A Multi-Scale Mapping Approach Based on a Deep Learning CNN Model for Reconstructing High-Resolution Urban DEMs

Abstract: The shortage o f high - resolution urban digital elevation model ( DEM) datasets has been a challenge for modelling urban flood and managing its risk . A solution is to develop effective approaches to r econstruct high - resolution DEMs from their low - resolution equivalents that are more widely available . However, the current high - resolution DEM reconstruction approaches mainly focus on natural topography . F ew attempts have been made for urban topography which is typically a n integration of complex man - made and natural features . T his study proposes a novel multi - scale mapping approach based on convolutional neural network (CNN) to deal with the complex characteristics of urban topography and reconstruct high - resolution urban DEMs . T he proposed multi - scale CNN model is first ly trained using urban DEMs that contain topographic features at different resolutions, and then used to reconstruct the urban DEM at a specified (high) resolution from a low - resolution equivalent . A two - level accuracy assessment approach is also designed to evaluate the performance of the proposed urban DEM reconstruction method, in terms of numerical accuracy and morphological accuracy . Compared with other commonly used met hods, the current CNN based approach produces superior results, provid ing a cost - effective innovative method to acquire high - resolution DEMs in other data - scarce environment s . Introduction Digital elevation model s (DEM s) have been widely used in many fields such as l andform evolution, soil erosion modeling and other geo - simulation s ( Bishop et al., 2012; Liu et al., 2015; Mondal et al., 2017; Li and Wong, 2010) . In p articular, DEMs provide indispensable data to support water resources management and flood risk assessment (Moore et al., 1991; O'Loughlin et al., 2016) . I n urban flood risk assessment, the availability of high - resolution urban DEMs is crucial for the accurate representation of complex urban topographic features and required for a reliable prediction of flood inundation to inform risk calculation ( Ramirez et al., 2016; Leitão and de Sousa, 2018) .