Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |