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Found 7 papers, 1 papers with code
A sustainable development perspective on urban-scale roof greening priorities and benefits
Greenspaces are tightly linked to human well-being.
UB-FineNet: Urban Building Fine-grained Classification Network for Open-access Satellite Images
Then, a new fine-grained classification network with Category Information Balancing Module (CIBM) and Contrastive Supervision (CS) technique is proposed to mitigate the problem of class imbalance and improve the classification robustness and accuracy.
Urban GeoBIM construction by integrating semantic LiDAR point clouds with as-designed BIM models
In this paper, we propose a complementary strategy that integrates LiDAR point clouds with as-designed BIM models for reconstructing urban scenes.
Classification of Single Tree Decay Stages from Combined Airborne LiDAR Data and CIR Imagery
Finally, the classification is conducted on the two datasets (3D multispectral point clouds and 2D projected images) based on the three Machine Learning algorithms.
One Class One Click: Quasi Scene-level Weakly Supervised Point Cloud Semantic Segmentation with Active Learning
It considerably outperforms genuine scene-level weakly supervised methods by up to 25\% in terms of average F1 score and achieves competitive results against full supervision schemes.
A new weakly supervised approach for ALS point cloud semantic segmentation
For the ISPRS 3D Labeling Vaihingen data, by using only 0. 1% of labels, our method achieves an overall accuracy of 83. 0% and an average F1 score of 70. 0%, which have increased by 6. 9% and 12. 8% respectively, compared to model trained by sparse label information only.
Weakly Supervised Pseudo-Label assisted Learning for ALS Point Cloud Semantic Segmentation
Competitive point cloud semantic segmentation results usually rely on a large amount of labeled data.
