{"url":"/dataset/geoeye-1-pairmax","name":"GeoEye-1 PairMax","full_name":null,"description_markdown":"This dataset refers to the two images acquired by the GeoEye-1 satellite, representing London and Trenton, respectively.\r\n\r\nThe PAirMax dataset is a collection of images for evaluating the performance of pansharpening algorithms. This data collection includes nine test cases at full resolution, acquired by different sensors belonging to Maxar's constellation of high-resolution satellites. Nine related test cases at reduced resolution, simulated according to Wald’s protocol, are also included.\r\n\r\nFor further details, refer to the paper:\r\n\r\nG. Vivone, M. Dalla Mura, A. Garzelli, and F. Pacifici, \"A Benchmarking Protocol for Pansharpening: Dataset, Pre-processing, and Quality Assessment,\" IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021.","description_withheld":null,"homepage":"https://resources.maxar.com/product-samples/pansharpening-benchmark-dataset","introduced_date":"2021-06-06","introduced_date_note":null,"introduced_by":null,"license":{"name":"Proprietary","url":"https://www.maxar.com/legal/evaluation-license"},"modalities":[],"tasks":[{"name":"Pansharpening","url":"/task/pansharpening","datasets_with_task":"/datasets/task/pansharpening"}],"languages":[],"variants":["GeoEye-1 PairMax"],"data_loaders":[],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/pansharpening-on-geoeye-1-pairmax","task":"Pansharpening","dataset_variant":"GeoEye-1 PairMax","rows":1,"metrics":["D_lambda","D_lambda_aligned","D_rho","R-ERGAS"],"first_row_in_archive_order":{"model":"Lambda-PNN","paper":"/paper/unsupervised-deep-learning-based","metrics":{"D_lambda":"0.049","D_lambda_aligned":"0.026","D_rho":"0.042","R-ERGAS":"3.193"},"code_links":[{"title":"matciotola/hyperspectral_pansharpening_toolbox","url":"https://github.com/matciotola/hyperspectral_pansharpening_toolbox"},{"title":"matciotola/lambda-pnn","url":"https://github.com/matciotola/lambda-pnn"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/unsupervised-deep-learning-based","title":"Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced Spectral and Spatial Fidelity","date":"2023-07-26","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}