--- license: cc-by-sa-4.0 pretty_name: GEO-Bench 1.0 size_categories: - 10B GEO-Bench is a benchmark for earth monitoring machine learning applications composed of 6 classification tasks and 6 segmentation tasks. ## Dataset Details ### Dataset Description - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses ### Direct Use [More Information Needed] ### Out-of-Scope Use [More Information Needed] ## Dataset Structure [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Data Collection and Processing [More Information Needed] #### Who are the source data producers? [More Information Needed] ### Annotations [optional] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] #### Personal and Sensitive Information [More Information Needed] ## Bias, Risks, and Limitations [More Information Needed] ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation **BibTeX:** ```bibtex @misc{lacoste2023geobench, title={GEO-Bench: Toward Foundation Models for Earth Monitoring}, author={Alexandre Lacoste and Nils Lehmann and Pau Rodriguez and Evan David Sherwin and Hannah Kerner and Björn Lütjens and Jeremy Andrew Irvin and David Dao and Hamed Alemohammad and Alexandre Drouin and Mehmet Gunturkun and Gabriel Huang and David Vazquez and Dava Newman and Yoshua Bengio and Stefano Ermon and Xiao Xiang Zhu}, year={2023}, eprint={2306.03831}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```