23 citations as recorded by crossref.

1. Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10) Z. Venter & M. Sydenham 10.3390/rs13122301
2. Lessons learned in developing reference data sets with the contribution of citizens: the Geo-Wiki experience L. See et al. 10.1088/1748-9326/ac6ad7
3. Time-first approach for land cover mapping using big Earth observation data time-series in a data cube – a case study from the Lake Geneva region (Switzerland) G. Giuliani 10.1080/20964471.2024.2323241
4. Skyline variations allow estimating distance to trees on landscape photos using semantic segmentation L. Martinez-Sanchez et al. 10.1016/j.ecoinf.2022.101757
5. Developing High-Resolution Crop Maps for Major Crops in the European Union Based on Transductive Transfer Learning and Limited Ground Data Y. Luo et al. 10.3390/rs14081809
6. From parcel to continental scale – A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations R. d’Andrimont et al. 10.1016/j.rse.2021.112708
7. Forest tree species distribution for Europe 2000–2020: mapping potential and realized distributions using spatiotemporal machine learning C. Bonannella et al. 10.7717/peerj.13728
8. A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat M. Witjes et al. 10.7717/peerj.13573
9. Ha Long—Cam Pha Cities Evolution Analysis Utilizing Remote Sensing Data G. Nguyen et al. 10.3390/rs14051241
10. Unbiased Area Estimation Using Copernicus High Resolution Layers and Reference Data L. Kleinewillinghöfer et al. 10.3390/rs14194903
11. Current state and challenges in producing large-scale land cover maps: review based on recent land cover products F. Gilić et al. 10.1080/10106049.2023.2242693
12. Evaluation of Accuracy Enhancement in European-Wide Crop Type Mapping by Combining Optical and Microwave Time Series B. Ghassemi et al. 10.3390/land11091397
13. Development of a 10-m resolution maize and soybean map over China: Matching satellite-based crop classification with sample-based area estimation H. Li et al. 10.1016/j.rse.2023.113623
14. Designing a European-Wide Crop Type Mapping Approach Based on Machine Learning Algorithms Using LUCAS Field Survey and Sentinel-2 Data B. Ghassemi et al. 10.3390/rs14030541
15. Rapid early-season maize mapping without crop labels N. You et al. 10.1016/j.rse.2023.113496
16. Crop Identification Using Deep Learning on LUCAS Crop Cover Photos M. Yordanov et al. 10.3390/s23146298
17. A novel and robust method for large-scale single-season rice mapping based on phenology and statistical data M. Yang et al. 10.1016/j.isprsjprs.2024.05.019
18. Seasonal Spatio-temporal Land Cover Dynamics in the Upper Brantas Watershed S. Beselly et al. 10.1088/1755-1315/930/1/012021
19. Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union R. d’Andrimont et al. 10.1038/s41597-020-00675-z
20. Designing a European-Wide Crop Type Mapping Approach Based on Machine Learning Algorithms Using LUCAS Field Survey and Sentinel-2 Data B. Ghassemi et al. 10.3390/rs14030541
21. Crowdsourcing LUCAS: Citizens Generating Reference Land Cover and Land Use Data with a Mobile App J. Laso Bayas et al. 10.3390/land9110446
22. Global annual wetland dataset at 30 m with a fine classification system from 2000 to 2022 X. Zhang et al. 10.1038/s41597-024-03143-0
23. Modelling Accessibility to Urban Green Areas Using Open Earth Observations Data: A Novel Approach to Support the Urban SDG in Four European Cities G. Giuliani et al. 10.3390/rs13030422