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Feature Importance Analysis for Local Climate Zone Classification Using a Residual Convolutional Neural Network with Multi-Source Datasets

Qiu, Chunping and Schmitt, Michael and Mou, Lichao and Ghamisi, Pedram and Zhu, Xiao Xiang (2018) Feature Importance Analysis for Local Climate Zone Classification Using a Residual Convolutional Neural Network with Multi-Source Datasets. Remote Sensing, 10 (10), pp. 1-14. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/rs10101572 ISSN 2072-4292

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Official URL: https://www.mdpi.com/2072-4292/10/10/1572/pdf-vor

Abstract

Global Local Climate Zone (LCZ) maps, indicating urban structures and land use, are crucial for Urban Heat Island (UHI) studies and also as starting points to better understand the spatio-temporal dynamics of cities worldwide. However, reliable LCZ maps are not available on a global scale, hindering scientific progress across a range of disciplines that study the functionality of sustainable cities. As a first step towards large-scale LCZ mapping, this paper tries to provide guidance about data/feature choice. To this end, we evaluate the spectral reflectance and spectral indices of the globally available Sentinel-2 and Landsat-8 imagery, as well as the Global Urban Footprint (GUF) dataset, the OpenStreetMap layers buildings and land use and the Visible Infrared Imager Radiometer Suite (VIIRS)-based Nighttime Light (NTL) data, regarding their relevance for discriminating different Local Climate Zones (LCZs). Using a Residual convolutional neural Network (ResNet), a systematic analysis of feature importance is performed with a manually-labeled dataset containing nine cities located in Europe. Based on the investigation of the data and feature choice, we propose a framework to fully exploit the available datasets. The results show that GUF, OSM and NTL can contribute to the classification accuracy of some LCZs with relatively few samples, and it is suggested that Landsat-8 and Sentinel-2 spectral reflectances should be jointly used, for example in a majority voting manner, as proven by the improvement from the proposed framework, for large-scale LCZ mapping.

Item URL in elib:https://elib.dlr.de/124766/
Document Type:Article
Title:Feature Importance Analysis for Local Climate Zone Classification Using a Residual Convolutional Neural Network with Multi-Source Datasets
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Qiu, Chunpingtu münchenUNSPECIFIED
Schmitt, Michaelm.schmitt (at) tum.deUNSPECIFIED
Mou, Lichaolichao.mou (at) dlr.deUNSPECIFIED
Ghamisi, PedramDLR-IMF/TUM-LMFUNSPECIFIED
Zhu, Xiao XiangDLR-IMF/TUM-LMFUNSPECIFIED
Date:October 2018
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:10
DOI :10.3390/rs10101572
Page Range:pp. 1-14
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:Local Climate Zones (LCZs); Sentinel-2; Landsat-8; spectral reflectance; classification; Residual convolutional neural Network (ResNet)
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Hoffmann, Eike Jens
Deposited On:11 Dec 2018 12:47
Last Modified:14 Dec 2019 04:26

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