Urban Structure Changes in Three Areas of Detroit, Michigan (2014–2018) Utilizing Geographic Object-Based Classification

Abstract

The following study utilized geographic object-based image analysis methods to detect pervious and impervious landcover with respect to residential structure changes. The datasets consist of freely available very high-resolution orthophotos acquired under the United States National Agriculture Imagery Program. Over the last several decades, cities in America’s Rust Belt region have experienced population and economic declines—most notably, the city of Detroit. With increased property vacancies, many residential structures are abandoned and left vulnerable to degradation. In many cases, one of the answers is to demolish the structure, leaving a physical, permanent change to the urban fabric. This study investigates the performance of object-based classification in segmenting and classifying orthophotos across three neighbourhoods (Crary/St. Mary, Core City, Pulaski) with different demolition rates within Detroit. The research successfully generated the distinction between pervious and impervious land cover and linked those to parcel lot administrative boundaries within the city of Detroit. Successful detection rates of residential parcels containing structures ranged from a low of 63.99% to a high of 92.64%. Overall, if there were more empty residential parcels, the detection method performed better. Pervious and impervious overall classification accuracy for the 2018 and 2014 imagery was 98.333% (kappa 0.966) with some slight variance in the producers and users statistics for each year.