TY - JOUR A1 - Luft, Harrison A1 - Schillaci, Calogero A1 - Ceccherini, Guido A1 - Vieira, Diana A1 - Lipani, Aldo Y1 - 2022/10// UR - https://doi.org/10.3390/fire5050163 N1 - This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ID - discovery10157975 N2 - The study presented here builds on previous synthetic aperture radar (SAR) burnt area estimation models and presents the first U-Net (a convolutional network architecture for fast and precise segmentation of images) combined with ResNet50 (Residual Networks used as a backbone for many computer vision tasks) encoder architecture used with SAR, Digital Elevation Model, and land cover data for burnt area mapping in near-real time. The Santa Cruz Mountains Lightning Complex (CZU) was one of the most destructive fires in state history. The results showed a maximum burnt area segmentation F1-Score of 0.671 in the CZU, which outperforms current models estimating burnt area with SAR data for the specific event studied models in the literature, with an F1-Score of 0.667. The framework presented here has the potential to be applied on a near real-time basis, which could allow land monitoring as the frequency of data capture improves. AV - public KW - deep learning; fire mapping; synthetic aperture radar; land cover; ResNet IS - 5 PB - MDPI AG VL - 5 SN - 2571-6255 TI - Deep Learning Based Burnt Area Mapping Using Sentinel 1 for the Santa Cruz Mountains Lightning Complex (CZU) and Creek Fires 2020 JF - Fire ER -