Human-Instructed Deep Hierarchical Generative Learning for Automated Urban Planning
Authors: Dongjie Wang, Lingfei Wu, Denghui Zhang, Jingbo Zhou, Leilei Sun, Yanjie Fu
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we present extensive experiments to demonstrate the effectiveness of our framework. Experiments Experimental Setup Data Description. Our research focuses on Beijing. The data collection process is as follows:... |
| Researcher Affiliation | Collaboration | 1 University of Central Florida 2 Pinterest 3 Rutgers University 4 Baidu Research 5 Beihang University |
| Pseudocode | No | The paper describes the methodology in text and equations but does not include any explicitly labeled "Pseudocode" or "Algorithm" blocks or figures. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | we first crawled 2990 residential communities from soufun.com and downloaded 328,668 POIs with 20 distinct POI categories from openstreetmap.org to construct land-use configuration samples referring to (Wang et al. 2020). Then, we collected taxi trajectories from the T-drive project (Yuan et al. 2010) and downloaded road networks and POIs from openstreetmap.org. to discover urban functional zones referring to (Yuan et al. 2014). |
| Dataset Splits | No | We randomly split the dataset into two independent sets. The prior 90% is the train set, and the remaining 10% is the test set. (No mention of a validation split). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or other library versions). |
| Experiment Setup | No | The paper states: "We provided other experimental details in the technical appendix." However, the appendix is not included in the provided text, so explicit experimental setup details are not present within the provided document. |