Lidar- Data: Automatic Object Detection to Support Urban Flooding Simulation

Urban flooding is an increasingly alarming issue in terms of public safety and property damage. Climate change and its possible effects on the occurrence of more frequent extreme weather events have become an important topic in the area of global politics, science and engineering of today’s world. The proper design of urban drainage systems and analysis of their hydraulic performance to secure flood protection in urban areas is a challenging task of urban water management. High resolution surface data describing hydrologic and hydraulic properties of complex urban areas is the prerequisite to more accurately describe and simulate the flood water movement and thereby to take adequate measures against urban flooding. LiDAR (Light detection and ranging) point cloud is an efficient way of generating high resolution digital surface model (DSM) of any study area. This paper presents an approach to segment LiDAR point cloud into ground and non-ground points based on slope change, height variation and standard deviation of neighbouring points. The non-ground points are later classified into buildings and trees by using an approach based on surface roughness and planar component calculation. Streets play an important role in terms of surface runoff generation. A semiautomatic approach has been developed to extract street point candidates from LiDAR data. A knowledge based expert system has been implemented to identify impervious surfaces (other than street) and grassland. Finally future ideas will be described to detect other surface drainage elements such as property boundary walls and slope information in the neighbourhood of the street as they are believed to guide the flood water flow and influence their possible intrusion into the private property (garage, basement).