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

Matthias Häberle, Martin Werner, Xiao Xiang Zhu
2019 European Journal of Remote Sensing  
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
more » ... ec, 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.
doi:10.1080/22797254.2019.1586451 fatcat:akjw4aiazzezhdhez7unle627i