This paper proposes a framework for optimizing allocation of infrastructure spending on sidewalk improvement and allowing planners to focus their budgets on the areas in the most need. In this research, we identify curb ramps from Google Street View images using traditional machine learning and deep learning methods. Our convolutional neural network approach achieved an 83% accuracy and high level of precision when classifying curb cuts. We found that as the model received more data, the accuracy increased, which with the continued collection of crowdsourced labeling of curb cuts will increase the model’s classification power. We further investigated a model using the TensorFlow Object Detection API, to construct a model that could accurately isolate curbs and identify whether a curb contained a proper cut or not. Our Results suggest that a municipality could pinpoint areas for infrastructure spend automatically. We leave implementation and data layering as an area of exploration.
Abbott, Andrew; Deshowitz, Alex; Murray, Dennis; and Larson, Eric C.
"WalkNet: A Deep Learning Approach to Improving Sidewalk Quality and Accessibility,"
SMU Data Science Review: Vol. 1:
1, Article 7.
Available at: https://scholar.smu.edu/datasciencereview/vol1/iss1/7
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