{"url":"/dataset/trn","name":"TRN","full_name":"Toulouse Road Network","description_markdown":"The Toulouse Road Network dataset describes patches of road maps from the city of Toulouse, represented both as spatial graphs G = (A, X) and as grayscale segmentation images. \r\n\r\nThe TRN dataset contains 111,034 data points (map tiles), of which: 80,357 are in the training set (around 72.4%), 11,679 are in the validation set (around 10.5%), 18,998 are in the test set (around 17.1%). \r\n  \r\nEach tile represents a squared region of side 0.001 degrees of latitude and longitude on the map, which corresponds to a square of around 110 meters. The semantic segmentation of each patch is represented as a 64 × 64 grayscale image. \r\n\r\nThe dataset is generated starting from publicly available data from OpenStreetMap. More details on the dataset characteristic and generation methods are available in our [blogpost](https://davide-belli.github.io/toulouse-road-network.html).\r\n\r\nSource: [https://github.com/davide-belli/toulouse-road-network-dataset](https://github.com/davide-belli/toulouse-road-network-dataset)\r\nImage Source: [https://arxiv.org/pdf/1910.14388.pdf](https://arxiv.org/pdf/1910.14388.pdf)","description_withheld":null,"homepage":"https://github.com/davide-belli/toulouse-road-network-dataset","introduced_date":"2019-10-31","introduced_date_note":null,"introduced_by":{"paper":"/paper/image-conditioned-graph-generation-for-road","title":"Image-Conditioned Graph Generation for Road Network Extraction","first_author":"Davide Belli","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Graphs","url":"/datasets/modality/graphs"}],"tasks":[{"name":"Graph Generation","url":"/task/graph-generation","datasets_with_task":"/datasets/task/graph-generation"}],"languages":[],"variants":["Toulouse Road Network","TRN"],"data_loaders":[{"repo":"https://github.com/davide-belli/toulouse-road-network-dataset","url":"https://github.com/davide-belli/toulouse-road-network-dataset","frameworks":["pytorch"]}],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/graph-generation-on-toulouse-road-network","task":"Graph Generation","dataset_variant":"Toulouse Road Network","rows":6,"metrics":["StreetMover"],"first_row_in_archive_order":{"model":"GGT","paper":"/paper/image-conditioned-graph-generation-for-road","metrics":{"StreetMover":"0.0158"},"code_links":[{"title":"davide-belli/generative-graph-transformer","url":"https://github.com/davide-belli/generative-graph-transformer"},{"title":"davide-belli/toulouse-road-network-dataset","url":"https://github.com/davide-belli/toulouse-road-network-dataset"},{"title":"2023-MindSpore-1/ms-code-217","url":"https://github.com/2023-MindSpore-1/ms-code-217/tree/main/trn"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/image-conditioned-graph-generation-for-road","title":"Image-Conditioned Graph Generation for Road Network Extraction","date":"2019-10-31","rows_on_this_dataset":4,"code_links":3,"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."}