Remote Sensing Imagery: A new frontier for Urban Analytics?

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In a tight economic climate, it is more important than ever to have the best possible understanding of how the built environment changes over time, so policy makers can prioritise and adapt effectively. Coupled with climate change, the global population living in the built environment is projected to increase to 68% by 2050 (United Nations 2018) and global patterns of urbanization will be heterogeneous (McGee 2010; Kourtit et al. 2014), so accurate measurements of changes in and between urban agglomerations (both physical and virtual) is critical for the planning of Smart Cities. Measurements of urban change is critical to meet the future challenges of housing, transportation, energy systems, education and healthcare.There are three sources of data that have emerged in the field of urban analytics, which are data collected by mobile sensors carried by individuals, data extracted from internet sites and government data released in an open format (Arribas-Bel 2014). We propose that Remote Sensing Information (RSI) will emerge as a large and thoroughly-used new source of information for urban studies. RSI contains an abundance of information, which has driven insights into socio-economic relationships (Jean et al. 2016). The potential for RSI to help automate change detection in the built environment is massive; it would be both time and cost efficient compared to the main alternative, field surveying. Whilst several studies have applied LiDAR to monitor changes in the built environment, historic applications of coarse-medium spatial resolution (15-60m) Multispectral Satellite Imagery (MSI) have been limited due to the inability to classify complex features such as buildings (Hegazy and Kaloop 2015). To overcome this limitation, some studies have fused high temporal resolution MSI with high spatial resolution LiDAR data to build better overall insights (Yan et al. 2014; Wu et al. 2015). Furthermore, fusion methods have the merits of high horizontal accuracy from aerial images and high vertical accuracy from LIDAR data (Alonso-Benito et al. 2016). For example, Dong et al (2010) combined LiDAR with Landsat Thematic Mapper to calculate small-area population estimates. Further, the space industrial sector is rapidly growing and there is a global movement towards the commercialisation of RSI: recent technological advances achieve spatial resolutions similar to LiDAR, less than 1m.The aim of this project is to evaluate the potential of applying RSI for the analysis of changes to cities over time. Specifically, we will compare whether the current technologies of LiDAR, MSI or a fusion of these technologies is best practice for classifying complex features such as buildings and roads in the built environment and how they change over time. Building up a detailed geo- referenced space-time database on urban evolution is important for forecasting and policy. For example, identifying socio-economic and demographic diversity within and between urban areas. Such a study is forward looking, given the increasing spatial and temporal resolution of RSI.