Comparison Study of Three Building Regularization Algorithms

Building outline generation and regularization is an ongoing topic in remote sensing applications. The success of the methods used for building outline detection impacts studies that depend on the accuracy of the methods applied, such as urban planning, geospatial analysis, and 3D city modeling. The results of the building outline detection methods can vary due to several factors, such as the area to which they are applied and/or the parameters used for the methods. Since there are well-established deep-learning based software and plugins for building outlining, this paper compares the results of such a method, namely an open-source AI implementation (Mapflow), with the standard non-deep-learning based tools introduced by (Mousa et al., 2019) and (Bulatov et al., 2014). We present a comparative analysis of these two methods for regularizing building outlines in terms of accuracy, efficiency, and robustness in dealing with different levels of buildings’ complexity and structures. While the results of (Mousa et al., 2019) and (Bulatov et al., 2014) are comparable and outperform the AI method, (Mousa et al., 2019) is the method least impacted by the different parameters, however, has a higher computing time.