• Korean Journal of Construction Engineering and Management
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• v.19 no.2
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• pp.15-24
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• 2018

Despite the recognition of the need for productivity information and its importance, the feedback of productivity information is not well-established in the construction industry. Effective use of productivity information is required to improve the reliability of construction planning. However, in many cases, on-site productivity information is hardly management effectively, but rather it relies on the experience and/or intuition of project participants. Based on the literature review and expert interviews, the authors recognized that one of the possible solutions is to develop a systematic approach in dealing with productivity information of the construction job-sites. It is required that the new system should not be burdensome to users, purpose-oriented information management, easy-to follow information structure, real-time information feedback, and productivity-related factor recognition. Based on the preliminary investigations, this study proposed a framework for a novel system that facilitate the effective management of construction productivity information. This system has utilized Sketchup software which has good user accessibility by minimizing additional data input and related workload. The proposed system has been designed to input, process, and output the pertinent information through a four-stage process: preparation, input, processing, and output. The inputted construction information is classified into Task Breakdown Structure (TBS) and Material Breakdown Structure (MBS), which are constructed by referring to the contents of the standard specification of building construction, and converted into productivity information. In addition, the converted information is also graphically visualized on the screen, allowing the users to use the productivity information from the job-site. The productivity information management system proposed in this study has been pilot-tested in terms of practical applicability and information availability in the real construction project. Very positive results have been obtained from the usability and the applicability of the system and benefits are expected from the validity test of the system. If the proposed system is used in the planning stage in the construction, the productivity information and the continuous information is accumulated, the expected effectiveness of this study would be conceivably further enhanced.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.363-373
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• 2020

During last decades numerous studies generating orthoimage have been carried out. Traditional methods require exterior orientation parameters of aerial images and precise 3D object modeling data and DTM (Digital Terrain Model) to detect and recover occlusion areas. Furthermore, it is challenging task to automate the complicated process. In this paper, we proposed a new concept of true orthoimage generation using DL (Deep Learning). DL is rapidly used in wide range of fields. In particular, GAN (Generative Adversarial Network) is one of the DL models for various tasks in imaging processing and computer vision. The generator tries to produce results similar to the real images, while discriminator judges fake and real images until the results are satisfied. Such mutually adversarial mechanism improves quality of the results. Experiments were performed using GAN-based Pix2Pix model by utilizing IR (Infrared) orthoimages, intensity from LiDAR data provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) through the ISPRS (International Society for Photogrammetry and Remote Sensing). Two approaches were implemented: (1) One-step training with intensity data and high resolution orthoimages, (2) Recursive training with intensity data and color-coded low resolution intensity images for progressive enhancement of the results. Two methods provided similar quality based on FID (Fréchet Inception Distance) measures. However, if quality of the input data is close to the target image, better results could be obtained by increasing epoch. This paper is an early experimental study for feasibility of DL-based true orthoimage generation and further improvement would be necessary.