• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.3
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• pp.173-179
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• 2015

In this paper, object-based classification of urban areas based on a combination of information from lidar and aerial images is introduced. High resolution images are frequently used in automatic classification, making use of the spectral characteristics of the features under study. However, in urban areas, pixel-based classification can be difficult since building colors differ and the shadows of buildings can obscure building segmentation. Therefore, if the boundaries of buildings can be extracted from lidar, this information could improve the accuracy of urban area classifications. In the data processing stage, lidar data and the aerial image are co-registered into the same coordinate system, and a local maxima filter is used for the building segmentation of lidar data, which are then converted into an image containing only building information. Then, multiresolution segmentation is achieved using a scale parameter, and a color and shape factor; a compactness factor and a layer weight are implemented for the classification using a class hierarchy. Results indicate that lidar can provide useful additional data when combined with high resolution images in the object-oriented hierarchical classification of urban areas.

• Proceedings of the CALSEC Conference
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• 2003.09a
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• pp.249-254
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• 2003

Object caching is a desirable feature to improve the both scalability and performance of distributed application systems for information infrastructure, the information management system leveraging the power of network computing. However, in order to exploit such benefits, we claim that the following problems: cache server placement, cache replacement, and cache synchronization, should be considered when designing any object cache system. We are under developing DOC: a Distributed Object Caching, as a part of building our information infrastructures. In this paper, we show how each problem is inter-related, and focus to highlight how we handle cache server deployment problem

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.587-598
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• 2022

To increase building change recognition accuracy, we present a deep learning-based building change detection using remote sensing images. In the proposed approach, by merging pixel-level and object-level information of multitemporal remote sensing images, we create the difference image (DI), and the frequency-domain significance technique is used to generate the DI saliency map. The fuzzy C-means clustering technique pre-classifies the coarse change detection map by defining the DI saliency map threshold. We then extract the neighborhood features of the unchanged pixels and the changed (buildings) from pixel-level and object-level feature images, which are then used as valid deep neural network (DNN) training samples. The trained DNNs are then utilized to identify changes in DI. The suggested strategy was evaluated and compared to current detection methods using two datasets. The results suggest that our proposed technique can detect more building change information and improve change detection accuracy.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.381-392
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• 2021

In this study, we perform deep learning-based object detection analysis on eight types of buildings defined by the digital map topography standard code, leveraging images taken with UAV (Unmanned Aerial Vehicle). Image labeling was done for 509 images taken by UAVs and the YOLO (You Only Look Once) v5 model was applied to proceed with learning and inference. For experiments and analysis, data were analyzed by applying an open source-based analysis platform and algorithm, and as a result of the analysis, building objects were detected with a prediction probability of 88% to 98%. In addition, the learning method and model construction method necessary for the high accuracy of building object detection in the process of constructing and repetitive learning of training data were analyzed, and a method of applying the learned model to other images was sought. Through this study, a model in which high-efficiency deep neural networks and spatial information data are fused will be proposed, and the fusion of spatial information data and deep learning technology will provide a lot of help in improving the efficiency, analysis and prediction of spatial information data construction in the future.

• Spatial Information Research
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• v.19 no.2
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• pp.37-46
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• 2011

Recently in the field of GIS(Geographic Information System), data integration from various sources has become an important topic in order to use spatial data effectively. In general, the integration of spatial data is accomplished by navigating corresponding space object and combining the information interacting with each object. But it is very difficult to navigate an object which has correspondence with one in another dataset. Many matching methods have been studied for navigating spatial object. The purpose of this paper is development of method for searching correspondent spatial object considering local position error which is remained even after coordinate transform ation when two different building data sets integrated. To achieve this goal, we performed coordinate transformation and overlapped two data sets and generated blocks which have similar position error. We matched building objects within each block using similarity and ICP algorithm. Finally, we tested this method in the aspect of applicability.

• Korean Journal of Computational Design and Engineering
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• v.21 no.3
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• pp.325-333
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• 2016

This paper aims to classify objects and their properties represented in Korea Building Act sentences for applying to BIM-enabled automated code compliance checking task. In order to conduct automated code compliance checking, it is necessary to develop translation process of converting the building act sentences into computer-executable forms. However, since Korea building act sentences are written in natural language, some of requirements are ambiguous to translate explicitly. In this regard, the building act sentences regarding building permit requirements are analyzed focusing on the regulation-specific objects and related properties representation from noun phrases within the scope of this paper. From 1977 building act sentences and attached reference regulations, 1200 regulation-specific objects and about 220 related properties are extracted and classified. In the application for the classification, object-property database is implemented and some of application using the database and the regulation-specific classification is suggested to support to generate rule set written in computable codes.

