Walk and bike path cleaning
About this dataset
Columns (23)
Sample rows
| id | uuid | breite | laenge | nummer | wegeart | wegetyp | kreis_name | beschreibung | strasse_name | gemeinde_name | kreis_schluessel |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0000009560-002 | 000037ab-220c-42bc-96de-3357f525659f | 1.5 | 1477.04 | 62 | Radweg | Rostock | von Einfahrt Hafengelände bis Am Kabutzenhof und bis Wendehammer Kempowski Ufer | Warnowufer | Rostock, Hanse- und Universitätsstadt | 13003 | |
| 0000003270-002 | 00012303-b1e1-42fb-a4aa-f14e0647287c | 2 | 2812.84 | 27 | Gehweg | Rostock | Hamburger Str. von Holbeinplatz bis Schutower Kreuz linke Seite (nur WD) | Hamburger Str. | Rostock, Hanse- und Universitätsstadt | 13003 | |
| 0000006070-001 | 0018eea7-c11f-47f1-9480-ab2fc0b1c963 | 2.5 | 150.52 | 5 | Gehweg | Rostock | Klopstockstr. beidseitig der Grünfläche / Ecke Goetheplatz bis Goetheplatz 9 | Klopstockstr. | Rostock, Hanse- und Universitätsstadt | 13003 | |
| 0000009600-005 | 001c35c9-da36-4fec-96f2-efe58fcad3a4 | 2 | 296.1 | 30 | Gehweg | Rostock | Gehweg entlang Schäferteich von Weidendamm bis Parkplatz S.-Allende-Str. | Weidendamm | Rostock, Hanse- und Universitätsstadt | 13003 | |
| 0000008180-002 | 002df96b-676e-4eb2-b5bc-3a20f72a6b54 | 3 | 415.81 | 23 | Gehweg | Rostock | Satower Str. von Rennbahnallee bis Am Richtfunkturm | Satower Str. | Rostock, Hanse- und Universitätsstadt | 13003 | |
| 0000008110-002 | 0090957d-7033-4ceb-92be-74b508342abc | 2.5 | 123.23 | 20 | Gehweg | Rostock | von R.-Diesel-Str. zur Brahestr. | Rudolf-Diesel-Str. | Rostock, Hanse- und Universitätsstadt | 13003 | |
| 0000010050-001 | 00e02c9e-2dcd-45a4-b499-33998b8ed7d2 | 2.5 | 936.2 | 11 | Gehweg | Rostock | Groß Kleiner Damm zwischen Warnowallee B 105 und Kleiner Warnowdamm | Groß Kleiner Damm | Rostock, Hanse- und Universitätsstadt | 13003 | |
| 0000001120-002 | 011f6b4f-250c-4e25-ae2b-97a79a6e4f9e | 9 | 96.3 | Gehweg | Rostock | Barnstorfer Weg | Rostock, Hanse- und Universitätsstadt | 13003 |
A sample of the data. The full table and an interactive map open with a free account.
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This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between paved and unpaved roads across the region. Surface information is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name: "Germany: **Planet** Road Surface". The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 21.4159 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 11.9981 million km (56.0243%) - Unpaved roads: 3.8254 million km (17.8622%) About 6.166 million km (28.7914%) of roads lack surface information in OSM. To reduce this gap, the Mapillary-derived surface dataset provides an additional 1.3618 million km (22.0859%) of surface information. Intended Use This dataset supports transportation planning, infrastructure assessment, climate emissions analysis, and GIS applications. AI Features - pred_class: Model-predicted surface class ("paved" or "unpaved") - pred_label: Binary label (0 = paved, 1 = unpaved) - osm_surface_class: Surface classification derived from OSM tags - combined_surface_osm_priority: Combined classification prioritizing OSM - combined_surface_DL_priority: Combined classification prioritizing DL prediction - n_of_predictions_used: Number of predictions used for estimation - predicted_length: DL-estimated length (meters) - DL_mean_timestamp: Mean timestamp of predictions OSM Attributes - name - name:en - name:* (local language) - highway - surface - smoothness - width - lanes - oneway - bridge - layer - source Urban Classification Attributes - continent - country_iso_a2 - urban (0 = rural, 1 = urban) - urban_area - osm_id - osm_type Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org
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