This Small Business Innovation Research Phase I project will conduct a feasibility study to demonstrate that by combining currently available high-resolution imagery, geospatial data (e.g., parcel data or structure data), and other related online data sources (e.g., property tax data or census data), it is possible to automatically generate highly targeted direct marketing leads for a variety of markets. The plan is to approach this problem by (1) aligning existing geospatial sources with the high-resolution imagery in order to determine the exact location and determine the address of the parcels seen in the imagery, (2) extracting the relevant features from the imagery to provide appropriate leads, such as determining the presence or absence of a swimming pool, the type of roofing materials used, or what types of cars are parked in the driveway, and (3) bringing in other sources of data, such as property tax assessment data to provide additional context. The primary focus of the phase I project will be to demonstrate the use of machine learning technology for identifying features in high-resolution imagery that can be used for direct marketing. High-resolution aerial imagery is now being widely collected and is available for low cost or in some cases is even free. The challenges are to first to align parcel data with the high resolution imagery to identify the exact address and boundaries of a property, and second to develop feature extraction techniques that can exploit the contextual information to accurately identify novel features, such as roofs, cars, pools, landscaping, etc., that can be used for direct marketing. The ability to accurately identify features in imagery and then relate them to specific properties as well as related sources of information will allow a targeted direct marketing product to be built. The end users of this product will be companies seeking to market products directly to residential consumers. This includes product and services relating to home improvement, both exterior and interior, as well as those products relating to residents of the home, that can be gleaned from imagery available for the parcel in question. This is a large market and includes everyone from home improvement stores to roofing companies, construction companies, automobile dealers, tree trimmers, landscapers, and pool construction companies. Beyond direct marketing, the technology can also be used for other applications that combine imagery, geospatial data, and structured information. For example, it could used for mosquito abatement, which is important to stop the spread of West Nile Virus, by identifying large pools of stagnant water, associating those hazards with the appropriate address, and then mailing abatement notifications to the residents
