, (doi: 10.5281/zenodo.7839695)

Abstract

Tree Preservation Order (TPO) is used to protect specific trees from damage and destruction, which is determined in high subjectivity. This research collected and analyzed TPO data, aerial images, geographic data, and socio-economic data in the Greater London area and developed a multi-input deep learning (DL) framework to classify TPO-protected and non-TPO-protected trees. The synergy use of aerial images and GIS data with the fusion model of ResNet50 and multilayer perceptron network produced the best classification accuracy of 87.32%. The result indicated the robustness of the multi-input DL model to identify the social attributes of trees compared with the single-input DL model.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Wang, Mr Mingkang and Zhao, Dr Qunshan and Li, Dr Qiaosi
• Authors: Li, Q., Wang, M., Cai, Y., Luo, Q., Wang, Z., and Zhao, Q.
• College/School: College of Social Sciences > School of Social and Political Sciences > Urban Studies
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in 31st Annual Geographical Information Science Research UK Conference (GISRUK)
• Publisher Policy: Reproduced under a Creative Commons license

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
190698		Urban Big Data Research Centre	Nick Bailey	Economic and Social Research Council (ESRC)	ES/L011921/1	S&PS - Urban Big Data
304042		UBDC Centre Transition	Nick Bailey	Economic and Social Research Council (ESRC)	ES/S007105/1	S&PS - Administration