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..
Terrain Diffusion Network: Climatic-Aware Terrain Generation with Geological Sketch Guidance
Authors: Zexin Hu, Kun Hu, Clinton Mo, Lei Pan, Zhiyong Wang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on a new dataset constructed from NASA Topology Images clearly demonstrate the effectiveness of our proposed method, achieving the state-of-the-art performance. |
| Researcher Affiliation | Academia | 1The University of Sydney 2Civil Aviation Flight University of China |
| Pseudocode | No | The paper describes its methodology in narrative text and through a diagram (Fig. 2) but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/TDNResearch/TDN. |
| Open Datasets | Yes | A new dataset is constructed from NASA Topology Images (Allen 2005) to evaluate the effectiveness of the proposed TDN. |
| Dataset Splits | Yes | We obtained 10,446 samples for training and 2,611 for testing. |
| Hardware Specification | Yes | We used one NVIDIA RTX 3090 GPU card to train our model. |
| Software Dependencies | No | The paper mentions 'Pysheds (Bartos 2020) packages' but does not provide specific version numbers for this or any other software libraries or dependencies required to replicate the experiments. |
| Experiment Setup | Yes | The U-Net like synthesisers in TDN comprise 3 downsampling blocks, 3 upsampling blocks, and a middle block with 8-head selfattention mechanisms employed for embedding the diffusion step t. With a total of 1.216 billion parameters, the model s training is conducted with a learning rate of 1.0e05 and a batch size of 6. During inference, TDN takes a set of user sketches as the input and iteratively generates a noise-free terrain latent representation. In total, 36 steps are taken to derive the final latent representation. |