進階搜尋


   電子論文尚未授權公開,紙本請查館藏目錄
(※如查詢不到或館藏狀況顯示「閉架不公開」,表示該本論文不在書庫,無法取用。)
系統識別號 U0026-1308201811025200
論文名稱(中文) 利用開放街圖之道路資訊輔助建物邊界線規則化
論文名稱(英文) Building Footprint Regularization Assisted by Road Vector of Open Street Map
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 106
學期 2
出版年 107
研究生(中文) 郭芯瑜
研究生(英文) Hsin-Yu Kuo
學號 P66054060
學位類別 碩士
語文別 英文
論文頁數 62頁
口試委員 指導教授-饒見有
口試委員-林昭宏
口試委員-蔡榮得
中文關鍵字 建物邊界線  規則化  開放街圖  三維房屋模型 
英文關鍵字 Building Footprint, Regularization  Open Street Map  3D Building Models 
學科別分類
中文摘要 建物邊界線萃取在許多城市相關應用中是很重要的一環,舉凡城市變遷分析、都市計劃、災害管理等議題,都需要由建物邊界線來輔助完成。使用物件導向式影像分析,能夠有效率地從正射影像和數值地表模型中,偵測得到城市中的建物邊界線。但在物件導向影像式分析的過程中,會對影像進行切割,導致筆直的建物邊界線及直角特徵消失,因像元矩形邊界的特性而產生不規則的鋸齒狀,因此本研究的目的為開發建物邊界線規則化的演算法,使物件導向影像分析所萃取之建物邊界線更接近實際狀況。研究測試區位於台北市,測試區內涵蓋各式各樣的建築物型態,建物屋頂也包含不同的形狀和紋理,如此高密度與形式複雜的房屋對規則化產生極大的挑戰,連商用化軟體,例如ArcGIS,也無法處理。本研究由高解析衛星影像產製之真實正射影像和數值地表模型,以物件導向式影像分析取得建物邊界線,並進行簡化(Simplification)與規則化(Regularization)。基於都市地區大部分的建物邊界線與道路間具有方向一致之特性,因此開放街圖(Open Street Map)的道路資訊可用來輔助建物主軸的判斷,透過分析建物周圍的道路方向決定出建物主軸。接著使用規則化演算法將房屋線修正成與主軸一致的方向,得到筆直之建物邊界線與直角。另一方面,在地狹人稠的都市中,大部分房屋都毗鄰而立,因此在演算法中同時也考量了相鄰建物間的拓樸關係,建物邊界線共邊的部分將同時被規則化,以得到一致之成果,避免共邊部份產生重疊或破洞,維持建物間正確的拓樸關係,使成果更符合相關應用之需求。
英文摘要 Building footprint is an essential part of urban environmental studies. Object-Based Image Analysis (OBIA) can be utilized to efficiently extract the building footprint from the Digital Surface Model (DSM) and true-orthoimage especially for the large urban area. The procedure of OBIA is to segment the image into image objects and classify them into different land covers according to object features. Then, the building footprint can be extracted from the building class. However, the building footprint is jagged and irregular because of image segmentation. Furthermore, the original straight edges and right-angles of building footprint may disappear. Thus, in this research, we propose an algorithm to regularize the building footprint. The study area is located in Taipei City. There are various types of buildings which can be used to evaluate the potential of the proposed method. The building density in our study area is very high and many buildings are connected to each other. It is a difficult challenge, even the commercial software cannot handle it, such as ArcGIS. Hence, the shared edges of adjacent buildings should be handled separately to keep the topology correct and must not have overlap and gap between buildings. The Douglas-Peucker algorithm is utilized to simplify the building footprint. The complexity of footprint features will be reduced and be more concise. After simplification, the building footprint needs to be regularized. Most of the buildings are rectangular which have the same direction to the road nearby. Therefore, the principal orientations of buildings can be determined based on road vector to assist the building footprint regularization. Moreover, the shared edges are simplified and regularized only once and applied for the polygons on both sides, which can avoid the topology errors. Through the proposed approach, the regularized building footprint will be the straight line with right-angle which are regular and reasonable.
