Identifying Tree Preservation Order (TPO) by Deep Learning in Greater London Area
Topics: Geographic Information Science and Systems
, Spatial Analysis & Modeling
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Keywords: Tree Preservation Order, deep learning, ResNet, MLP, LSTM
Session Type: Virtual Paper Abstract
Day: Monday
Session Start / End Time: 2/28/2022 08:00 AM (Eastern Time (US & Canada)) - 2/28/2022 09:20 AM (Eastern Time (US & Canada))
Room: Virtual 2
Authors:
Mingkang Wang, Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow
Yang Cai, Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow
Qianyao Luo, Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow
Zian Wang, Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow
Qunshan Zhao, Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow
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Abstract
Tree Preservation Order (TPO) is used to protect specific trees from damage and destruction. However, the current criteria and process of determining a TPO are vague and subjective in the UK. TPO data in many London boroughs are stored in paper form, with a cumbersome and lengthy application process. It is a barrier for both urban development and environmental protection. This research collected and analysed TPO data in Greater London and developed a multi-input deep learning model to detect potential TPOs in areas without public TPO data. The deep learning method is more efficient and accurate than traditional GIS or surveying methods. The research outcomes are useful for governments agencies, real estate developers and environmental organisations and can be extended to other areas.
Identifying Tree Preservation Order (TPO) by Deep Learning in Greater London Area
Category
Virtual Paper Abstract
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