@inproceedings{7faec213423546b7a10fd1f4289463b6,
title = "Building Type Classification from Social Media Texts via Geo-Spatial Textmining",
abstract = "In this work, we present a model for building type classification from Twitter text messages (tweets) by employing geo-spatial textmining methods. First, we apply standard text pre-processing methods and convert the tweets into sentence vectors using fastText. For classification, we apply a feedforward network with two fully connected hidden layers and feed the generated sentence vectors as linguistic features. Classification results suggest that the classes are distinguishable to a certain extent with pure text even with unbalanced class distributions and a very small sample size. However, these findings also undermine, that building type classification with pure text data is a challenging task.",
keywords = "Building Settlement Type, Classification, Data Mining, Deep Learning, Language, Natural Language Processing, Social Media, Urban Remote Sensing, Word Embedding",
author = "Matthias Haberle and Martin Werner and Zhu, {Xiao Xiang}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",
year = "2019",
month = jul,
doi = "10.1109/IGARSS.2019.8898836",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "10047--10050",
booktitle = "2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings",
}