Deep Learning-Based Semantic Segmentation of Airborne LiDAR Point Clouds Using a Transformer-Enhanced PointNet++ Architecture
- DOI
- 10.3390/geomatics6030043
- Published
- 2026-04-29
- Container
- Geomatics
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/geomatics6030043,
title = {Deep Learning-Based Semantic Segmentation of Airborne LiDAR Point Clouds Using a Transformer-Enhanced PointNet++ Architecture},
author = {Hacer Kubra Sevinc and Ismail Rakip Karas},
year = {2026},
journal = {Geomatics},
doi = {10.3390/geomatics6030043},
url = {https://doi.org/10.3390/geomatics6030043}
}RIS
TY - JOUR TI - Deep Learning-Based Semantic Segmentation of Airborne LiDAR Point Clouds Using a Transformer-Enhanced PointNet++ Architecture AU - Hacer Kubra Sevinc AU - Ismail Rakip Karas PY - 2026 JO - Geomatics DO - 10.3390/geomatics6030043 UR - https://doi.org/10.3390/geomatics6030043 ER -
APA
Sevinc, H. K., & Karas, I. R. (2026). Deep Learning-Based Semantic Segmentation of Airborne LiDAR Point Clouds Using a Transformer-Enhanced PointNet++ Architecture. Geomatics. https://doi.org/10.3390/geomatics6030043
Source records
- crossref · retrieved 2026-09-25T05:29:41.182Z