High-Fidelity Road Network Generation with Latent Diffusion Models
Authors: Jinming Wang, Hongkai Wen, Geyong Min, Man Luo
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on multiple datasets demonstrate the proposed Graph Walker can effectively generate high quality road networks from noisy and sparse trajectories, showcasing significant improvements over state-of-the-art. Extensive experiments conducted on diverse real-world datasets show that Graph Walker outperforms both appearance-based methods and state-of-the-art diffusion-based graph generation approaches, demonstrating significant improvements in generation accuracy and superior robustness with limited trajectory data and unusual road topologies present. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Exeter 2Department of Computer Science, University of Warwick EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods in prose and uses figures for architectural diagrams (e.g., Figure 2 and Figure 3) but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1Code at: https://github.com/Jinming Wang/Graph Walker. |
| Open Datasets | Yes | Concretely, for an area within the city specified by a bounding box of certain size, we collect its road network data using Open Street Map [Open Street Map contributors, 2017], and aerial images from Google Maps [Google, nd]. |
| Dataset Splits | No | The paper mentions using data from different cities and varying area sizes for training and testing, and varying the number of input trajectories, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts) for the synthesized trajectories. |
| Hardware Specification | Yes | measured on a single NVIDIA 4090 GPU |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used for implementation (e.g., Python, PyTorch, TensorFlow, CUDA). |
| Experiment Setup | No | The paper describes the loss functions used for training the W2G-VAE and T2W-DiT, and mentions following 'standard training procedures for diffusion models', but it does not provide specific hyperparameter values such as learning rates, batch sizes, number of epochs, or optimizer details. |