• Title/Summary/Keyword: 3D Geo-Spatial Information

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Production and Accuracy Analysis of Topographic Status Map Using Drone Images (드론영상을 이용한 지형 현황도 제작 및 정확도 분석)

  • Kim, Doopyo;Back, Kisuk;Kim, Sungbo
    • Journal of the Korean GEO-environmental Society
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    • v.22 no.2
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    • pp.35-39
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    • 2021
  • Photogrammetry using drone can produce high-resolution ortho image and acquire high-accuracy 3D information, which is useful. Therefore, this study attempted to determine the possibility of using drone-photogrammetry in park construction by producing a topographic map using drone-photogrammetry and analyzing the problems and accuracy generated during production. For this purpose, we created ortho image and DSM (digital surface model) using drone images and created topographic status map by vectorizing them. Accuracy was compared based on topographic status map by GPS (global positioning system) and TS (total station). The resulting of analyzing mean of the residuals at check points showed that 0.044 m in plane and 0.066 m in elevation, satisfying the tolerance range of 1/1,000 numerical maps, and result of compared lake size showed a difference of about 4.4%. On the other hand, it was difficult to obtain accurate height values for terrain in which existed vegetation when producing the topographic map, and in the case of underground buried objects, it is not possible to confirm it in the image, so direct spatial information acquisition was necessary. Therefore, it is judged that the topographic status map using drone photogrammetry can be efficiently constructed if direct spatial data acquisition is achieved for some terrain.

Rainfall Intensity Estimation Using Geostationary Satellite Data Based on Machine Learning: A Case Study in the Korean Peninsula in Summer (정지 궤도 기상 위성을 이용한 기계 학습 기반 강우 강도 추정: 한반도 여름철을 대상으로)

  • Shin, Yeji;Han, Daehyeon;Im, Jungho
    • Korean Journal of Remote Sensing
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    • v.37 no.5_3
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    • pp.1405-1423
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    • 2021
  • Precipitation is one of the main factors that affect water and energy cycles, and its estimation plays a very important role in securing water resources and timely responding to water disasters. Satellite-based quantitative precipitation estimation (QPE) has the advantage of covering large areas at high spatiotemporal resolution. In this study, machine learning-based rainfall intensity models were developed using Himawari-8 Advanced Himawari Imager (AHI) water vapor channel (6.7 ㎛), infrared channel (10.8 ㎛), and weather radar Column Max (CMAX) composite data based on random forest (RF). The target variables were weather radar reflectivity (dBZ) and rainfall intensity (mm/hr) converted by the Z-R relationship. The results showed that the model which learned CMAX reflectivity produced the Critical Success Index (CSI) of 0.34 and the Mean-Absolute-Error (MAE) of 4.82 mm/hr. When compared to the GeoKompsat-2 and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) rainfall intensity products, the accuracies improved by 21.73% and 10.81% for CSI, and 31.33% and 23.49% for MAE, respectively. The spatial distribution of the estimated rainfall intensity was much more similar to the radar data than the existing products.

3D Reconstruction of Pipe-type Underground Facility Based on Stereo Images and Reference Data (스테레오 영상과 기준데이터를 활용한 관로형 지하시설물 3차원 형상 복원)

  • Cheon, Jangwoo;Lee, Impyeong
    • Korean Journal of Remote Sensing
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    • v.38 no.6_1
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    • pp.1515-1526
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    • 2022
  • Image-based 3D reconstruction is to restore the shape and color of real-world objects, and image sensors mounted on mobile platforms are used for positioning and mapping purposes in indoor and outdoor environments. Due to the increase in accidents in underground space, the location accuracy problem of underground spatial information has been raised. Image-based location estimation studies have been conducted with the advantage of being able to determine the 3D location and simultaneously identify internal damage from image data acquired from the inside of pipeline-type underground facilities. In this study, we studied 3D reconstruction based on the images acquired inside the pipe-type underground facility and reference data. An unmanned mobile system equipped with a stereo camera was used to acquire data and image data within a pipe-type underground facility where reference data were placed at the entrance and exit. Using the acquired image and reference data, the pipe-type underground facility is reconstructed to a geo-referenced 3D shape. The accuracy of the 3D reconstruction result was verified by location and length. It was confirmed that the location was determined with an accuracy of 20 to 60 cm and the length was estimated with an accuracy of about 20 cm. Using the image-based 3D reconstruction method, the position and line-shape of the pipe-type underground facility will be effectively updated.

Producing True Orthophoto Using Multi-Dimensional Spatial Information (다차원공간정보를 이용한 실감정사영상 제작 방안)

  • Lee, Hyun-Jik
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.26 no.3
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    • pp.241-253
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    • 2008
  • Recently, it is appearing that new paradigm of urban planning that ubiquitous concept such as the u-City, uECO-City is introduced while is rising necessity about third dimensional geo-spatial information of high quality for urban area. Orthophoto can manufacture by expense and time that is less easily than digital map using personal computer even if is not highly technician and according as position relation between manmade feature and natural feature is equal, can get information of distance, angle, horizontal and vertical position coordinate of topographic, area etc.. directly through orthophoto. Also, visual effect is good that orthophoto is expressed by image and interpretation is easy to detailed part of topographic. Manufacture and practical use are consisting in various field, for it is having advantage that can recognize information effectively than digital map. Therefore, this study presents a way of generating a detailed DSM for producing a true-orthphoto of the urban area, and this study also presents a way to produce an optimum true-orthophoto for an urban area by investigating through experiment the optimum variable for the geometric and radiometric correction of the orthophoto. This study also examined the potentials of the thesis by building a 3-dimensional city model of the model region with the above thesis on optimum generating method.

