房屋模型提供了三維的空間資訊，其應用之層面非常廣泛，如都市規劃、房地產管理等位置基礎的應用。為了應用的時效，房屋模型需要進行更新，而更新變遷區域之房屋模型較重建全區域之房屋模型更有效率。這種方式需要先進行變遷之偵測，再進行重建之步驟。本研究之目標在於融合光達點雲資料以及航照影像進行三維房屋模型的變遷偵測。
本研究包含了三個步驟：(1)資料套合，(2)房屋模型變遷偵測以及(3)偵測新建房屋區域。資料套合，為進行多元資料之間套合處理的步驟。房屋模型變遷偵測，藉由檢驗航照影像的光譜資訊、光達點雲與房屋模型的高程差資訊以及航照影像之線型特徵資訊，使用規則式的判斷方法逐一對每個房屋模型進行變遷偵測。並且為了克服規則式判斷中，使用單一門檻會遇到的門檻高敏感度問題，本研究導入了雙重門檻的策略。偵測新建房屋區域，藉由去除植生、地面以及舊時期房屋區域來偵測出屬於新建房屋區域的光達點雲，使用區域成長的方法將點雲區分成不同群組，最後使由邊界追蹤的方法得到新建房屋的區域。
成果驗證中，本研究於房屋模型變遷偵測的部份，導入雙重門檻策略可使整體精度從93.1% 提升至95.9%。於偵測新建房屋區域的部份，正確偵測率為100%。並且，為了瞭解影響房屋模型變遷偵測的因素，本研究亦針對各種不同的案例進行分析。

Building models are built to provide three dimensional (3D) spatial information, which is needed for varieties of applications, such as city planning, construction of location-based services, and the like. However, three dimensional building models need to be updated from time to time. Rather than reconstructing building models for the entire area, it would be more effective to only revise the parts that have changed. This approach might be achieved by detecting changes first, followed by a reconstruction procedure. In this study, we aim at finding changes with 3D building models through a combination of LIDAR data and aerial imagery.
The proposed scheme comprises three steps, namely, (1) data registration, (2) change detection of three dimensional building models, and (3) detection of new building models. The first step performs data registration for multi-source data. The second step performs the rule-based change detection, it include examination of spectrum from aerial images, examination of height difference between building models and LIDAR points, and examination of linear features from aerial images. A double-threshold strategy is applied to cope with the highly sensitive thresholding often encountered when using the rule-based approach. In the third step, we detect the LIDAR point clouds in the new building areas by removing vegetation, ground and old building areas. We then use region growing to separate the LIDAR point clouds into different groups. Finally, we use boundary tracing to get the new building areas.
Ground truth data are used for validation. The experimental results indicate that the double-threshold strategy improves the overall accuracy from 93.1% to 95.9%. The results for new building area detection reach 100% overall accuracy. To provid comprehensive observations, the different cases are scrutinized.

王晉倫，蕭國鑫，游明芳，陳大科，劉進金，2005，”空載LiDAR資料應用於地形變化偵測”，內政部「辦理LIDAR測區之高精度及高解析度數值地形測繪、資料庫建置與應用推廣工作案」成果發表暨應用研討會，237-246頁。
史天元，彭淼祥，2003，空載光達數據檢查：以農委會台灣地區測試
新竹部分為例，URL：http://140.137.13.100/Conference/DERC001/pdf/A100.pdf。
李訢卉，2007，「整合房屋、道路及地形模型之高解析影像正射改正」，碩士論文，國立中央大學土木工程研究所。
湯美華，2006，「空載光達點雲及地形圖輔助生產真實正射影像之研究」，碩士論文，國立成功大學測量及空間資訊學系。
黃智遠，2008，「整合形狀及光譜資訊於房屋模型之變遷偵測」，碩士論文，國立中央大學土木工程研究所。
Bailloeul, T., Duan, J., Prinet, V., and Serra, B., 2003. Urban Digital Map Updating From Satellite High Resolution Images Using GIS Data as A Priori Knowledge, in Proceedings of the 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, pp. 283–287, IEEE/ISPRS, Berlin, Germany, May 2003.
Ceresola, S., Fusiello, A., Bicego, M., Belussi, A., and Murino, V., 2005. Automatic Updating of Urban Vector Maps, Lecture Notes in Computer Science, International Conference on Image Analysis and Processing, Vol. 3617, pp.1133-1139.
Chen, L. C., and Rau, J. Y., 2002, “Geometrical Building Modeling and ItsApplication to the Ortho-Rectification for Aerial Images”, Ph. D. National Central University, Taiwan.
Danahy, J., 1999. Visualization Data Needs in UrbanEnvironmental Planning and Design, Proceedings of the Photogrammetric Week, Karlsrnhe, pp.351-365.
Girardeau-Montaut, D., Roux, M., Marc, R., and Thibault, G., 2005. Change Detection on Points Cloud Data Acquired with a Ground Laser Scanner, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 36, part 3/W19, pp.30-35.
Golias, N.A. and Dutton, R.W., 1997. Delaunay triangulation and 3D adaptive mesh generation, Finite Element in Analysis and Design, Vol. 25, pp.331-341.
Hazel, G.G., 2001. Object-Level Change Detection in Spectral Imagery, IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, NO. 3, pp.553-561.
