• Journal of Electrical Engineering and Technology
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• v.3 no.3
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• pp.436-443
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• 2008

This paper presents a new method for building detection and reconstruction from aerial images. In our approach, we extract useful building location information from the generated disparity map to segment the interested objects and consequently reduce unnecessary line segments extracted in the low level feature extraction step. Hypothesis selection is carried out by using an undirected graph, in which close cycles represent complete rooftops hypotheses. We test the proposed method with the synthetic images generated from Avenches dataset of Ascona aerial images. The experiment result shows that the extracted 3D line segments of the reconstructed buildings have an average error of 1.69m and our method can be efficiently used for the task of building detection and reconstruction from aerial images.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.177-181
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• 1999

This paper presents an algorithm for building detection from aerial image using segment measure function and line relation. In the detection algorithm proposed, edge detection, linear approximation and line linking are used and then line measure function is applied to each line segment in order to improve the accuracy of linear approximation. Parallelisms, orthogonalities are applied to the extracted liner segments to extract building. The algorithm was applied to aerial image and the buildings were accurately detected.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.4
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• pp.46-55
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• 2000

This paper presents an algorithm for 3D building reconstruction from a pair of stereo aerial images using the 3D building model and the linear segments of building. Direct extraction of linear segments from original building images using parametric building model is attempted instead of employing the conventional procedures such as edge detection, linear approximation and line linking A segment measure function is simultaneously applied to each line segment extracted in order to improve the accuracy of building detection comparing to individual linear segment detection. The algorithm has been applied to pairs of stereo aerial images and the result showed accurate detection and reconstruction of buildings.

• Structural Engineering and Mechanics
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• v.18 no.6
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• pp.709-729
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• 2004

This paper presents numerical and experimental investigations on damage detection of mono-coupled multistory buildings using natural frequency as only diagnostic parameter. Frequency equation of a mono-coupled multistory building is first derived using the transfer matrix method. Closed-form sensitivity equation is established to relate the relative change in the stiffness of each story to the relative changes in the natural frequencies of the building. Damage detection is then performed using the sensitivity equation with its special features and minimizing the norm of an objective function with an inequality constraint. Numerical and experimental investigations are finally conducted on a mono-coupled 3-story building model as an application of the proposed algorithm, in which the influence of modeling error on the degree of accuracy of damage detection is discussed. A mono-coupled 10-story building is further used to examine the capability of the proposed algorithm against measurement noise and incomplete measured natural frequencies. The results obtained demonstrate that changes in story stiffness can be satisfactorily detected, located, and quantified if all sensitive natural frequencies to damaged stories are available. The proposed damage detection algorithm is not sensitive to measurement noise and modeling error.

• 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.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1137-1139
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• 2003

This paper proposed a practical method for building detection and extraction using airborne laser scanning data. The proposed method consists mainly of two processes: low and high level processes. The major distinction from the previous approaches is that we introduce a concept of pseudogrid (or binning) into raw laser scanning data to avoid the loss of information and accuracy due to interpolation as well as to define the adjacency of neighboring laser point data and to speed up the processing time. The approach begins with pseudo-grid generation, noise removal, segmentation, grouping for building detection, linearization and simplification of building boundary , and building extraction in 3D vector format. To achieve the efficient processing, each step changes the domain of input data such as point and pseudo-grid accordingly. The experimental results shows that the proposed method is promising.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.989-1006
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• 2020

