LITHOLOGICAL CLASSIFICATION USING MULTI-SENSOR DATA AND CONVOLUTIONAL NEURAL NETWORKS

13 citations as recorded by crossref.

1. Geological Mapping Using Direct Sampling and a Convolutional Neural Network Based on Geochemical Survey Data Z. Wang et al. 10.1007/s11004-022-10023-z
2. Interpretable Dual-Channel Convolutional Neural Networks for Lithology Identification Based on Multisource Remote Sensing Data S. Wu & Y. Liu 10.3390/rs17071314
3. Automatic lithological classification of the sinian system based on spectral features and machine learning: a case study from the Gaozhu section in Wuxi M. Guo 10.1016/j.microc.2025.115175
4. Research status and prospect of Ga-rich alunite deposits F. FAN et al. 10.3724/j.1000-4734.2025.45.074
5. Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities Y. Chen et al. 10.3390/min13091153
6. Lithologic Unit Classification Attention-Based and Multiscale Geology Knowledge-Guided Framework in Vegetated Areas Z. Hui et al. 10.1109/TGRS.2025.3581493
7. RSWFormer: A Multi-Scale Fusion Network from Local to Global with Multiple Stages for Regional Geological Mapping S. Han et al. 10.3390/rs16142548
8. Lithological Mapping Based on Fully Convolutional Network and Multi-Source Geological Data Z. Wang et al. 10.3390/rs13234860
9. Fusion of GaoFen-5 and Sentinel-2B data for lithological mapping using vision transformer dynamic graph convolutional network Y. Dong et al. 10.1016/j.jag.2024.103780
10. Landslide Susceptibility Mapping Using Feature Fusion-Based CPCNN-ML in Lantau Island, Hong Kong Y. Chen et al. 10.1109/JSTARS.2021.3066378
11. Deep learning-based geological map generation using geological routes C. Li et al. 10.1016/j.rse.2024.114214
12. Lightweight Deep Learning Approaches for Lithological Mapping in Vegetated Terrains of the Vălioara Valley, Romania V. Árvai & G. Albert 10.3390/ijgi14090350
13. Pattern Recognition in the Tasks of Landform Mapping S. Kharchenko 10.31857/S2587556623010089