Multimodal Integrated Remote Sensing for Urban Environments

This research will develop novel computational methods to process remotely sensed data to monitor urban environments. Specifically, the developed methods will look to tackle challenges that arise when processing multimodal data such that the complementary strengths of each observation mode can be harnessed. The analysis of such data presents many different problems to classic problems in the field of computer vision, where input data is most commonly acquired using sensors which capture visible wavelengths and is generally Euclidean in nature. While working with data which is inherently non-euclidean presents many challenges, this research aims to consider, among other techniques, the merits of exciting developments in geometric deep learning for the analysis of multimodal, remotely-sensed data.
The following EPSRC Research Areas have been identified as topics which this research will engage with: Artificial intelligence technologies; Built environment; Data signal processing; Image and vision computing; Infrastructure and urban systems; Statistics and applied probability; Structural engineering.