Achmad et al. (2026) Integrating CCD and Adaptive-dNBR with Metaheuristic-Optimized Hybrid Deep Learning for Wildfire Detection and Susceptibility Mapping in Los Angeles County

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Arief Rizqiyanto Achmad, Eunseok Park, 이광재, Seung-Jae Lee, Sung-Ho Chae, Hahn Chul Jung, Yu‐Chul Park, Chang-wook Lee
• DOI: 10.1109/jstars.2026.3678448

Research Groups

[Not specified in the provided text]

Short Summary

This paper proposes an integrated methodology combining CCD, adaptive-dNBR, and metaheuristic-optimized hybrid deep learning for wildfire detection and susceptibility mapping in Los Angeles County.

Objective

• To develop and apply an integrated methodology for wildfire detection and susceptibility mapping, utilizing CCD, adaptive-dNBR, and metaheuristic-optimized hybrid deep learning.

Study Configuration

• Spatial Scale: Los Angeles County
• Temporal Scale: [Not specified in the provided text]

Methodology and Data

• Models used: Hybrid Deep Learning (metaheuristic-optimized), CCD (Change Detection), Adaptive-dNBR (Normalized Burn Ratio).
• Data sources: [Not specified in the provided text, but typically involves satellite imagery for CCD and dNBR]

Main Results

[Not specified in the provided text]

Contributions

[Not specified in the provided text, but likely the novel integration of the mentioned techniques for wildfire detection and susceptibility mapping]

Funding

[Not specified in the provided text]

Citation

@article{Achmad2026Integrating,
  author = {Achmad, Arief Rizqiyanto and Park, Eunseok and 이광재 and Lee, Seung-Jae and Chae, Sung-Ho and Jung, Hahn Chul and Park, Yu‐Chul and Lee, Chang-wook},
  title = {Integrating CCD and Adaptive-dNBR with Metaheuristic-Optimized Hybrid Deep Learning for Wildfire Detection and Susceptibility Mapping in Los Angeles County},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3678448},
  url = {https://doi.org/10.1109/jstars.2026.3678448}
}