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研究生： 蘇帕默
Soedarmodjo, Theo Prastomo
論文名稱： 基於DSM進行LOD-1三維建物模塑及其品質與精度評估
Quality and Accuracy Evaluation of LOD-1 3D Building Modeling Based on DSM
指導教授： 饒見有
Rau, Jiann-Yeou
學位類別： 碩士
Master
系所名稱： 工學院 - 測量及空間資訊學系
Department of Geomatics
論文出版年： 2019
畢業學年度： 108
語文別： 英文
論文頁數： 82
外文關鍵詞： 3D Building Model, LOD-1, DSMs
相關次數： 點閱：89  下載：6 
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    • The 3D building model is a product of the 3D city model. Geometric information from the 3D building model is very important to know the quality of the modeling product. There are various data sources that can produce 3D building models such as Digital Surface Models (DSMs) data, where DSMs are raster data reflecting the elevation information above the earth's surface. DSMs can be produced from various sources such as professional aerial photogrammetry, airborne laser scanning (ALS) and even unmanned aerial photogrammetry (UAV). Each data has its advantages of making the 3D building model. CityGML is an international standardization in 3D city modeling. In CityGML, the level-of-detail (LOD) is a scale used to represent the detail in modeling an object. Among which the LOD-1 building model describe a building with flat roof and vertical façade. In this study, we perform 3D building modeling based on the LOD-1 CityGML standard using different data sources. The object-based segmentation is carried out to each DSMs data then the results were compared with reference data that were manually delineated from UAV true-ortho image. Then the outcomes are evaluated to assess the behavior of the data employed. The quality and accuracy evaluation procedures were performed to analyze the results, including the total number of extracted roof objects, completeness, planimetric error accuracy, and elevation accuracy of several building footprints. This research was conducted in the area around the NCKU campus, Tainan City, Taiwan.

    ABSTRACT I ACKNOWLEDGEMENTS II TABLE OF CONTENTS III LIST OF TABLES VI LIST OF FIGURES VII CHAPTER 1. INTRODUCTION 1 1.1. Motivation 1 1.2. Research Objectives 2 1.3. Thesis Structure 3 CHAPTER 2. LITERATURE REVIEW 4 2.1. 3D City Modeling 4 2.1.1. 3D City from Aerial Photogrammetry 5 2.1.2. 3D City from Satellite Photogrammetry 5 2.1.3. 3D City from Close-range Photogrammetric processing 6 2.1.4. 3D City from Aerial Laser Scanning 6 2.1.5. 3D City from Terrestrial Laser Scanning 7 2.2. CityGML Standard 8 2.3. LOD-1 3D Building Model 10 2.4. Accuracy Evaluation 11 CHAPTER 3. MATERIAL AND STUDY AREA 13 3.1. Digital Surface Models 13 3.1.1 Professional Aerial Photogrammetry 13 3.1.2 Airborne Laser Scanning 19 3.1.3 Unmanned Aerial Photogrammetric Survey 20 3.2. Building Block Boundary 21 3.3. Reference Data 23 3.4. Study Area 24 CHAPTER 4. METHODOLOGY 26 4.1. LOD-1 3D Building Model Reconstruction 26 4.2. OHM Extraction 27 4.3. Resampling 28 4.4. Segmentation 29 4.5. Regularization of Building Footprint 37 4.6. LOD-1 Building Models 38 4.7. Clipping 38 4.8. Quality Evaluations 39 4.9. Accuracy Assessments 41 4.9.1. Total number of extracted roof objects analysis 42 4.9.2. Completeness (omitted/committed) analysis 42 4.9.3. Planimetric error analysis 43 4.9.4. Elevation accuracy analysis 45 CHAPTER 5. RESULTS AND DISCUSSIONS 47 5.1. Quality Evaluation on 2D aspect from building footprints 47 5.1.1. Quality Evaluation 2D aspect of 100cm OHM building footprints 47 5.1.2. Quality Evaluation 2D aspect of 25cm OHM building footprints 51 5.1.3. Quality Evaluation 2D aspect of 10cm OHM building footprints 55 5.2. Quality Evaluation 3D Aspect from LOD-1 Building Models Extraction 58 5.2.1. Quality Evaluation 3D aspect of OHM-100cm LOD-1 3D Building Model 58 5.2.2. Quality Evaluation 3D aspect of OHM-25cm LOD-1 3D Building Model 61 5.2.3. Quality Evaluation 3D aspect of OHM-10cm LOD-1 3D Building Model 64 5.3. Accuracy Assessment 2D Aspect from Building Footprint 66 5.3.1. Total number of extracted roof objects analysis 66 5.3.2. OHM 100cm analysis 67 5.3.3. OHM 25cm Analysis 69 5.3.4. OHM 10cm Analysis 72 CHAPTER 6. CONCLUSIONS AND SUGGESTIONS 75 REFERENCES 79

