Integrating vision and lidar based hyperlocal metadata for optimal capacity expansion planning in hillside road networks

Abstract

The rapidly expanding population and scarcity of affordable housing in many urban areas, such as Los Angeles, have resulted in large inventories of structures in hilly landscapes. Such urbanization has also rendered road capacities inadequate in many instances, with hazards such as floods, fires, and earthquakes posing additional risks to such inventory. Planning construction activities such as widening, structural, and geotechnical retrofits to increase capacity and resiliency by cutting through slopes and incorporating landslide protection measures requires hyperlocal information for road segments in hillside areas. Performing traditional surveys for large areas of such road networks can be both time-consuming and cost prohibitive. At the same time, data acquired from satellites may not provide the required spatial resolution for planning, especially regarding road-adjacent features such as slopes. This paper proposes a novel framework to fuse hyperlocal data regarding road and adjacent slopes gathered from the street-level mobile mapper equipped with cameras and lidar into the network design problem (NDP) to develop an optimal road width expansion plan. The hyperlocal features are derived from a colorized point cloud map and deep learning techniques to estimate the relevant features in hillside landscapes. Specifically, a deep learning-based segmentation and detection algorithms combined with novel point cloud processing techniques are used to obtain a spatially dense profile of the road and the adjacent slope. By leveraging such hyperlocal information, we explore all the potential road capacity and improvement options for road capacity expansion. Furthermore, we demonstrate the advantages of our proposed method over conventional techniques, particularly working with budget constraints. In terms of novel contributions, incorporating hyperlocal information into NDP for hillside road capacity expansion is novel, as well as algorithms to automatically detect and characterize relevant roadside features from remotely sensed lidar point clouds.