## 2 Literature Reviews
### Implementation of the Framework
#### 3.2.1 Implementation of Extracting Vector Information System  
The vector information extraction system implemented the process associated with Figure 2's Activity A1. A system for extracting vector information from sketch data, used to create wall, floor, and roof objects in a BIM model, was implemented using the Python programming language and the Pytorch deep learning framework. Table 1 displays the input data for the vector information extraction system and the data output through the applied mechanisms. Figure 2: The workflow of the framework, which transforms sketched data into a BIM model and conducts building performance evaluations. A perspective transformation application was developed to eliminate unnecessary information, such as the desk surface outside the paper boundary in sketch images, that is not part of the sketch strokes. This application utilized perspective transformation functions and functions for accessing and editing image files provided by the OpenCV library. The perspective transformation application allows users to select four corner points of the paper boundary in a clockwise direction within the sketch image. Following selection, the area defined by these corner points is saved as an image with a predefined resolution (width: 2016 pixels, height: 1512 pixels). It also offers functionality for users to select the area (four corner points) for perspective transformation application through a left mouse click. Table 1's column, \"Remove noises from outside the sheet\", illustrates the removal of unnecessary information from outside the paper boundary in the sketch image through the perspective transformation application. A sketch information recognition model was developed to eliminate extraneous information, such as shadows, reflections, and paper texture, within the sketch image that is not part of the sketch strokes. The development of the sketch information recognition model utilized the skip connection, encoding, and decoding architecture of Unet++[20], with individual Convolution units of Unet++ implemented as Residual units from ResUnet[21]. The sketch information recognition model was developed using ResUnet architecture, originally designed for road detection in remote sensing images due to its effective binary classification with minimal training data. This study leveraged the morphological parallels between roads in remote sensing and sketch strokes, alongside the similar binary classification challenge of differentiating sketch strokes from non-stroke elements. Thus, a deep learning model based on ResUnet was created to accurately identify sketch strokes in images. The architecture of Unet++ was employed to accurately identify pixels corre \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Step** & \begin{tabular}{c} **(1) Remove Noises** \\ **from Outside in the** \\ **Sheet** \\ \end{tabular} & \begin{tabular}{c} **(2) Remove Noises** \\ **from Inside in the** \\ **Sheet** \\ \end{tabular} & \
\begin{tabular}{c} **(3) Retrieve Center** \\ **Lines** \\ \end{tabular} &
\begin{tabular}{c} **(4) Split the Image** \\ **(5) Retrieve Vector Data** \\ \end{tabular} \\ \hline \multirow{4}{*}{Input} & Sketch image & Pre-processed image & Wall image & Pre-processed image & Center line image \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the vector data retrieval system. sponding to sketch strokes in sketch images, while the Residual units of ResUnet were used to facilitate model training with a limited amount of training data. The combination of both models allowed for the implementation of the sketch information recognition model capable of high-accuracy recognition of sketch information with limited training data. The model was implemented with an encoding and decoding structure of Unet++ composed of 15 Residual units.