  There has been a considerable amount of research aimed at automating the
documentation of accessibility in the built environment. Yet so far, there has
been no fully automatic system that has been shown to reliably document surface
quality barriers in the built environment in real-time. This is a mixed problem
of HCI and applied machine learning, requiring the careful use of applied
machine learning to address the real-world concern of practical documentation.
To address this challenge, we offer a framework for designing applied machine
learning approaches aimed at documenting the (in)accessibility of the built
environment. This framework is designed to take into account the real-world
picture, recognizing that the design of any accessibility documentation system
has to take into account a range of factors that are not usually considered in
machine learning research. We then apply this framework in a case study,
illustrating an approach which can obtain a f-ratio of 0.952 in the best-case
scenario.
