Local governments frequently use parcel maps or other spatial data for decision-making, but much of this data is often inaccurate and outdated, producing a negative influence on the expected outcome. Remote sensing can provide accurate and current high-resolution imagery. However, one major bottleneck to the integration of high-resolution imagery into existing geographic information systems is the issue of positional accuracy of the existing line-work within the vector Gis database, making it difficult to match with imagery. This paper presents a vector-to-imagery conflation approach to improve the positional accuracy of digital parcel maps by conflating the vector parcel maps to make it consistent with high-resolution imagery. Road intersections are automatically extracted from imagery and used as control points. A relaxation labeling algorithm is used to find the matches between the two road intersection point sets from vector and imagery. The links are created from the matched point pairs and are used to perform a piecewise transformation. The test results show that this approach can improve the accuracy of vector parcel maps significantly. It is a very cost-effective method and has great potential to save considerable time and money for localgovernments to upgrade their inaccurate vector datasets.
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