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  -