A novel theoretical accuracy evaluation method of automatic mapping: a case study of automatic mapping accuracy evaluation of buildings

Intelligent mapping technology has become more popular in the era of AI. However, traditional mapping accuracy evaluation methods, algebraic geometric methods (Hausdorff distance), and AI accuracy evaluation indicators based on IOU and its derivative methods, due to theoretical design flaws, are all unable to accurately calculate the true error values of the automated mapping results. Therefore, this paper proposes a theoretical accuracy evaluation method for intelligent mapping results based on automatic contour matching and Riemann integration. Firstly, we calculated the inflection-point matching relationship between the mapping contour and the real reference contour based on the proposed contour matching method. Next, using the inflection point matching relationship between the two contours, the two vector contours are divided into several groups of mutually matching edges. The distances between the matching edges were calculated using the proposed calculation method, and the weighted average distance of these distances based on the lengths of the matching edges was considered as the error of the mapping contour. Experiments were conducted on an ideal dataset and two groups of mapping-contour data generated by different automatic mapping algorithms. The experiments show that the accuracy evaluation effect of the proposed method is significantly better than that of the existing mapping accuracy evaluation methods.