D4AR - A 4-DIMENSIONAL AUGMENTED REALITY - MODEL FOR AUTOMATION AND VISUALIZATION OF CONSTRUCTION PROGRESS MONITORING

  • Mani Golparvar-Fard;Feniosky Pena-Mora
    • International conference on construction engineering and project management
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    • 2009.05a
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    • pp.30-31
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    • 2009
  • Early detection of schedule delay in field construction activities is vital to project management. It provides the opportunity to initiate remedial actions and increases the chance of controlling such overruns or minimizing their impacts. This entails project managers to design, implement, and maintain a systematic approach for progress monitoring to promptly identify, process and communicate discrepancies between actual and as-planned performances as early as possible. Despite importance, systematic implementation of progress monitoring is challenging: (1) Current progress monitoring is time-consuming as it needs extensive as-planned and as-built data collection; (2) The excessive amount of work required to be performed may cause human-errors and reduce the quality of manually collected data and since only an approximate visual inspection is usually performed, makes the collected data subjective; (3) Existing methods of progress monitoring are also non-systematic and may also create a time-lag between the time progress is reported and the time progress is actually accomplished; (4) Progress reports are visually complex, and do not reflect spatial aspects of construction; and (5) Current reporting methods increase the time required to describe and explain progress in coordination meetings and in turn could delay the decision making process. In summary, with current methods, it may be not be easy to understand the progress situation clearly and quickly. To overcome such inefficiencies, this research focuses on exploring application of unsorted daily progress photograph logs - available on any construction site - as well as IFC-based 4D models for progress monitoring. Our approach is based on computing, from the images themselves, the photographer's locations and orientations, along with a sparse 3D geometric representation of the as-built scene using daily progress photographs and superimposition of the reconstructed scene over the as-planned 4D model. Within such an environment, progress photographs are registered in the virtual as-planned environment, allowing a large unstructured collection of daily construction images to be interactively explored. In addition, sparse reconstructed scenes superimposed over 4D models allow site images to be geo-registered with the as-planned components and consequently, a location-based image processing technique to be implemented and progress data to be extracted automatically. The result of progress comparison study between as-planned and as-built performances can subsequently be visualized in the D4AR - 4D Augmented Reality - environment using a traffic light metaphor. In such an environment, project participants would be able to: 1) use the 4D as-planned model as a baseline for progress monitoring, compare it to daily construction photographs and study workspace logistics; 2) interactively and remotely explore registered construction photographs in a 3D environment; 3) analyze registered images and quantify as-built progress; 4) measure discrepancies between as-planned and as-built performances; and 5) visually represent progress discrepancies through superimposition of 4D as-planned models over progress photographs, make control decisions and effectively communicate those with project participants. We present our preliminary results on two ongoing construction projects and discuss implementation, perceived benefits and future potential enhancement of this new technology in construction, in all fronts of automatic data collection, processing and communication.

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Orthophoto Application for Geo-spatial Information Acquisiton of Construction Area(DAM) (공사지역(댐)의 지형정보구축을 위한 정사영상의 활용)

  • 한승희;이형석;이성순
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.18 no.4
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    • pp.395-403
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    • 2000
  • It is so sensitive that the matter of compensation for private possessions caused in the course of public construction planning is very important. Especially, more logical planning is necessary when the dam be constructed, because it is mainly controlled by the surface of water, and if that planning is made public, artificial change is occurred in land use in that area for the purpose of rising the compensation. In this study, the plan for the application of aerial photo based ortho image was drew up for solving these problems and for the rational, rapid compensation. Ortho image was made by aerial photo, used as reading material for the change in land use. The modeling of drainage basin, came under the planning surface of water, and the 3D simulation were performed for the scene analysis, the change understanding in land use for a lot number in a certain period by overlapping the digital image, the digital land registration map, and the digital topography map as well as the analysis of the admitted land followed by the height of reservoir water.

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Comparison Among Sensor Modeling Methods in High-Resolution Satellite Imagery (고해상도 위성영상의 센서모형과 방법 비교)

  • Kim, Eui Myoung;Lee, Suk Kun
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.26 no.6D
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    • pp.1025-1032
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    • 2006
  • Sensor modeling of high-resolution satellites is a prerequisite procedure for mapping and GIS applications. Sensor models, describing the geometric relationship between scene and object, are divided into two main categories, which are rigorous and approximate sensor models. A rigorous model is based on the actual geometry of the image formation process, involving internal and external characteristics of the implemented sensor. However, approximate models require neither a comprehensive understanding of imaging geometry nor the internal and external characteristics of the imaging sensor, which has gathered a great interest within photogrammetric communities. This paper described a comparison between rigorous and various approximate sensor models that have been used to determine three-dimensional positions, and proposed the appropriate sensor model in terms of the satellite imagery usage. Through the case study of using IKONOS satellite scenes, rigorous and approximate sensor models have been compared and evaluated for the positional accuracy in terms of acquirable number of ground controls. Bias compensated RFM(Rational Function Model) turned out to be the best among compared approximate sensor models, both modified parallel projection and parallel-perspective model were able to be modelled with a small number of controls. Also affine transformation, one of the approximate sensor models, can be used to determine the planimetric position of high-resolution satellites and perform image registration between scenes.