Mingzhu Wang
- (2023): Assessing the impact of urban morphological parameters on land surface temperature in the heat aggregation areas with spatial heterogeneity: A case study of Nanjing. In: Building and Environment, v. 235 (May 2023).
- (2023): Scan2Drawing: Use of Deep Learning for As-Built Model Landscape Architecture. In: Journal of Construction Engineering and Management, v. 149, n. 5 (May 2023).
- (2023): Full-body pose estimation for excavators based on data fusion of multiple onboard sensors. In: Automation in Construction, v. 147 (March 2023).
- (2022): Science Mapping for Recent Research Regarding Urban Underground Infrastructure. In: Buildings, v. 12, n. 11 (27 October 2022).
- (2022): Construction and maintenance of urban underground infrastructure with digital technologies. In: Automation in Construction, v. 141 (September 2022).
- (2022): Vision-based monitoring of site safety compliance based on worker re-identification and personal protective equipment classification. In: Automation in Construction, v. 139 (July 2022).
- (2022): A framework for synthetic image generation and augmentation for improving automatic sewer pipe defect detection. In: Automation in Construction, v. 137 (May 2022).
- (2022): Mapping and modelling defect data from UAV captured images to BIM for building external wall inspection. In: Automation in Construction, v. 139 (July 2022).
- (2021): Ontology-based modelling of lifecycle underground utility information to support operation and maintenance. In: Automation in Construction, v. 132 (December 2021).
- (2022): Enriched and discriminative convolutional neural network features for pedestrian re‐identification and trajectory modeling. In: Computer-Aided Civil and Infrastructure Engineering, v. 37, n. 5 (14 March 2022).
- (2021): Automatic detection of sewer defects based on improved you only look once algorithm. In: Automation in Construction, v. 131 (November 2021).
- (2021): Automated semantic segmentation of industrial point clouds using ResPointNet++. In: Automation in Construction, v. 130 (October 2021).
- (2021): Towards an automated condition assessment framework of underground sewer pipes based on closed-circuit television (CCTV) images. In: Tunnelling and Underground Space Technology, v. 110 (April 2021).
- (2021): Construction machine pose prediction considering historical motions and activity attributes using gated recurrent unit (GRU). In: Automation in Construction, v. 121 (January 2021).
- (2020): A bi-directional missing data imputation scheme based on LSTM and transfer learning for building energy data. In: Energy and Buildings, v. 216 (June 2020).
- (2021): Automated sewer pipe defect tracking in CCTV videos based on defect detection and metric learning. In: Automation in Construction, v. 121 (January 2021).
- (2020): Full body pose estimation of construction equipment using computer vision and deep learning techniques. In: Automation in Construction, v. 110 (February 2020).
- (2020): Deep Learning–Based Automated Detection of Sewer Defects in CCTV Videos. In: Journal of Computing in Civil Engineering, v. 34, n. 1 (January 2020).
- (2019): An integrated underground utility management and decision support based on BIM and GIS. In: Automation in Construction, v. 107 (November 2019).
- (2020): A unified convolutional neural network integrated with conditional random field for pipe defect segmentation. In: Computer-Aided Civil and Infrastructure Engineering, v. 35, n. 2 (10 January 2020).
- (2018): Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques. In: Automation in Construction, v. 95 (November 2018).
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