FUSION OF HYPERSPECTRAL, MULTISPECTRAL, COLOR AND 3D POINT CLOUD INFORMATION FOR THE SEMANTIC INTERPRETATION OF URBAN ENVIRONMENTS

9 citations as recorded by crossref.

1. Tinto: Multisensor Benchmark for 3-D Hyperspectral Point Cloud Segmentation in the Geosciences A. Afifi et al.
2. Glacier Monitoring Based on Multi-Spectral and Multi-Temporal Satellite Data: A Case Study for Classification with Respect to Different Snow and Ice Types J. Florath et al.
3. Multi-model ensembles for object detection in multispectral images: A case study for precision agriculture D. Scutelnic et al.
4. A Prior Level Fusion Approach for the Semantic Segmentation of 3D Point Clouds Using Deep Learning Z. Ballouch et al.
5. Integrating colored LiDAR and YOLO semantic segmentation for design feature extraction in Chinese ancient architecture Y. Li et al.
6. Improving Mineral Classification Using Multimodal Hyperspectral Point Cloud Data and Multi-Stream Neural Network A. Rizaldy et al.
7. Point cloud semantic segmentation of grotto scenes using the knowledge-guided deep learning method S. Yang et al.
8. Impact of visual features and urban form on street vitality using explainable AI: a study of historic districts in Shanghai Z. Liu et al.
9. Investigating Prior-Level Fusion Approaches for Enriched Semantic Segmentation of Urban LiDAR Point Clouds Z. Ballouch et al.