論文目次 摘要 I
Abstract II
致謝 III
CATALOG IV
List of Tables VII
List of Figures VIII
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 3
1.3 Research Approach 4
1.4 Thesis Structure 5
Chapter 2 Literature Review 7
2.1 Building Footprint Extraction 7
2.2 Building Footprint Simplification 8
2.3 Building Footprint Regularization 8
Chapter 3 Study Area and Material 10
3.1 Study Area 10
3.2 True-orthoimage and Digital Surface Model 11
3.3 Road Vector 15
3.4 Building Footprint 16
3.4.1 Multiresolution Segmentation (MRS) 16
3.4.2 Classification 17
3.4.3 Individual Building Footprint 19
3.4.4 Initial Building Footprint 19
Chapter 4 Methodology 21
4.1 Workflow 21
4.2 Build Topology 23
4.3 Simplification 24
4.4 Determination of Principal Orientations 26
4.5 Regularization 28
4.6 Consideration of Road Direction 31
4.7 Building Footprint Improvement 32
4.7.1 Topology Checking 32
4.7.2 Remove the Small Polygons around Buildings 33
4.8 LOD-1 Building Models 34
Chapter 5 Case Studies and Analysis 36
5.1 Building Footprint Simplification 36
5.1.1 Comparisons of Simplification Considering Shared Edges 36
5.1.2 Comparisons of Simplification with Different Tolerance 37
5.1.3 Simplification Results 39
5.2 Building Footprint Regularization 40
5.2.1 Comparisons of Regularization Results 40
5.2.2 Results of the Proposed Algorithm 41
5.2.3 Comparisons between the Results and the Ground Truth 44
5.3 Exceptional Cases of Regularization 46
5.3.1 Different Circumstances of Road Vector 46
5.3.2 Different Shapes of Buildings 49
5.3.3 Different Orientations of Buildings and Road Vector 50
5.4 LOD-1 Building Models 51
Chapter 6 Conclusions and Suggestions 58
References 60
參考文獻 Albert, J., Bachmann, M., Hellmeier, A., 2003. Zielgruppen und anwendungen für digitale stadtmodelle und digitale geländemodelle. Erhebungen im Rahmen der SIG 3D der GDI NRW.
Alharthy, A., Bethel, J., 2002. Heuristic filtering and 3D feature extraction from LIDAR data. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences 34, 29-34.
Arroyo Ohori, K., Ledoux, H., Stoter, J., 2015. A dimension-independent extrusion algorithm using generalised maps. International Journal of Geographical Information Science 29, 1166-1186.
ASTRIUM, 2012. PLEIADES imagery user guide. An EADS Company
Baatz, M., Schaepe, A., 2000. Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. Angewandte geographische informationsverarbeitung XII 58, 12-23.
Blaschke, T., 2010. Object based image analysis for remote sensing. ISPRS journal of photogrammetry and remote sensing 65, 2-16.
Blaschke, T., Lang, S., Lorup, E., Strobl, J., Zeil, P., 2000. Object-oriented image processing in an integrated GIS/remote sensing environment and perspectives for environmental applications. Environmental information for planning, politics and the public 2, 555-570.
Dorninger, P., Pfeifer, N., 2008. A comprehensive automated 3D approach for building extraction, reconstruction, and regularization from airborne laser scanning point clouds. Sensors 8, 7323-7343.
Douglas, D.H., Peucker, T.K., 1973. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: The International Journal for Geographic Information and Geovisualization 10, 112-122.
eCognition, 2014. eCognition Developer Reference Book, München, Germany: Trimble Germany GmbH.
Gröger, G., Kolbe, T., Nagel, C., HÄFELE, K., 2012. OGC City Geography Markup Language (CityGML) En-coding Standard. Open Geospatial Consortium Inc, OGC.
Gribov, A., 2017. Searching for a compressed polyline with a minimum number of vertices, Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on. IEEE, pp. 13-14.
Haala, N., Kada, M., 2010. An update on automatic 3D building reconstruction. ISPRS Journal of Photogrammetry and Remote Sensing 65, 570-580.
Ledoux, H., Meijers, M., 2011. Topologically consistent 3D city models obtained by extrusion. International Journal of Geographical Information Science 25, 557-574.
Opheim, H., 1981. Smoothing a digitized curve by data reduction methods, Proceedings of the International Conference and Exhibition, Eurographics, pp. 127-135.
Opheim, H., 1982. Fast data reduction of a digitized curve. Geo-processing 2, 33-40.
Perera, S.N., Nalani, H.A., Maas, H.-G., 2012. An automated method for 3D roof outline generation and regularization in airborne laser scanner data. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci, 281-286.
Rau, J.Y., Chen, L.C., 2003. Fast straight lines detection using Hough transform with principal axis analysis. Journal of Photogrammetry and Remote Sensing 8, 15-34.
Sampath, A., Shan, J., 2007. Building boundary tracing and regularization from airborne LiDAR point clouds. Photogrammetric Engineering & Remote Sensing 73, 805-812.
Shi, W., Cheung, C., 2006. Performance evaluation of line simplification algorithms for vector generalization. The Cartographic Journal 43, 27-44.
Su, B.W., Rau, J.Y., Cheng, T.C.T., Lo, E.Y.M., Pan, T.C., 2017. Building footprint extraction from HRSI derived DSM and orthoimage, International Symposium on Remote Sensing 2017, Nagoya, Japan, pp. 409-412.
Teo, T.A., Chen, L.C., 2004. Object-based building detection from LiDAR data and high resolution satellite imagery, Proceedings of the 25th Asian Conference on Remote Sensing, p. 6.
Whiteside, T., Ahmad, W., 2005. A comparison of object-oriented and pixel-based classification methods for mapping land cover in northern Australia, Proceedings of SSC2005 Spatial intelligence, innovation and praxis: The national biennial Conference of the Spatial Sciences Institute, pp. 1225-1231.
Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., Schirokauer, D., 2006. Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering & Remote Sensing 72, 799-811.
Zhao, Z., Saalfeld, A., 1997. Linear-time sleeve-fitting polyline simplification algorithms, Proceedings of AutoCarto. Citeseer, pp. 214-223.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2023-08-15起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2023-08-15起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw