@article{liu_han_rasdorf_2022, title={Assessment and Prediction of Impact of Flight Configuration Factors on UAS-Based Photogrammetric Survey Accuracy}, volume={14}, ISSN={["2072-4292"]}, url={https://doi.org/10.3390/rs14164119}, DOI={10.3390/rs14164119}, abstractNote={Recent advances in computer vision and camera-equipped unmanned aerial systems (UAS) for 3D modeling enable UAS-based photogrammetry surveys with high spatial-temporal resolutions. To generate consistent and high-quality 3D models using UASs, understanding how influence factors (i.e., flight height, image overlap, etc.) affect the 3D modeling accuracy and their levels of significance are important. However, there is little to no quantitative analysis that studies how these influence factors interact with and affect the accuracy when changing the values of the influence factors. Moreover, there is little to no research that assesses more than three influence factors. Therefore, to fill this gap, this paper aims to evaluate and predict the accuracy generated by different flight combinations. This paper presents a study that (1) assessed the significance levels of five influence factors (flight height, average image quality, image overlap, ground control point (GCP) quantity, and camera focal lengths), (2) investigated how they interact and impact 3D modeling accuracy using the multiple regression (MR) method, and (3) used the developed MR models for predicting horizontal and vertical accuracies. To build the MR model, 160 datasets were created from 40 flight missions collected at a site with a facility and open terrain. For validating the prediction model, five testing datasets were collected and used at a larger site with a complex building and open terrain. The results show that the findings of this study can be applied to surveyors’ better design flight configurations that result in the highest accuracies, given different site conditions and constraints. The results also provide a reasonable prediction of accuracy given different flight configurations.}, number={16}, journal={REMOTE SENSING}, author={Liu, Yajie and Han, Kevin and Rasdorf, William}, year={2022}, month={Aug} }
 @article{noghabaei_liu_han_2022, title={Automated compatibility checking of prefabricated components using 3D as-built models and BIM}, volume={143}, ISSN={["1872-7891"]}, DOI={10.1016/j.autcon.2022.104566}, abstractNote={There have been recent efforts to use reality capture technologies to perform remote quality control in construction. However, there is a lack of research efforts in detecting construction incompatibilities in modular construction using reality capture technologies. The construction incompatibilities in modular construction often cause reworks and delays in the project schedule. To address this issue, this paper presents a general compatibility analysis method that propose scanning the modules in manufacturing plant and construction site, and check module-to-module compatibility remotely, prior to the shipment and installation. This study provides three sample module-to-module compatibility scenarios to validate the proposed compatibility analysis. The case study results show that the compatibility analysis method was able to identify the compatibility issues with high accuracy. Lastly, the compatibility analysis method was validated in terms of accuracy and time performance in six scenarios that was defined on the modules.}, journal={AUTOMATION IN CONSTRUCTION}, author={Noghabaei, Mojtaba and Liu, Yajie and Han, Kevin}, year={2022}, month={Nov} }