• Korean Journal of Construction Engineering and Management
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• v.16 no.2
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• pp.97-105
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• 2015

Building information modeling (BIM)-based construction projects have increased and become more varied, and as such the management of BIM-based facility information is also increasingly important for facility maintenance. Information management, and specifically product data mapping, however, has some problems in the area of manual data entry and does not adequately consider the exchange requirements of facility maintenance. Therefore, it is necessary to introduce a method to improve the management of composite object information for BIM-based facility maintenance so that it can handle construction operation building information exchange (COBie) data for a composite object. Therefore, we present a method to map COBie data to related materials of a composite object. This research contributes to increasing the efficiency and accuracy of the required information mapping between a building model and product data using a BIM library through optimal BIM data adoption. Moreover, it allows for the creation and management of specific product data at the design development phase.

• Journal of KIBIM
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• v.12 no.4
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• pp.70-79
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• 2022

As BIM(Building Information Modeling) technology advances in architecture around the world, projects and industries using BIM are increasing. Unlike previous developments that were limited to buildings, BIM is now spreading to other fields such as civil engineering and electricity. In architecture, BIM is used in the entire process from design to maintenance of a building, and IFC(Industry Foundation Classes), a neutral format with interoperability, is used as an open BIM format. Since firefighting requires intuitive 3D models for evacuation and fire simulations, BIM models are desirable. However, due to the BIM model, which was developed centered on building objects, there are no objects and specific properties for fire evacuation in the IFC scheme. Therefore, in this study, when adding a new object in the firefighting area to the IFC schema, the IFC interoperability is not broken and the building WBS(Work Breakdown Structure) is analyzed with a hierarchical system similar to the IFC format to define the scope for a new object and the firefighting part within of the building WBS to derive a firefighting HBS(Hierarchy Breakdown Structure) with the extension of the object-oriented IFC file. And according to HBS, we propose an IFC schema extension method. It is a methodology that allows BIM users to instantly adapt the IFC schema to their needs. Accordingly, the methodology derived from this study is expected to be expanded in various areas to minimize information loss from IFC. In the future, we will apply the IFC extension methodology to the actual development process using HBS to verify that it is actually applicable within the IFC schema.

• Korean Journal of Remote Sensing
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• v.23 no.3
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• pp.199-209
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• 2007

Traditionally, aerial images have been used as main sources for compiling topographic maps. In recent years, lidar data has been exploited as another type of mapping data. Regarding their performances, aerial imagery has the ability to delineate object boundaries but omits much of these boundaries during feature extraction. Lidar provides direct information about heights of object surfaces but have limitations with respect to boundary localization. Considering the characteristics of the sensors, this paper proposes an approach to extracting buildings from lidar and aerial imagery, which is based on the complementary characteristics of optical and range sensors. For detecting building regions, relationships among elevation contours are represented into directional graphs and searched for the contours corresponding to external boundaries of buildings. For generating building models, a wing model is proposed to assemble roof surface patches into a complete building model. Then, building models are projected and checked with features in aerial images. Experimental results show that the proposed approach provides an efficient and accurate way to extract building models.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.136-144
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• 2022

Construction sites are characterized by dangerous situations and environments that cause fatal accidents. Potential risk detection needs to be improved by continuously monitoring site conditions. However, the current labor-intensive inspection practice has many limitations in monitoring dangerous conditions at construction sites. Computer vision technology that can quickly analyze and collect site conditions from images has been in the spotlight as a solution. Nonetheless, inspection results obtained via computer vision are still stored and managed in centralized systems vulnerable to tampering with information by the central node. Blockchain has been used as a reliable and efficient decentralized information management system. Despite its potential, only limited research has been conducted integrating computer vision and blockchain. Therefore, to solve the current safety management problems, the authors propose a framework for construction site inspection that integrates object detection and blockchain network, enabling efficient and reliable remote inspection. Object detection is applied to enable the automatic analysis of site safety conditions. As a result, the workload of safety managers can be reduced with inspection results stored and distributed reliably through the blockchain network. In addition, errors or forgery in the inspection process can be automatically prevented and verified through a smart contract. As site safety conditions are reliably shared with project participants, project participants can remotely inspect site conditions and make safety-related decisions in trust.