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A multi-scale mapping approach based on a deep learning CNN model for reconstructing high-resolution urban DEMs

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journal contribution
posted on 13.05.2020, 10:07 by Ling Jiang, Yang Hu, Xilin Xia, Qiuhua Liang, Andrea Soltoggio, Syed Kabir
The scarcity of high-resolution urban digital elevation model (DEM) datasets, particularly in certain developing countries, has posed a challenge for many water-related applications such as flood risk management. A solution to address this is to develop effective approaches to reconstruct high-resolution DEMs from their low-resolution equivalents that are more widely available. However, the current high-resolution DEM reconstruction approaches mainly focus on natural topography. Few attempts have been made for urban topography, which is typically an integration of complex artificial and natural features. This study proposed a novel multi-scale mapping approach based on convolutional neural network (CNN) to deal with the complex features of urban topography and to reconstruct high-resolution urban DEMs. The proposed multi-scale CNN model was firstly trained using urban DEMs that contained topographic features at different resolutions, and then used to reconstruct the urban DEM at a specified (high) resolution from a low-resolution equivalent. A two-level accuracy assessment approach was also designed to evaluate the performance of the proposed urban DEM reconstruction method, in terms of numerical accuracy and morphological accuracy. The proposed DEM reconstruction approach was applied to a 121 km2 urbanized area in London, United Kingdom. Compared with other commonly used methods, the current CNN-based approach produced superior results, providing a cost-effective innovative method to acquire high-resolution DEMs in other data-scarce regions.

Funding

This work was supported by the U.K. Natural Environment Research Council (NERC) through the WeACT project (grant number NE/S005919/1), ValBGI project (grant number NE/S00288X/1) and Luanhe Living Lab project (grant number NE/S012427/1), the National Natural Science Foundation of China (grant numbers 41501445, 41701450, 41571398), State Major Project of Water Pollution Control and Management (grant number 2017ZX07603-001), China Postdoctoral Science Foundation (grant number 2018M642146), Jiangsu Planned Projects for Postdoctoral Research Funds (grant number 2018K144C), Anhui overseas visiting projects for outstanding young talents in Colleges and universities (grant number gxgwfx2018078), and Key Project of Natural Science Research of Anhui Provincial Department of Education (grant number KJ2017A416).

History

School

  • Architecture, Building and Civil Engineering
  • Science

Department

  • Computer Science

Published in

Water

Volume

12

Issue

5

Publisher

MDPI AG

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Acceptance date

09/05/2020

Publication date

2020-05-12

Copyright date

2020

eISSN

2073-4441

Language

en

Depositor

Dr Xilin Xia. Deposit date: 13 May 2020

Article number

1369

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