METHODOLOGY AND TOOLS FOR ALIGNING VECTOR AND IMAGE DATA

Present methods of vector-raster conflation require extensive human intervention and labor, which is an expensive effort better spent in cognitive, decision-making tasks. Integration or alignment of vector and raster geospatial data may have inaccurate and contradictory geo-references, or not have them at all. Different and unknown rotations, disproportional scales, uncontrolled noise, various physical modalities, and other factors are fundamental challenges for robust alignment of such data. This is an area of extensive research and development, but commercial software products that can meet the current challenges do not yet exist. Our Phase I research has shown that automated/semi-automated vector–raster conflation is feasible. Our focus on automation is to significantly reduce the manual steps and time necessary to align vector and image data. We are already in contact with probable users of conflation technology and are engaging potential investors and partners. Our commercialization/transition approach is based upon a working relationship with Visual Learning Systems, Inc., which is a worldwide leader in automated feature extraction solutions within the GIS industry. The technical goal and envisioned outcome of this Phase II project is an initial version of a commercial conflation tool that can be moved rapidly into the market through Phase III efforts.