An object-oriented approach for analysing and characterizing urban landscape at the parcel level

This paper presents an object-oriented approach for analysing and characterizing the urban landscape structure at the parcel level using high-resolution digital aerial imagery and LIght Detection and Ranging (LIDAR) data. Additional spatial datasets including property parcel boundaries and building footprints were used to both facilitate object segmentation and obtain greater classification accuracy. The study area is the Gwynns Falls watershed, which includes portions of Baltimore City and Baltimore County, MD. A three-level hierarchical network of image objects was generated, and objects were classified. At the two lower levels, objects were classified into five classes, building, pavement, bare soil, fine textured vegetation and coarse textured vegetation, respectively. The object-oriented classification approach proved to be effective for urban land cover classification. The overall accuracy of the classification was 92.3%, and the overall Kappa statistic was 0.899. Land cover proportions as well as vegetation characteristics were then summarized by property parcel. This exercise resulted in a knowledge base of rules for urban land cover classification, which could potentially be applied to other urban areas.