Jung, F., 2004. Detecting Building Changes from Multitemporal Aerial Stereopairs, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 58, pp.187-201.
Knudsen, T. and Olsen, B.P., 2003. Automated Change Detection for Updates of Digital Map Databases, Photogrammetric Engineering and Remote Sensing, Vol. 69, No.11, pp.1289-1296.
Kocaman, S., L. Zhang, A. Gruen, and D. Poli, 2006. 3D City Modeling from High-resolution Satellite Images, Proceedings of the ISPRS WG I/5 I/6I Workshop on "Topographic Mapping from Space (with Special Emphasis on Small Satellites)", 14-16 February 2006, Istanbul, Ankara, ISPRS Workshop Commission I, unpaginated CD-ROM.
Lee, D.H., Lee, K.M. and Lee, S.U., 2008, Fusion of Lidar and Imagery for Reliable Building Extraction, Photogrammetric Engineering and Remote Sensing, 74(2):215-225.
Leica Geosystems, 2006. Leica Geosystems brochure of ALS50-II, http://gi.leica-geosystems.com/default.aspx(accessed 6, Jun, 2006).
Matikainen, L., Hyyppa, J., and Hyyppa, H., 2003. Automatic Detection of Buildings from Laser Scanner Data for Map Updating, International Archives of Photogrammetry, Remote sensing and Spatial Information Sciences, Vol. 34, part 3/W13, pp.218-224.
Matikainen, L., Hyyppa, J., and Hyyppa, H., 2004. Automatic Detection of Changes from Laser Scanner and Aerial Image Data for Updating Building Maps, International Archives of Photogrammetry, Remote sensing and Spatial Information Sciences, Vol. 35, part B2, pp.434-439.
Metternicht, G., 1999. Change detection assessment using fuzzy sets and remotely sensed data: an application of toptgraphic map revision, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 54, pp.221-233.
Murakami, H., Nakagawa, K., Hasegawa, H., and Shibata, T., 1999. Change Detection of Buildings Using an Airborne Laser Scanner, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 54, pp.148-152.
Rottensteiner, F., Trinder, J., Clode, S., and Kubik, K., 2003. Building Detection Using LIDAR Data and Multi-spectral Images, in: Proc. APRS Conference on Digital Image Computing: Techniques and Applications, DICTA, Sydney, December 2003, vol. 2, pp.673-682.
Rottensteiner, F., Trinder, J., Clode, S., and Kubik, K., 2007. Building detection by fusion of airborne laser scanner data and multi-spectral images: Performance evaluation and sensitivity analysis, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 62, pp.135-149.
Rowe, N.C. and Grewe, L.L., 2001. Change Detection for Linear Features in Aerial Photographs Using Edge-Finding, IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 7, pp.1608-1612.
Siebe, E., and Buning, U., 1997. Application of Digital Photogrammetric Products for Cellnlar Radio Network Planning, Proceedings of the Photogrammetric Week, Karlsruhe, pp.159-164.
Skrlatos, D., 1999. Orthophotograph Production in Urban Areas, Photogrammtric Record, Vol. 16, No. 94, pp.643-650.
Teo, T.A., Chen, L.C., Rau, J.Y., and Chen, S.J., 2007, Building Reconstruction using a Split-Shape-Merge Method, Asian Journal of Geoinformatics, Vol. 7, No. 3, pp.31-34.
Tuong, T.V., Matsuoka, M., and Yamazaki, F., 2004. LIDAR-based Change Detection of Buildings in Dense Urban Areas, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Vol. 5, pp.3413-3416.
Tuong, T.V., Matsuoka, M., and Yamazaki, F., 2004. LiDAR Signatures to Update Japanese Building Inventory Database, 25th Asian Conference on Remote Sensing.
Volz, S.and D. Klinec, 1999, Nexus: the Development of a Platform for Location Aware Application, Proceedings of the third Turkish-German Joint Geodetic Days, Vol. 2, Istanbul, pp.599-608.
Vogtle, T. and Steinle, E., 2004. Detection and Recognition of Changes in Building Geometry Derived from Multitemporal Laserscanning Data, International Archives of Photogrammetry and Remote Sensing, Vol. 35, part B2, pp.428-433.
Vu, T.T., Matsuoka, M., Yamazaki, F., 2004. LIDAR-based Change Detection of Buildings in Dense Urban Areas, IEEE International Geoscience and Remote Sensing Symposium, Vol. 5, pp.3413-3416.
Vu, T.T., Matsuoka, M., Yamazaki, F., 2004. LiDAR Signatures to Update Japanese Building Inventory Database, 25th Asian Conference on Remote Sensing.
Walter V., 2004. Object-based classification of remote sensing data for change detection, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 58, pp.225-238.
Walter V., 2004. Object-based Evaluation of LIDAR and Multispectral Data for Automatic Change Detection in GIS Databases, International Archives of Photogrammetry and Remote Sensing, Vol. 35, part B2, pp.723-728.
Xia, Z., and Z. Qing, 2004. Applications of 3D city models based spatial analysis to urban, Proceedings of the ISPRS XXth Congress, 12-23 July 2004, Istanbul, Turkey, ISPRS Workshop Commission III, unpaginated CD-ROM.