{"url":"/dataset/freiburg-terrains","name":"Freiburg Terrains","full_name":"Freiburg Terrains","description_markdown":"**Freiburg Terrains** consist of three parts: 3.7 hours of audio recordings of the microphone pointed at the robot wheels. It also contains 24K RGB images from the camera mounted on top of the robot. The dataset creators also provide the SLAM poses for each data collection run. The dataset can be used for terrain classification which is useful for agent navigation tasks.\n\nSource: [http://deepterrain.cs.uni-freiburg.de/](http://deepterrain.cs.uni-freiburg.de/)\nImage Source: [http://deepterrain.cs.uni-freiburg.de/](http://deepterrain.cs.uni-freiburg.de/)","description_withheld":null,"homepage":"http://deepterrain.cs.uni-freiburg.de/","introduced_date":"2019-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/self-supervised-visual-terrain-classification","title":"Self-Supervised Visual Terrain Classification from Unsupervised Acoustic Feature Learning","first_author":"Jannik Zürn","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Audio","url":"/datasets/modality/audio"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"}],"languages":[],"variants":["Freiburg Terrains"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"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."}