Ghosh et al. (2026) Deep Learning Framework For High Resolution Large-Scale Vegetation Optical Depth Mapping Using Airborne LiDAR and Mobile GNSS-T Data

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Abesh Ghosh, Mohammad Ehsanul Hoque, Md Mehedi Farhad, Ali Cafer Gürbüz, Alicia Peduzzi, Mehmet Kurum
• DOI: 10.1109/jstars.2026.3675561

Research Groups

The paper introduces a deep learning framework to map vegetation optical depth at high resolution and large scale, leveraging data from airborne LiDAR and mobile GNSS-T systems.

Objective

• To develop and implement a deep learning framework for high-resolution, large-scale mapping of vegetation optical depth.

Study Configuration

• Spatial Scale: Large-scale, High Resolution.
• Temporal Scale:

Methodology and Data

• Models used: Deep Learning Framework.
• Data sources: Airborne Light Detection and Ranging (LiDAR), Mobile Global Navigation Satellite System-Transmitter (GNSS-T) data.

Main Results

Contributions

Funding

Citation

@article{Ghosh2026Deep,
  author = {Ghosh, Abesh and Hoque, Mohammad Ehsanul and Farhad, Md Mehedi and Gürbüz, Ali Cafer and Peduzzi, Alicia and Kurum, Mehmet},
  title = {Deep Learning Framework For High Resolution Large-Scale Vegetation Optical Depth Mapping Using Airborne LiDAR and Mobile GNSS-T Data},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3675561},
  url = {https://doi.org/10.1109/jstars.2026.3675561}
}