# Scan to BIM Mapping Process Description for Building Representation in 3D GIS
## 3 Related Works
There has been a study on building model extraction using ALS data [11]. A Leica CityMapper was used for data collection. This study proposed a method for creating a building shape by extracting the roof contour using the CloudCompare tool and generating a poly surface model using Rhino. To utilize IFC (Industry Foundation Classes) when creating a GIS information model, there has been a study on the geographic coordinate reference method and calculations [12]. This study describes a technique for mapping the local coordinate system of IFC elements to the GIS coordinate system. There was a study on constructing a 3D city model from satellite images [13]. This study proposed extracting building segments using the SSD (Single Shot Detector) deep learning model implemented in the ESRI ArcGIS Pro tool and extracting the building height using the DSM (Digital Surface Model). A study analyzed the CityJSON model's structure to review the 3D spatial data model [14]. In this study, the data structure and exchange method of CityJSON were examined, and the feasibility of the application was evaluated. There was a study on effectively creating BIM information in GIS [15]. This study presented a method for automatically generating building LoD using IFC files. There was a study on analyzing building renovation projects using the Ready2 BIM tool [16]. This tool supports the BIM modeling process using 3D scan data. Ready2 BIM was developed with support from EUREKA and enables web-based IFC and point cloud data viewing. A study proposed a building area classification method for LiDAR point clouds [17]. In this study, the problem of estimating the building area was pointed out, which often includes unrelated objects other than the roof, causing errors in height estimations. In this study, the buildings were classified using the Random Forest (RF) classifier algorithm, and the difference between height and actual height was confirmed through sample data from one building. There was a study on data acquisition for urban building energy modeling [18]. This study presented a methodology for acquiring and modeling the dataset required for building energy modeling. There was a study on extracting dense urban buildings from LiDAR point clouds [19]. This study proposed a methodology to acquire the building area after creating a Digital Surface Model (DSM) from aerial photographs and LiDAR point clouds. One study presented a modeling methodology for historical buildings [20]. This study investigated the challenges of modeling historic buildings in Malaysia and proposed a method for modeling from scanned point cloud data using ESRI software. There was a study on the methodology to connect a 3D point cloud and CityGML model [21]. This study proposed a method for generating point clouds from CityGML shapes for digital twin model development. Point cloud data can be used in various fields. A study investigated the automation method for BIM-GIS integration [22]. This study identified the technique required for BIM-GIS integrated automation from 3D scan point cloud data to modeling. One study proposed a modeling method that can lighten scanned point cloud data for path estimations of an unmanned aerial vehicle such as a drone [23]. This study suggested a method for extracting a bounding box from point cloud data and presented the data compression rate through it. A study proposed a method for generating a 3D tree model from a LiDAR point cloud [24]. In this study, the roof part was filtered from the point cloud, and the tree area was extracted using the DSM generated from the rest of the point cloud. A study proposed Scan to BIM technology for digital data management and expression [25]. This study presented the use of Rhino and ArcScene software for Scan to BIM. One study proposed a method of extracting ground from a 3D point cloud [26]. In this study, a ground extraction method using ArcGIS was proposed. There was a review study on integrating GIS and BIM required for developing a digital twin model for smart city design [27].