Building change monitoring based on building detection is one of the most important fields in terms of monitoring artificial structures using high-resolution multi-temporal images such as CAS500-1 and 2, which are scheduled to be launched. However, not only the various shapes and sizes of buildings located on the surface of the Earth, but also the shadows or trees around them make it difficult to detect the buildings accurately. Also, a large number of misdetection are caused by relief displacement according to the azimuth and elevation angles of the platform. In this study, object-based building detection was performed using the azimuth angle of the Sun and the corresponding main direction of shadows to improve the results of building change detection. After that, the platform's azimuth and elevation angles were used to detect changed buildings. The object-based segmentation was performed on a high-resolution imagery, and then shadow objects were classified through the shadow intensity, and feature information such as rectangular fit, Gray-Level Co-occurrence Matrix (GLCM) homogeneity and area of each object were calculated for building candidate detection. Then, the final buildings were detected using the direction and distance relationship between the center of building candidate object and its shadow according to the azimuth angle of the Sun. A total of three methods were proposed for the building change detection between building objects detected in each image: simple overlay between objects, comparison of the object sizes according to the elevation angle of the platform, and consideration of direction between objects according to the azimuth angle of the platform. In this study, residential area was selected as study area using high-resolution imagery acquired from KOMPSAT-3 and Unmanned Aerial Vehicle (UAV). Experimental results have shown that F1-scores of building detection results detected using feature information were 0.488 and 0.696 respectively in KOMPSAT-3 image and UAV image, whereas F1-scores of building detection results considering shadows were 0.876 and 0.867, respectively, indicating that the accuracy of building detection method considering shadows is higher. Also among the three proposed building change detection methods, the F1-score of the consideration of direction between objects according to the azimuth angles was the highest at 0.891.

• Smart Structures and Systems
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• v.12 no.3_4
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• pp.399-414
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• 2013

The smart sensor technology has opened new horizons for assessing and monitoring structural health of civil infrastructure. Smart sensor's unique features such as onboard computation, wireless communication, and cost effectiveness can enable a dense network of sensors that is essential for accurate assessment of structural health in large-scale civil structures. While most research efforts to date have been focused on realizing wireless smart sensor networks (WSSN) on bridge structures, relatively less attention is paid to applying this technology to buildings. This paper presents a decentralized damage detection using the WSSN for building structures. An existing flexibility-based damage detection method is extended to be used in the decentralized computing environment offered by the WSSN and implemented on MEMSIC's Imote2 smart sensor platform. Numerical simulation and laboratory experiment are conducted to validate the WSSN for decentralized damage detection of building structures.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.2
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• pp.3-10
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• 2018

Damage location and extent of structure could be detected by the inverse analysis on dynamic response properties such as frequencies and mode shapes. In practice the measured difference of natural frequencies represent the stiffness change reliably, however the measured mode shape is insensitive for stiffness change, but provides spatial information of damage. The damage detection index on shear building structures is formulated in this study. The damage detection index could be estimated from mode shape and srory stiffness of undamaged structure and frequency difference between undamaged and damaged structure. For the verification of the observed damage detection method, the numerical analysis of Matlab and MIDAS and shacking table test were performed. In results, the damage index of damaged story was estimated so higher than undamaged stories that indicates the damaged story apparently.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.111-122
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• 2021

Building footprint extraction is an active topic in the domain of remote sensing, since buildings are a fundamental unit of urban areas. Deep convolutional neural networks successfully perform footprint extraction from optical satellite images. However, semantic segmentation produces coarse results in the output, such as blurred and rounded boundaries, which are caused by the use of convolutional layers with large receptive fields and pooling layers. The objective of this study is to generate visually enhanced building objects by directly extracting the vertices of individual buildings by combining instance segmentation and keypoint detection. The target keypoints in building extraction are defined as points of interest based on the local image gradient direction, that is, the vertices of a building polygon. The proposed framework follows a two-stage, top-down approach that is divided into object detection and keypoint estimation. Keypoints between instances are distinguished by merging the rough segmentation masks and the local features of regions of interest. A building polygon is created by grouping the predicted keypoints through a simple geometric method. Our model achieved an F1-score of 0.650 with an mIoU of 62.6 for building footprint extraction using the OpenCitesAI dataset. The results demonstrated that the proposed framework using keypoint estimation exhibited better segmentation performance when compared with Mask R-CNN in terms of both qualitative and quantitative results.