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..
EvaNet: Elevation-Guided Flood Extent Mapping on Earth Imagery
Authors: Mirza Tanzim Sami, Da Yan, Saugat Adhikari, Lyuheng Yuan, Jiao Han, Zhe Jiang, Jalal Khalil, Yang Zhou
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that Eva Net significantly outperforms the U-Net baselines, and works as a perfect drop-in replacement for U-Net in existing solutions to flood extent mapping. |
| Researcher Affiliation | Academia | 1Indiana University Bloomington 2University of Alabama at Birmingham 3University of Florida 4St. Cloud State University 5Auburn University |
| Pseudocode | No | The paper describes the formulations and architecture (e.g., Figure 4), but it does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Eva Net is open-sourced at https://github.com/MTSami/Eva Net. |
| Open Datasets | Yes | We obtain high-resolution aerial imagery from NOAA National Geodetic Survey during Hurricane Matthew in North Carolina (NC) in 2016 [NOAA, 2016]. The accompanied DEM data are obtained from the University of North Carolina Libraries [NCSU, 2023]... from [NOAA, 2017], and the corresponding DEM data was obtained from USGS data downloader [USGS, 2023]. |
| Dataset Splits | No | Without loss of generalization, in Table 2, we use R1 and R2 for training, and use R3, R4, R5, R6 and R7 for test. The paper does not explicitly mention a separate validation split within its data partitioning description. |
| Hardware Specification | Yes | We trained our Eva Net models and the U-Net baselines on a cluster with NVIDIA-P100 GPUs for 100 epochs with a learning rate of 1e-7 and batch size of 4. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify version numbers for PyTorch or any other software dependencies used in the experiments. |
| Experiment Setup | Yes | We trained our Eva Net models and the U-Net baselines on a cluster with NVIDIA-P100 GPUs for 100 epochs with a learning rate of 1e-7 and batch size of 4. |