High-Resolution Flood Simulation in Urban Areas Through the Application of Remote Sensing and Crowdsourcing Technologies

Abstract

High-resolution flood simulation considering the influence of high buildings and fundamental facilities is important for flood risk assessment in urban areas. However, it is also a challenging task due to the difficulties in acquiring detailed topography and monitoring data for model construction and validation. In this study, a high-resolution flood simulation with a grid size of 0.5 m is realized through the use of detailed topography obtained by an unmanned aerial vehicle and real-time flood information acquired from social media. To discover the influence of terrain resolution on flood simulations, the high-resolution simulation results are compared with those with coarser grid resolutions (5, 10, and 20 m) for a flash flood event in Taiwan. In the case with higher grid resolution, the simulation results are in better agreement with the photos from social media in terms of flood extent, depth, and occurrence time. The flood simulation with coarse resolution (>5 m) tends to overestimate the flood duration on roads and provide bias information to decision-makers in the assessment of traffic impact and economic loss.

Accuracy assessment of land-use-land-cover maps: the semantic gap between in situ and satellite data

“…Due to the rise in crowdsourced data availability, there has been a growing interest in using street-level images to create reference data across various fields. [13][14][15] D'Andrimont et al 15 propose a method to support the European Union's Common Agricultural Policy (CAP) that aims to simplify the control of farmers' parcel declarations. Sentinel-1 and Sentinel-2 data are used to automatically detect parcels that are potentially not grasslands but are declared by farmers as such.…”