DEAD WOOD DETECTION BASED ON SEMANTIC SEGMENTATION OF VHR AERIAL CIR IMAGERY USING OPTIMIZED FCN-DENSENET

16 citations as recorded by crossref.

1. Densely multiscale framework for segmentation of high resolution remote sensing imagery I. Bello et al. https://doi.org/10.1016/j.cageo.2022.105196
2. UAS Imagery-Based Mapping of Coarse Wood Debris in a Natural Deciduous Forest in Central Germany (Hainich National Park) C. Thiel et al. https://doi.org/10.3390/rs12203293
3. Automatic resin duct detection and measurement from wood core images using convolutional neural networks A. Fabijańska & G. Cahalan https://doi.org/10.1038/s41598-023-34304-7
4. Detection of Standing Deadwood from Aerial Imagery Products: Two Methods for Addressing the Bare Ground Misclassification Issue K. Zielewska-Büttner et al. https://doi.org/10.3390/f11080801
5. Deep learning for forest inventory and planning: a critical review on the remote sensing approaches so far and prospects for further applications A. Hamedianfar et al. https://doi.org/10.1093/forestry/cpac002
6. Detection of Large Woody Debris in Braided-Rivers RGB-UAV Dataset: A Comparative Study Q. Han et al. https://doi.org/10.3390/rs18060900
7. Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review S. Marvasti-Zadeh et al. https://doi.org/10.1145/3625387
8. Self-Adaptive-Filling Deep Convolutional Neural Network Classification Method for Mountain Vegetation Type Based on High Spatial Resolution Aerial Images S. Li et al. https://doi.org/10.3390/rs16010031
9. Deep Learning-Based Automated Forest Health Diagnosis From Aerial Images C. Chiang et al. https://doi.org/10.1109/ACCESS.2020.3012417
10. Accurate Mapping of Downed Deadwood in a Dense Deciduous Forest Using UAV-SfM Data and Deep Learning S. Dietenberger et al. https://doi.org/10.3390/rs17091610
11. Review on Convolutional Neural Networks (CNN) in vegetation remote sensing T. Kattenborn et al. https://doi.org/10.1016/j.isprsjprs.2020.12.010
12. A Systematic Review of Remote Sensing Methods for Detecting Coarse Woody Debris—Challenges and Opportunities M. Hussain et al. https://doi.org/10.1007/s41064-026-00406-w
13. Vision Transformer-Based Unhealthy Tree Crown Detection in Mixed Northeastern US Forests and Evaluation of Annotation Uncertainty D. Joshi & C. Witharana https://doi.org/10.3390/rs17061066
14. Detection of Trees on Street-View Images Using a Convolutional Neural Network D. Jodas et al. https://doi.org/10.1142/S0129065721500428
15. Review on the Possibilities of Mapping Old-Growth Temperate Forests by Remote Sensing in Europe M. Hirschmugl et al. https://doi.org/10.1007/s10666-023-09897-y
16. Evaluating superpixel algorithms for standing dead tree delineation using aerial orthoimagery I. Pawelec et al. https://doi.org/10.1016/j.jag.2026.105180