Performance of shape indices and classification schemes for characterising perceptual shape complexity of building footprints in GIS

ABSTRACT Shape characterisation is important in many fields dealing with spatial data. For this purpose, numerous shape analysis and recognition methods with different degrees of complexity have so far been developed. Among them, relatively simple indices are widely used in spatial applications, but their performance has not been investigated sufficiently, particularly for building footprints (BFs). Therefore, this article focuses on BF shape characterisation with shape indices and classification schemes in a GIS environment. This study consists of four phases. In the first phase, the criteria for BF shape complexity were identified, and accordingly, benchmark data was constructed by human experts in three shape complexity categories. In the second phase, 18 shape indices were selected from the literature and automatically computed in GIS. The performance of these indices was then statistically assessed with histograms, correlation matrix and boxplots, and consequently four indices were found to be appropriate for further investigation. In the third phase, two new indices (Equivalent Rectangular index and Roughness index) were proposed with the objective to measure some BF shape characteristics more efficiently. The proposed indices also were found to be appropriate with the same statistical assessment procedures. In the final phase, BF shape complexity categories were created with the pairs of six appropriate indices and four choropleth mapping classification schemes (equal intervals, natural break, standard deviation, and custom) in GIS. The performance of the index–scheme pairs was assessed against the benchmark data. The findings demonstrated that both new indices and two of the selected indices (Convexity and Rectangularity) delivered higher performance. The custom classification scheme was found more ideal to reveal absolute shape complexity with the index value ranges derived from the boxplots while the other classification schemes were more appropriate to reveal relative shape complexity.

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