The present disclosure describes systems and methods that use available imagery of a building or other structure in combination with one or more machine learning techniques or machine-learned models to assist in estimating risk associated with the building. The systems and methods described herein can also be adapted to predict failure risk of various types of infrastructure (e.g., utility infrastructure such as power lines and power line poles) and/or industrial assets. Risk assessment can be useful for various objectives including assessment for insurance purposes, valuation purposes, etc. In one example, one or more machine learning models can be used to identify attributes of a building for use with standard actuarial tables based on imagery of the building. In another example, machine-learned models can be used to directly estimate risk based on imagery of the building. Keywords associated with the present disclosure include: machine learning; neural network; model; training; image; imagery; satellite; aerial; insurance; risk estimate; risk estimation; cost estimation; building; structure; roof; and attributes.
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Greene, Michael, "ESTIMATING BUILDING RISK USING IMAGERY AND MACHINE LEARNING", Technical Disclosure Commons, (October 05, 2016)