Automatic building information model reconstruction in high-density urban areas: Augmenting multi-source data with architectural knowledge

Comparison of Digital Building Height Models Extracted from AW3D, TanDEM-X, ASTER, and SRTM Digital Surface Models over Yangon City

“…Of these technologies, airborne laser scanning (ALS) has the highest accuracy in parameterizing building morphology, ranging from simple footprint identification [17] to complicated 3D structure and roof plane modeling [14,18]. State-of-the-art ALS approaches have also achieved very high accuracy in complex urban environments by integrating aerial imagery [19], city administrative data [20], architectural knowledge [21], and the Big Data approach [22].Despite these promising results, there have been relatively few published studies on such methods being applied to large areas [23]. Furthermore, ALS data sources and aerial images are often under the control of government ministries, and, due to high operational costs, they are not available in many parts of the world [24].…”

“…Of these technologies, airborne laser scanning (ALS) has the highest accuracy in parameterizing building morphology, ranging from simple footprint identification [17] to complicated 3D structure and roof plane modeling [14,18]. State-of-the-art ALS approaches have also achieved very high accuracy in complex urban environments by integrating aerial imagery [19], city administrative data [20], architectural knowledge [21], and the Big Data approach [22].…”

From Semantic Segmentation to Semantic Registration: Derivative-Free Optimization–Based Approach for Automatic Generation of Semantically Rich As-Built Building Information Models from 3D Point Clouds

“…Nevertheless, these data-driven and model-driven types of approaches (Volk et al, 2014) both rely heavily on semantic segmentation requiring geometry-to-label correlation models (e.g., based on rules or machine learning models). Examples of rule-based models include random sample consensus (RANSAC) for planar components (Schnabel et al, 2007;Lagüela et al, 2013;Jung et al, 2014), parametric curved surface components (Dimitrov et al, 2016), geometric simplification such as voxelization (Aijazi et al, 2013;Zhu et al, 2017), and asserted simple geometry (e.g., indoor boundary and rooftop primitives) from intersection of planes (Valero et al, 2012;Chen et al, 2018b). Some approaches have employed machine learning techniques for more complicated correlation models, like support vector machines (SVM) (Adan and Huber, 2011;Koppula et al, 2011;Perez-Perez et al, 2016;Wang et al, 2017) or convolutional neural networks (Babacan et al, 2017).…”

“…Conventional approaches rely on 'semantic segmentation', which is a computer vision or graphics technology that assigns each 3D point, or 2D pixel, to one of several semantic labels (Shamir, 2008;Vezhnevets and Buhmann, 2010) via a priori rules-enabled reasoning models (e.g., Valero et al, 2012;Chen et al, 2018b), and training example-enabled machine learning models like deep learning (e.g., Babacan et al, 2017;Zou et al, 2017). Although these approaches have achieved acceptable results on simple and regularly shaped components such as walls, windows, pipelines, and boxes of internal walls (e.g., Valero et al, 2012;Babacan et al, 2017;Nguyen and Choi, 2018;Zou et al, 2018), they have yet to satisfactorily deal with complex scenes such as furniture, irregularly shaped components, and non-geometric information (e.g., Koppula et al, 2011;Wang et al, 2018).…”

Testing and Application of Geospatial Techniques in Seismic Engineering

“…With this data source, it is possible to create three-dimensional models of buildings, known as Building Information Models (BIMs). Through this line of research, it is important to mention the following publications (among others): Chen et al, 2018 [9] and Pətrəucean et al, 2015 [10].…”