# Scan to BIM Mapping Process Description for Building Representation in 3D GIS
## 3 Related Works  
A literature survey was conducted with the following keywords to survey related processes and algorithms. \"Scan\", \"ALS\", \"Building\", \"Footprint\", \"BIM\", \"GIS\", \"Generation\", \"Process\", and \"Description\". Research on the parametric-based urban information modeling method has been conducted for earthquake disaster risk assessments. This study proposes a parametric  
Figure 1: Research method. methodology that supports urban information modeling through finite element analysis [3]. This study created a parametric city model using a geographic data database to create a city information model expressed in SHP (Shapefile format). This study focuses on processing parametric information using the Rhino Tool's Grasshopper add-in. A study proposed a method of generating a 2D model by automatically filtering the point cloud of a building acquired using ALS technology [4]. This study established the criteria for extracting the roof of a building based on the z-axis to create a 2D model of a building. Through this, a method of automatically generating a 2D model by extracting the exterior boundary and roof area of a building was proposed. There was a study on automatically extracting objects required for digital topographic map creation from point clouds obtained through ALS [2]. This study presented the topics of building outline denoising, edge vectorization, segmentation, and artificial intelligence-based building object extraction for the automatic generation of conventional 2D digital maps. This study mentions the discontinuity, incompleteness, and ambiguity of the point cloud. As one of the ways to improve them, the application of a deep learning model based on a Dynamic Graph Convolutional Neural Network (DGCNN) is proposed. In this study, a feature extraction module such as DGCNN for segmentation was used. This module mainly encodes the features of point cloud data to compute the meaning of the points. For reference, it is challenging to classify walls and windows only with point features in scan scene data. Therefore, deep learning models such as DGCNN encode center point and neighbor point color information in feature extractions [5]. The process of mapping building objects from ALS data presents several challenges. Among them, the method of extracting individual buildings is one factor that significantly influences the quality of the result. Research was conducted to increase instance creation accuracy through a matching process between scan data and polygons [6]. This study proposed a method to generate similar building instance footprints by extracting building roofs and assembling predefined building segments. A method of generating scan data as voxels was studied to obtain the geometric shape information required for building energy performance simulation [7]. This study used a point-projected grid system and Principal Component Analysis (PCA) to extract the wall elements necessary for an energy performance analysis. Openings such as doors were created by calculating empty grid areas under certain dimensional conditions. Plane intersection calculations fill edge gaps between walls. This study implemented and tested a shape generation technique for a single building. There was a study on CityGML LoD (Level of Detail) 3 modeling for smart city information model creation [8]. The data source used for modeling was an aerial Digital Terrain Model (DTM) image. In this study, a process using Sketchup, a commercial tool for modeling, was proposed. There was a study on extracting building footprints considering outliers and missing points in the ALS dataset [9]. This study used a Maximum Consistency within Minimum Distance (MCMD) and Random Sample Consensus (RANSAC) model to obtain the building space. A method for extracting the roof of a building from ALS data has been studied [10]. In this study, after extracting PCD seed points for the roof, a segmentation step is performed to compare the curvatures between the points to grow the roof point cloud region.