## 5 Conclusions  
To better exploit PlanetScope (PL) datasets within GEE, this work assessed the advantages of applying an object-based (OB) approach to PL data for land cover classification. Moreover, the benefits of integrating PL imagery with S2 and S1 data in a pixel-based (PB) and OB approach were evaluated. Using the PL-only dataset with a typical PB approach produced the worse overall accuracy results (67%). Adopting an OB approach on the same dataset yielded very good overall accuracy (82%), although some issues arose in classifying some classes (shrublands and bare soil). The integration of PL data with the S2 and S1 datasets improved both PB and OB accuracy outputs (82 vs. 67% and 91 vs. 82%, respectively). Although various export to asset steps were necessary, the study demonstrated the applicability of the OB approaches in GEE, even in vast study areas and on high-resolution imagery. Additional applications in other study areas, possibly including geometrical assessment and more LULC classes, may further validate the proposed approach. Including the objects' geometrical features in the OB approach remains an open challenge in GEE. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs14112628/s1](https://www.mdpi.com/article/10.3390/rs14112628/s1)]([https://www.mdpi.com/article/10.3390/rs14112628/s1](https://www.mdpi.com/article/10.3390/rs14112628/s1)), Table S1: Statistical significance of the difference in OA between the ten models compared using McNemar test (significant differences with \(p\)-value < 0.05 are indicated with *). This research received no external funding. [][][][]]]]]]]]]]]]]]]]]]])))))))))))((((((httphttphttp://www. References References  
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