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Geo-spatial text-mining from Twitter - a feature space analysis with a view toward building classification in urban regions

Häberle, Matthias and Werner, Martin and Zhu, Xiao Xiang (2019) Geo-spatial text-mining from Twitter - a feature space analysis with a view toward building classification in urban regions. European Journal of Remote Sensing, 52 (S2), pp. 2-11. Taylor & Francis. doi: 10.1080/22797254.2019.1586451. ISSN 2279-7254.

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Official URL: https://www.tandfonline.com/doi/full/10.1080/22797254.2019.1586451

Abstract

By the year 2050, it is expected that about 68% of global population will live in cities. To understand the emerging changes in urban structures, new data sources like social media must be taken into account. In this work, we conduct a feature space analysis of geo-tagged Twitter text messages from the Los Angeles area and a geo-spatial text mining approach to classify buildings types into commercial and residential. To create the feature space, broadly accepted word embedding models like word2vec, fastText and GloVe as well as more traditional models based on TF-IDF have been considered. A visual analysis of the word embeddings shows that the two examined classes yield several word clusters. However, the classification results produced by Naïve Bayes support vector machines, and a convolutional neural network indicates that building classification from pure social media text is quite challenging. Furthermore, this work illustrates a base toward fusing text features and remote sensing images to classify urban building types.

Item URL in elib:https://elib.dlr.de/120452/
Document Type:Article
Title:Geo-spatial text-mining from Twitter - a feature space analysis with a view toward building classification in urban regions
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Häberle, MatthiasUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Werner, MartinUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Zhu, Xiao XiangDLR-IMF/TUM-LMFUNSPECIFIEDUNSPECIFIED
Date:2019
Journal or Publication Title:European Journal of Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:52
DOI:10.1080/22797254.2019.1586451
Page Range:pp. 2-11
Publisher:Taylor & Francis
ISSN:2279-7254
Status:Published
Keywords:deep learning, remote sensing, building type classification, natural language processing, urban remote sensing, land use
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Häberle, Matthias
Deposited On:22 Feb 2019 12:00
Last Modified:21 Nov 2023 13:46

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