Classification of settlement types from tweets using LDA and LSTM

Land use reflects the interrelation between the physically built environment and the activity patterns of people. It is indispensable information for decision-makes, but up-to-date and accurate land use information is often absent. Unlike approaches that make use of remote sensing data, in this work, we are interested in a novel data source, tweets, and explore its potential for land use classification in urban areas. Specifically, we propose a general framework for classifying settlement land-use types by extracting location, time, quantity and text features of twitter data. To do so, we apply latent Dirichlet allocation (LDA) and long short-term memory (LSTM) and then combines those features with spatial-temporal feature using Fused SVM and a two-stream convolutional neural network (CNN) for classification. For the case of classifying individual tweets by the land-use classes relevant in this study - residential, non-residential and mixed usage -, we reach overall accuracy (OA), average accuracy (AA), and Kappa coefficient with 72.35%, 73.76%, and 58.43%, respectively. As for the case of classifying block settlement types, we reach 61.90%, 63.33%, and 42.84%, respectively.