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 | Conference PDF | Archive PDF | Plain Text | 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. |