## 3 Experiments and Results
### Sketch-Text Dataset  
To address the challenge of generating the appearance of high-rise buildings, we required a dataset containing paired high-rise building sketches and the corresponding textual descriptions. A total of 856 images were meticulously gathered from online sources, with a distinct emphasis on the top 1000 tall buildings worldwide. The images of high-rise buildings represent real-world structures from various countries and regions, including a variety of architectural styles (such as postmodernism, modernism, and structuralism) and functional types (including commercial and cultural buildings). This diversity ensures that the model can generalize the architectural characteristics of high-rise buildings in different styles and cultural contexts. The initial preprocessing step involved resizing the images and removing their backgrounds. Subsequently, conceptual sketches were generated using the eXtended difference-of-Gaussians operator (XDoG), as shown in Figure 6. The underlying procedure of the XDoG operation can be outlined as follows [33]:  
\[g(x)=S_{\sigma,k,p}(\mathbf{x})*I(\mathbf{x}) \tag{22}\]  
\[S_{\sigma,k,p}(\mathbf{x})=(1+p)G_{\sigma}(\mathbf{x})-pG_{k\sigma}(\mathbf{x}) \tag{23}\]  
\[G_{\sigma}(\mathbf{x})=\frac{1}{2\pi\sigma^{2}}\text{exp}\left(-\frac{\|\mathbf{x}\|^ {2}}{2\sigma^{2}}\right) \tag{24}\]  
where \(I(\mathbf{x}),\mathbf{x}\), and \(g(\mathbf{x})\) denote the original image, a two-dimensional coordinate, and the filtered sketch generated by the XDoG operator, respectively; \(S_{\sigma,k,p}(\mathbf{x})\) is the reparameterization of the XDoG filter for simplified control; and \(\sigma,k,p\) are the control parameters.  
Textual descriptions included architectural style (e.g., modernism, futurism, or post-modernism), high-rise type (e.g., high-rise or tower), architectural design manner (e.g., symmetrical, misaligned, or segmented), architectural structure (e.g., podium or revolving floor), and other common design concepts to denote the abstract design concepts. To mitigate potential overfitting, we leveraged the pre-trained latent stable diffusion model and ChatGPT 3.5 from Open AI to augment each sketch-text pair, as shown in Figure 7. For image augmentation using the pre-trained diffusion model, we leveraged the image-to-image functionality of _stable-diffusion-v1-5_, adjusting the output correlation coefficient to preserve the overall structure while modifying only the finer details, resulting in 32 variations per sketch. For text augmentation with ChatGPT, we rephrased each textual description into 10 semantically equivalent but differently phrased versions. During this process, we ensured that the rephrased sentences maintained the original semantics by applying the following established conditions, including using synonyms, reordering words, and adjusting the phrasing, aiming to generate diverse expressions with the same semantic content. Subsequently, we conducted manual evaluations to ensure that each of the 10 augmented text prompts for a sketch preserved the original meaning.  
Figure 6: Reshaping, background removal, and sketch conversion of high-rise building images using the XDoG operator.  
Figure 7: Data augmentation using the pre-trained latent stable diffusion model and ChatGPT.