SiteCity: a semi-automated site modelling system

This paper presents SiteCity, a semi-automated building extraction system integrating photogrammetry, geometric constraints and image understanding algorithms. Existing automated building extraction systems produce mixed results and it is clear that human intervention is required to correct mistakes from fully automated systems. SiteCity gives human operators the ability to construct and manipulate three dimensional building objects using multiple images. Image understanding algorithms are integrated into SiteCity to assist users. The automated processes in SiteCity use user-delineated roof boundaries as cues, and attempt to locate the floor of a building and match the building object in other images. In addition, photogrammetric cues are used to assist automated processes. These automated processes are described and their performance is evaluated, illustrating that automated processes in SiteCity produce comparable performance to that of human subjects.

[1]  Z. Aviad.  Locating corners in noisy curves by delineating imperfect sequences , 1988 .

[2]  J. Chris McGlone.  Bundle adjustment with object space geometric constraints for site modeling , 1995, Defense, Security, and Sensing.

[3]  Edward M. Mikhail,et al.  Observations And Least Squares , 1983 .

[4]  Yuan C. Hsieh,et al.  Performance analysis of object-space matching for building extraction using several images , 1995, Defense, Security, and Sensing.

[5]  Christian Heipke.  State-of-the-art digital photogrammetric workstations for topographic applications , 1993, Defense, Security, and Sensing.

[6]  Theodosios Pavlidis,et al.  Use of Shadows for Extracting Buildings in Aerial Images , 1990, Comput. Vis. Graph. Image Process..

[7]  Gunilla Borgefors,et al.  Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  John E Unruh,et al.  Mensuration Test using Digital Images , 1980 .

[9]  J. Chris McGlone,et al.  Projective and object space geometry for monocular building extraction , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[10]  J. Chris McGlone,et al.  Integration of photogrammetric cues into cartographic feature extraction , 1993, Defense, Security, and Sensing.

[11]  Pascal Fua,et al.  Objective functions for feature discrimination: applications to semiautomated and automated feature extraction , 1989 .

[12]  J. Trinder,et al.  Precision of digital target location , 1989 .

[13]  R. Bruce Irvin,et al.  Methods For Exploiting The Relationship Between Buildings And Their Shadows In Aerial Imagery , 1989, Photonics West - Lasers and Applications in Science and Engineering.

[14]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  David I. Havelock,et al.  Geometric Precision in Noise-Free Digital Images , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Walter J. Mueller,et al.  Model-based feature extraction , 1993, Defense, Security, and Sensing.

[17]  Ramakant Nevatia,et al.  Detection of buildings using perceptual grouping and shadows , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Michel Roux,et al.  Feature matching for building extraction from multiple views , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[19]  J. Jansa,et al.  An assessment of the precision and accuracy of methods of digital target location , 1995 .