MULTISENGE: A MULTIMODAL AND MULTITEMPORAL BENCHMARK DATASET FOR LAND USE/LAND COVER REMOTE SENSING APPLICATIONS

13 citations as recorded by crossref.

1. Paving the Way Toward Foundation Models for Irregular and Unaligned Satellite Image Time Series I. Dumeur et al.
2. MMF-CNN: a multimodal fusion CNN network for winter wheat extraction incorporating active and passive time series data J. Chen et al.
3. A 3-D Fully Convolutional Network Approach for Land Cover Mapping Using Multitemporal Sentinel-1 SAR Data D. Marzi et al.
4. Recursive classification of satellite imaging time-series: An application to land cover mapping H. Calatrava et al.
5. NTL-Unet: A Satellite-Based Approach for Non-Technical Loss Detection in Electricity Distribution Using Sentinel-2 Imagery and Machine Learning M. Gremes et al.
6. Learning-Based SAR–Optical Registration for Navigation: Insights From a Multiyear, Multiseason Continental-Scale Dataset S. Bertrand et al.
7. Multimodal and Multitemporal Land Use/Land Cover Semantic Segmentation on Sentinel-1 and Sentinel-2 Imagery: An Application on a MultiSenGE Dataset R. Wenger et al.
8. Automatic wide area land cover mapping using Sentinel-1 multitemporal data D. Marzi et al.
9. The LUCAS dataset revisited: enhancing spatial representativeness for machine learning land cover mapping L. Brodský et al.
10. CerraData-4 MM: A Multimodal Benchmark Dataset on Cerrado for Land Use and Land Cover Classification M. Miranda et al.
11. Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series I. Dumeur et al.
12. EarthNets: Empowering artificial intelligence for Earth observation Z. Xiong et al.
13. Multitemporal and multimodal cross-region fine-tuning: MultiSenGE and MultiSenNA applications R. Wenger et al.