Sagi Filin 
  
The algorithm described here addresses the complete process beginning with the detection of counterpart points, continuing 
with the matching of counterpart lines and concluding with the planar subdivision and the transformation of related objects. 
The results indicate a vast improvement in accuracy and indicate that the subsequent tasks will become easier to perform. 
As mentioned, the algorithm is general and can be applied for many other tasks, among which we may count change 
detection between vector data sets, integration in general of two or more vector data sets and so forth. 
REFERENCES 
Baumgartner A., Steger C., Mayer H., Eckstein W., 1997. Semantic Objects and Context for Finding Roads. Integrating 
Photogrammetric Techniques with Scene Analysis and Machine Vision III, Editors: D. M. McKeown, Jr., J.C. McGlone, O. 
Jamet, Proceedings of SPIE, Orlando, Florida (USA), Vol. 3072, S. 98-109 
De Berg M., Kraveld M., Overmars M., Schwartzkopf O., 1997. Computational Geometry, Springer. 
Doytsher Y., Gelbman E., 1995. A Rubber Sheeting Algorithm for Cadastral Maps. Journal of Surveying Engineering — 
ASCE, 121(4): 155-162. 
Filin S., Doytsher Y., 1999, Linear Approach to Map Conflation: Matching of Polylines. Surveying and Land Information 
Systems, Vol. 59(2), pp. 107-114. 
Gabay Y., Doytsher Y., 1995. Automatic Feature Correction in Merging of Line Maps. Proceedings of the 1995 
ACSM-ASPRS Annual Convention, Charlotte, North Carolina (USA), 2:404-410. 
Heipke C., Mayer H., Wiedemann C., Jamet O., 1997. Evaluation of Automatic Road Extraction. International Archives of 
Photogrammetry and Remote Sensing, International Society for Photogrammetry and Remote Sensing, Vol. 32 (3-2W3). 
Walter V., Fritsch D., 1999. Matching Spatial Data Sets: a Statistical Approach. International Journal of Geographical 
Information Science, 13(5), pp. 445-473. 
  
288 International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B3. Amsterdam 2000.