Featured Dataset
DLSR-FireCNet: A deep learning framework for burned area mapping based on decision level super-resolution
Associated Publication: Seydi, S.T. & Sadegh, M. (2025). DLSR-FireCNet: A deep learning framework for burned area mapping based on decision level super-resolution. Remote Sensing Applications: Society and Environment, 37, 101513. <a href="https://doi.org/10.1016/j.rs
File Size3.2 GB
Data TypeDataset
Published2026
Licensecc-by-4.0
Total Views78
Total Downloads80
Associated Publication: Seydi, S.T. & Sadegh, M. (2025). DLSR-FireCNet: A deep learning framework for burned area mapping based on decision level super-resolution. Remote Sensing Applications: Society and Environment, 37, 101513. https://doi.org/10.1016/j.rsase.2025.101513
Input Features
- Source: MODIS surface reflectance product
- Spectral bands used: Red (Band 1) and Near-Infrared / NIR (Band 2)
- Native input resolution: 250 m
- Image structure: Bi-temporal pairs — one pre-fire image and one post-fire image per event
- Architecture target: The model is trained to produce burned area maps at 30 m effective resolution via decision-level super-resolution
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Files are hosted on the source repository. Click download to access the full dataset.
Seydi, Seyd Teymoor (2026). DLSR-FireCNet: A deep learning framework for burned area mapping based on decision level super-resolution. https://doi.org/10.5281/zenodo.19403571