
This research proposes GroupSP, a novel unsupervised deep learning approach for semantic segmentation of high-density multispectral airborne laser scanning (ALS) data, aiming to reduce manual annotation efforts while achieving comparable accuracy to supervised methods.


coremsg

unsupervised-semantic-segmentation-of-high-density-multispectral-urban-airborne-laser-scanning-data-using-a-deep-clustering-method


Unsupervised Semantic Segmentation of High-Density Multispectral Urban Airborne Laser Scanning Data Using a Deep Clustering Method


title_rewrite


Advances in unsupervised learning have led to significant progress in semantic segmentation by incorporating depth information to improve feature correlation and sampling techniques.


unsupervised-semantic-segmentation-through-depth-guided-feature-correlation-and-sampling


Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling



EAGLE introduces a novel approach, emphasizing object-centric representation learning for unsupervised semantic segmentation, addressing the challenge of inadequate segmentation of complex objects with diverse structures.


eagle-eigen-aggregation-learning-for-object-centric-unsupervised-semantic-segmentation


EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation
