{"url":"/dataset/googleearth","name":"GoogleEarth","full_name":null,"description_markdown":"The GoogleEarth dataset is collected from Google Earth Studio, including 400 orbit trajectories in Manhattan and Brooklyn. Each trajectory consists of 60 images, with orbit radiuses ranging from 125 to 813 meters and altitudes varying from 112 to 884 meters. In addition to the images, Google Earth Studio provides camera intrinsic and extrinsic parameters, making it possible to create automated annotations for semantic and building instance segmentation","description_withheld":null,"homepage":"https://haozhexie.com/project/city-dreamer","introduced_date":"2023-09-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/citydreamer-compositional-generative-model-of","title":"CityDreamer: Compositional Generative Model of Unbounded 3D Cities","first_author":"Haozhe Xie","url":null},"license":{"name":"S-Lab License","url":"https://raw.githubusercontent.com/hzxie/CityDreamer/master/LICENSE"},"modalities":[],"tasks":[{"name":"Scene Generation","url":"/task/scene-generation","datasets_with_task":"/datasets/task/scene-generation"}],"languages":[],"variants":["GoogleEarth"],"data_loaders":[],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/scene-generation-on-googleearth","task":"Scene Generation","dataset_variant":"GoogleEarth","rows":5,"metrics":["Depth Error","KID","Camera Error","FID"],"first_row_in_archive_order":{"model":"GaussianCity","paper":"/paper/gaussiancity-generative-gaussian-splatting","metrics":{"Camera Error":"0.057","Depth Error":"0.136","FID":"86.94","KID":"0.09"},"code_links":[{"title":"hzxie/GaussianCity","url":"https://github.com/hzxie/GaussianCity"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/gaussiancity-generative-gaussian-splatting","title":"GaussianCity: Generative Gaussian Splatting for Unbounded 3D City Generation","date":"2024-06-10","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":10,"samples_ran":7,"samples_unverified":3,"pointer_only_for_licence":10,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/citydreamer-compositional-generative-model-of","title":"CityDreamer: Compositional Generative Model of Unbounded 3D Cities","date":"2023-09-01","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":9,"samples_ran":5,"samples_unverified":4,"pointer_only_for_licence":9,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/persistent-nature-a-generative-model-of","title":"Persistent Nature: A Generative Model of Unbounded 3D Worlds","date":"2023-03-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/scenedreamer-unbounded-3d-scene-generation","title":"SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collections","date":"2023-02-02","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":2,"samples_harvested":19,"samples_ran":12,"samples_unverified":7,"pointer_only_for_licence":19,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}