Semantics-aided 3D change detection on construction sites using UAV-based photogrammetric point clouds

Abstract

As the key to the construction progress monitoring, methods and strategies for change detection using 3D point clouds from various sources have been investigated for years. However, how to achieve object-level change detection with uncertainty evaluation is still an unsolved topic. Occlusions and noise in 3D points and other attribute information, such as colors, lead to problems in the task of change detection. In this paper, we present a semantic-aided change detection method aimed at monitoring construction progress using UAV-based photogrammetric point clouds. Our framework consists of two key parts, which identify changes in a progressive manner: The first part consists of the detection of geometric changes using occupancy-based spatial difference identification, which indicates the changes of occupancy in 3D space, comprising changes in appearance or shape of building objects. In occupancy-based change detection, occupancy conflicts of occupied space and empty space along the viewing rays of cameras can be detected by considering the sensor positions. At the same time, occlusions can be handled implicitly. The second part involves changes in semantics, which are used to detect changes where the occupancy-based change detection is not sufficient for presenting changes, due to limitations of parameter settings or lack of attribute information. By utilizing semantic segmentation results presented by class probabilities, the uncertainty of the semantic changes can be estimated. For the detection of geometric and semantic changes, Dempster–Shafer theory is applied to fuse information from data acquired in different time epochs to detect changes. Using the two different types of changes, we can fully consider the changes that may happen at the construction sites and define the differences between the changes. By utilizing the proposed change detection methods, changes with different characteristics, including geometric changes and semantic changes, can be correctly identified. In a specific example, for a construction period from Dec 12, 2014 to Jan 16, 2015, 97.8% of the changed areas could be successfully detected. For the other construction period, from Jan 16, 2015 to Feb 26, 2015, 93.6% of changes were correctly detected.