SEMANTIC SEGMENTATION OF BURNED AREAS IN SATELLITE IMAGES USING A U-NET-BASED CONVOLUTIONAL NEURAL NETWORK

21 citations as recorded by crossref.

1. BA_EnCaps: Dense Capsule Architecture for Thermal Scrutiny Q. Safder et al. https://doi.org/10.1109/TGRS.2022.3166352
2. Supervised Machine Learning Approaches on Multispectral Remote Sensing Data for a Combined Detection of Fire and Burned Area J. Florath & S. Keller https://doi.org/10.3390/rs14030657
3. Forest Burned Area Detection Using a Novel Spectral Index Based on Multi-Objective Optimization B. Wu et al. https://doi.org/10.3390/f13111787
4. Burned Area Detection in the Eastern Canadian Boreal Forest Using a Multi-Layer Perceptron and MODIS-Derived Features H. Meimand et al. https://doi.org/10.3390/rs17132162
5. TranSegNet: A Hybrid Transformer Model for Satellite Imagery Segmentation with Performance Benchmark Against U-Net Variants R. Ingole et al. https://doi.org/10.1016/j.procs.2025.04.310
6. A repeatable change detection approach to map extreme storm-related damages caused by intense surface runoff based on optical and SAR remote sensing: Evidence from three case studies in the South of France A. Cerbelaud et al. https://doi.org/10.1016/j.isprsjprs.2021.10.013
7. National Disaster Management and Monitoring Using Satellite Remote Sensing and Geo-Information J. Park et al. https://doi.org/10.7780/kjrs.2024.40.5.2.9
8. SSU-Net: A Model for Mapping Burned Areas Using Landsat-8 and Sentinel-2 S. Cui et al. https://doi.org/10.1109/TGRS.2024.3457543
9. Correlation Between Visible Landscape Elements and Locals’ Spatial Preference in Historical District Public Open Spaces via VR-Based EEG Measurement: A Case Study of Quanzhou Z. Zhang et al. https://doi.org/10.3390/buildings16040764
10. Burnt-Net: Wildfire burned area mapping with single post-fire Sentinel-2 data and deep learning morphological neural network S. Seydi et al. https://doi.org/10.1016/j.ecolind.2022.108999
11. Magnifier: A Multigrained Neural Network-Based Architecture for Burned Area Delineation D. Cambrin et al. https://doi.org/10.1109/JSTARS.2025.3565819
12. Semantic Segmentation of Satellite Images using 2 various U-Net Architectures: A Comparison Study N. Gonthina & . L.V. Narasimha Prasad https://doi.org/10.22399/ijcesen.1360
13. Unification of Semantic and Instance Segmentation with BoundaryX T. Boyadzhiev & K. Ivanova https://doi.org/10.2478/cait-2025-0022
14. Burned Area Mapping Using Unitemporal PlanetScope Imagery With a Deep Learning Based Approach A. Cho et al. https://doi.org/10.1109/JSTARS.2022.3225070
15. A Quadratic Morphological Deep Neural Network Fusing Radar and Optical Data for the Mapping of Burned Areas S. Seydi et al. https://doi.org/10.1109/JSTARS.2022.3175452
16. BASNet: Burned Area Segmentation Network for Real-Time Detection of Damage Maps in Remote Sensing Images W. Bo et al. https://doi.org/10.1109/TGRS.2022.3197647
17. Mapping Pluvial Flood-Induced Damages with Multi-Sensor Optical Remote Sensing: A Transferable Approach A. Cerbelaud et al. https://doi.org/10.3390/rs15092361
18. U-Net-STN: A Novel End-to-End Lake Boundary Prediction Model L. Yin et al. https://doi.org/10.3390/land12081602
19. A Method for Mapping and Associating Burned Areas with Agricultural Practices Within the Brazilian Cerrado P. Castro Abreu et al. https://doi.org/10.3390/fire8080320
20. Deep Learning Approaches for Wildland Fires Using Satellite Remote Sensing Data: Detection, Mapping, and Prediction R. Ghali & M. Akhloufi https://doi.org/10.3390/fire6050192
21. FBA-DPAttResU-Net: Forest burned area detection using a novel end-to-end dual-path attention residual-based U-Net from post-fire Sentinel-1 and Sentinel-2 images E. Khankeshizadeh et al. https://doi.org/10.1016/j.ecolind.2024.112589