As the availability of geospatial data increases, there is a growing need to match these datasets together. However, since these datasets often vary in their origins and spatial accuracy, they frequently do not correspond well to each other, which create multiple problems. To accurately align with imagery, analysts currently either: 1) manually move the vectors, 2) perform a labor-intensive spatial registration of vectors to imagery, 3) move imagery to vectors, or 4) redigitize the vectors from scratch and transfer the attributes. All of these are time consuming and labor-intensive operations. Automated matching and fusing vector datasets has been a subject of research for years, and strides are being made. However, much less has been done with matching or fusing vector and raster data. While there are initial forays into this research area, the approaches are not robust. The objective of this work is to design and build robust software called MapSnap to conflate vector and image data in an automated/semi-automated manner. This paper reports the status of the MapSnap project that includes: (i) the overall algorithmic approach and system architecture, (ii) a tiling approach to deal with large datasets to tune MapSnap parameters, (iii) time comparison of MapSnap with re-digitizing the vectors from scratch and transfer the attributes, and (iv) accuracy comparison of MapSnap with manual adjustment of vectors. The paper concludes with the discussion of future work including addressing the general problem of continuous and rapid updating vector data, and fusing vector data with other data
