Visual Analysis and Semantic Exploration of Error Aware Urban Change Detection

Many previous approaches to detecting urban change from LIDAR point clouds do not consider error and uncertainty and simplify their calculations by interpolating into a regular grid structure, producing a 2D image as output. We present a method of LIDAR change detection the accounts for error and uncertainty, maintains accuracy, and extracts relevant changes as individual 3D models. We then utilize these models, alongside existing GIS data, within an interactive application that allows the chronological exploration of the changes to an urban environment. A three-tiered level-of-detail system maintains a scale-appropriate, legible visual representation across the entire range of view scales, from individual changes such as a buildings and trees, to groups of changes such as new residential developments, deforestation, and construction sites, and finally to larger regions such as neighborhoods and districts of a city that are emerging or undergoing revitalization. Tools are provided to assist the visual analysis by urban planners and historians through semantic categorization and filtering of the changes presented.