Change detection in urban landscapes: a tensor factorization approach

Abstract

Analysis of urban landscape has been an interesting research challenge for decades. The advent of machine learning and data mining techniques have geared the problem from simple analysis of data to knowledge discovery from data. This work attempts to mine urban landscapes to find the change pattern which has happened over the region for a period of interest. The work proposes a spatiotemporal-metric miner, which uses the spatial, temporal and landscape metric data to discover the change that has occurred in a region. The model works on a hierarchical basis, wherein, the regions of interest are chosen in a landscape and are aggregated to find the change that has happened over the entire region. The entire model is built by taking advantage of the tensorized representation of data, and thus resulting in the effective mining of tensors. The growth of a landscape is evaluated regarding two parameters, namely, Inter-class Growth Index and Intra-class Growth Index. Experiments are performed on the landscape regions of Indian cities, and a ranking of cities is presented based on the growth indices, which are validated against standards. In the experiments, Jaipur city showed the highest Inter-class Growth Index value of 2.68 and Surat city had an Intra-class Growth Index of 0.78.