Developing a context-based bounded centrality approach of street patterns in flooding: a case study of London

Floods affect an average of 21 million people worldwide each year, and their frequency is expected to increase due to climate warming, population growth, and rapid urbanisation. Previous research on the robustness of transport networks during floods has mainly used percolation theory. However, giant component size of disrupted networks cannot capture the entire network’s information and, more importantly, does not reflect the local reality. To address this issue, this study introduces a novel approach to bounded context-based centrality to extract the local impact of disruption. In particular, we propose embedding travel behaviour into the road network to calculate bounded centrality and develop new measures characterising the size of connected components during flooding. Our analysis can identify critical road segments during floods by comparing the decreasing trend and dispersibility of component sizes on road networks. To demonstrate the feasibility of these approaches, a case study of London's transport infrastructure that integrates road networks with relevant urban contexts was developed. This approach is beneficial for practical risk management, helping decision-makers allocate resources efficiently in space and time

Occupancy grid artefact removal and error correction using GANs

Occupancy Grid Mapping is a form of Simultaneous Localisation and Mapping (SLAM) in which the world around a robot is visually represented as a grid map. This form of map can be compared to a floor plan in which features within an environment such as walls are labelled in place. Certain issues such as noise, artefacts, linear error, angular error, and incomplete rooms make this representation difficult to appropriate. Generative Adversarial Networks (GAN) [1] in the past have proven successful in and reliable methods for noise reduction, artefact removal [2], and partial observation completion [3]. We demonstrate a novel data creation process to mass produce samples of erroneous and ideal occupancy grid maps. We use this data to build two GAN models based on well-known frameworks CycleGAN [4] and CUT [5] for the task of occupancy grid cleaning. We demonstrate the generalisability of our models through making predictions of ‘clean’ maps on samples of real data from the Radish Dataset [6].</p

Occupancy grid artefact removal and error correction using GANs

Occupancy Grid Mapping is a form of Simultaneous Localisation and Mapping (SLAM) in which the world around a robot is visually represented as a grid map. This form of map can be compared to a floor plan in which features within an environment such as walls are labelled in place. Certain issues such as noise, artefacts, linear error, angular error, and incomplete rooms make this representation difficult to appropriate. Generative Adversarial Networks (GAN) [1] in the past have proven successful in and reliable methods for noise reduction, artefact removal [2], and partial observation completion [3]. We demonstrate a novel data creation process to mass produce samples of erroneous and ideal occupancy grid maps. We use this data to build two GAN models based on well-known frameworks CycleGAN [4] and CUT [5] for the task of occupancy grid cleaning. We demonstrate the generalisability of our models through making predictions of ‘clean’ maps on samples of real data from the Radish Dataset [6].</p