Automatic Volume Calculation and Mapping of Construction and Demolition Debris Using Drones, Deep Learning, and GIS

Abstract

This paper presents a time- and cost-efficient method for the management of construction and demolition (C&D) debris at construction sites, demolition jobsites, and illegal C&D waste dumping sites. The developed method integrates various drone, deep learning, and geographic information system (GIS) technologies, including C&D debris drone scanning, 3D reconstruction with structure from motion (SfM), image segmentation with fully convolutional network (FCN), and C&D debris information management with georeferenced 2D and 3D as-built. Experiments and parameter analysis led us to conclude that (1) drone photogrammetry using top- and side-view images is effective in the 3D reconstruction of C&D debris (stockpiles); (2) FCNs are effective in C&D debris extraction with point cloud-generated RGB orthoimages with a high intersection over union (IoU) value of 0.9 for concrete debris; and (3) using FCN-generated pixelwise label images, point cloud-converted elevation data for projected area, and volume measurements of C&D debris is both robust and accurate. The developed automatic method provides quantitative and geographic information to support city governments in intelligent information management of C&D debris.

Framework to Identify Directions for Future Construction and Demolition Waste Management Technologies

“…Another is to compute the volumes of C&D waste in landfills by applying mobile mapping technology provided by GeoSLAM (Simultaneous localisation and mapping) [18]. A study demonstrates that an innovative approach effectively integrates various technologies, such as Drones, CV, GIS, and 3DR, which shows better results in the final quantification and mapping of C&D debris [19]. Big Data (BD) is adopted in C&D waste management to analyse massive amounts of information generated from C&D waste to settle landfill management issues [20].…”

Pavement Inspection in Transport Infrastructures Using Unmanned Aerial Vehicles (UAVs)

“…Considering the criteria that those with a minimum number of citations equal to 50 are relevant authors in the field under study, 12 authors, divided into two clusters, were identified (see Figure 6). Of these, seven authors, namely Zhang, H., Li, S., Wang, Y., Li, Y., Wang, X., Zhang, X., and Solla, M., hold the authorship of documents included in the set under analysis (84 documents), including five articles considered in Phases 4 and 5 [30,[38][39][40][41]. Upon analyzing Figures 2-6, it becomes evident that the number of scientific publications related to the use of UAVs for infrastructure inspection and monitoring, as well as the emergence of relevant authors in this field, are on a rising trend.…”

“…This progress stands out as a crucial element for the effective understanding and monitoring of infrastructures, including those related to transportation [20,21,23]. This technology has been comprehensively applied to the monitoring and management of construction works [41,44], including those related to road construction [45], and in the management of transport infrastructure assets, such as pavements. Application studies aimed at monitoring [7][8][9]12,13,46,47] and determining [26] the condition of flexible road pavements are common, with less expression for rigid [47], stone [28], or airport [7,17] pavements.…”

Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey

“…Shape and position control in concrete casting and precast installation requires finding accurate object/segment boundaries [7]. Volume calculations in the construction and demolition (C&D) phases can be automated from laser scanner, UAV, or satellite syn-thetic aperture radar (SAR) footage with the help of semantic segmentation [8,9]. Reverse engineering digital twins of existing buildings (as built and scan to BIM) requires assigning element classes to scan data to reconstruct element geometries [10,11].…”