The rapid advancement of urbanization in recent decades has attracted extensive application of cellular automata (CA)-based models to simulate urban growth for planning and decision-making. However, the inaccurate representation of heterogeneous spatial interactions between urban units and the neglect of autocorrelated growth patterns in urbanization lead to unreliable simulation results of CA-based models. To address these two limitations, this study proposes a novel CA-based model integrated with Transformer network and Local Moran’s I, namely TL-CA. The Transformer network is built to quantify heterogeneous interaction between neighbors using the self-attention mechanism. Subsequently, Local Moran’s I is employed to implicitly guide the network in learning spatially autocorrelated patterns of urban growth through auxiliary learning. Finally, the development potential estimated from driving factors, i.e. the network output, is incorporated into CA to simulate urban growth. Land use data from Wuhan (2000–2020) are selected to verify TL-CA’s performance. The results demonstrate that TL-CA achieves the highest simulation accuracy, with an average increase in the figure of merit (FoM) of 9.97%. Attention visualization and residual analysis explain the model’s effectiveness in modeling heterogeneous interactions and autocorrelated growth. Additionally, TL-CA exhibits high computational efficiency and low resource consumption, with sufficient potential to support larger-scale research.

Efficient point cloud visualization is indispensable for practical applications. In the context of point cloud visualization, 3-D rendering can be viewed as the kernel that transforms 3-D points into a 2-D scene image. Compared with traditional point-based rendering (PBR), neural image-based rendering (NIBR) has gradually emerged as a feasible solution for point cloud rendering. To efficiently render sparse and colorless mobile laser scanning (MLS) point cloud, we propose a novel neural rendering framework based on deep generative learning, named MLS point cloud rendering with generative adversarial network (MPR-GAN). In this framework, perspective projection with intrinsic parameter scaling and cumulative distribution normalization is first utilized to transform the 3-D point cloud into a compact 2-D image; a conditional generative adversarial network (CGAN)-based rendering model is then proposed to generate a photorealistic scene image from the projected 2-D image. In this CGAN model, the asymmetric encoder–decoder generator can implement inpainting and true colorization using context feature capturing and edge information perception; a multiscale discriminator is built to guarantee the model output with global consistency and local details. Moreover, a hybrid loss function is designed to improve the visual quality of the generated images with similarity constraints from both the content and the structure. Two public MLS point cloud datasets are selected and employed to carry out extensive evaluation using MPR-GAN and other baseline frameworks. The experimental results demonstrate that MPR-GAN achieves the state-of-the-art rendering performance in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Furthermore, the efficiency analysis shows that MPR-GAN can support real-time rendering, achieving end-to-end rendering from raw points.