Temporal ViT-U-Net Tandem Model: Enhancing Multi-Sensor Land Cover Classification Through Transformer-Based Utilization of Satellite Image Time Series

6 citations as recorded by crossref.

1. Hierarchical multistep remote sensing classification enhances land use mapping accuracy in anthropogenically modified landscapes G. Domingues et al. https://doi.org/10.1590/s1982-21702026000100001
2. Edge-Guided Multiscale Spatio–Temporal–Spectral Dynamic Coordination for Agricultural Parcel Classification W. Gan et al. https://doi.org/10.1109/JSTARS.2025.3597559
3. Multi-class segmentation of land cover types for DigitalGlobe satellite imagery using deep hybrid UNet-ResNet-50 network optimised with metaheuristic particle swarm algorithm E. Boah et al. https://doi.org/10.1007/s44212-025-00098-4
4. Enhancing lake identification in Alpine periglacial environments by leveraging the global context of transformers J. Xu et al. https://doi.org/10.5194/tc-20-2851-2026
5. Abrupt intensification and spatiotemporal dynamics of large-scale harmful algal blooms in China’s marginal seas mapped using multi-sensor satellite imagery and deep learning Y. Du et al. https://doi.org/10.1016/j.isprsjprs.2026.04.033
6. Global, multi-scale standing deadwood segmentation in centimeter-scale aerial images J. Möhring et al. https://doi.org/10.1016/j.ophoto.2025.100104