    Amat, N.A., Setan, H., Majid, Z., 2010. Integration of Aerial and Close-Range Photogrammetric Methods for 3D City Modeling Generation. Geoinformation Science Journal 10, 49-60.
    Attarzadeh, R., Momeni, M., 2017. Object-Based Rule Sets and Its Transferability for Building Extraction from High Resolution Satellite Imagery. Journal of the Indian Society of Remote Sensing 46, 169-178.
    Baatz, M., Schäpe, A., 2000. Multiresolution Segmentation: An optimization approach for high quality multi-scale image segmentation. Proceedings of the Beiträge zum AGIT-Symposium, 12-23.
    Biljecki, F., Ledoux, H., Stoter, J., 2016. An improved LOD specification for 3D building models. Computers, Environment and Urban Systems 59, 25-37.
    Comert, R., Kaplan, O., 2018. Object Based Building Extraction and Building Period Estimation from Unmanned Aerial Vehicle Data. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-3, 71-76.
    Dodgson, N.A., 1992. Image resampling, in: UCAM-CL-TR-261 (Ed.). University of Cambridge, Computer Laboratory, United Kingdom.
    Dorninger, P., Pfeifer, N., 2008. A Comprehensive Automated 3D Approach for Building Extraction, Reconstruction, and Regularization from Airborne Laser Scanning Point Clouds. Sensors (Basel) 8, 7323-7343.
    El Garouani, A., Alobeid, A., El Garouani, S., 2014. Digital surface model based on aerial image stereo pairs for 3D building. International Journal of Sustainable Built Environment 3, 119-126.
    Gröger, G., Kolbe, T., Nagel, C., Häfele, K.-H., 2012. OGC City Geography Markup Language (CityGML) En-coding Standard. Open Geospatial Consortium, 1 - 344.
    Julin, A., Jaalama, K., Virtanen, J.P., Pouke, M., Ylipulli, J., Vaaja, M., Hyyppa, J., Hyyppa, H., 2018. Characterizing 3D City Modeling Projects: Towards a Harmonized Interoperable System. Isprs International Journal of Geo-Information 7, 18.
    Kocaman, S., Zhang, L., Gruen, A., Poli, D., 2006. 3D city modeling from high-resolution satellite images. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 36, 1.
    Kolbe, T.H., Nagel, C., Herreruela, J., 2013. 3D city database for CityGML, Addendum to the 3D City Database Documentation Version 2.0.1 (Master Theses). Technische Universität Berlin, Berlin, Germany.
    Kuo, H.-Y., 2018. Building Footprint Regularization Assisted by Road Vector of Open Street Map (Master Theses), Department of Geomatics. National Cheng Kung University, Tainan, Taiwan.
    Lang, S., 2008. Object-based image analysis for remote sensing applications: modeling reality–dealing with complexity. Springer, Berlin, Heidelberg.
    Li-Chee-Ming, J., Gumerov, D., Ciobanu, T., Armenakis, C., 2009. Generation of three dimensional photo-realistic models from LiDAR and image data. 2009 IEEE toronto international conference science and technology for humanity (TIC-STH), 445-450.
    Mao, B., Harrie, L., Cao, J., Wu, Z., Shen, J., 2014. NoSQL Based 3D City Model Management System. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4, 169-173.
    Papari, G., Petkov, N., Campisi, P., 2007. Artistic edge and corner enhancing smoothing. IEEE Trans Image Process 16, 2449-2462.
    Peeroo, U., Idrees, M.O., Saeidi, V., 2017. Building extraction for 3D city modelling using airborne laser scanning data and high-resolution aerial photo. South African Journal of Geomatics 6, 363-376.
    Püschel, H., Sauerbier, M., Eisenbeiss, H., 2008. A 3D model of Castle Landenberg (CH) from combined photogrammetric processing of terrestrial and UAV-based images. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci 37, 93-98.
    Shi, Y., Li, Q., Zhu, X.X., 2020. Building segmentation through a gated graph convolutional neural network with deep structured feature embedding. ISPRS Journal of Photogrammetry and Remote Sensing 159, 184-197.
    Singh, S.P., Jain, K., Mandla, V.R., 2013. Virtual 3D city modeling: techniques and applications. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 73-91.
    Svensk, J., 2017. Evaluation of aerial image stereo matching methods for forest variable estimation, Department of Electrical Engineering. Linköping University, Linköping, Sweden.
    Tao, C.V., Hu, Y., 2002. 3D reconstruction methods based on the rational function model. Photogrammetry Engineering and Remote Sensing 68, 705-714.
    Trimble, 2014. Ecognition Developer Reference Book 9.0. Trimble Germany GmbH, München, Germany.
    Yalcin, G., Selcuk, O., 2015. 3D City Modelling with Oblique Photogrammetry Method. Procedia Technology 19, 424-431.

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