Rémi - Lignes et itinéraires réguliers du réseau Rémi

Date des données : 2024 Source des données : IGN portail EnR Projection : Lambert 93 Zone : France Métropolitaine Les données correspondent à l’emprise des sites appartenant au réseau Grand Site de France. Le Réseau des Grands Sites de France rassemble des paysages exceptionnels, reconnus de tous et fragiles. Ils sont protégés et gérés selon les valeurs du développement durable. Cette couche représente notamment les périmètres définis à l'article L. 341-15-1 du code de l'environnement pour lesquels les communes doivent demander un avis du gestionnaire pour définir une zone d’accélération EnR (Loi APER) Cette couche d’entités est offerte par l’équipe GéoData d’Esri France. Elle fait partie du Pack EnR. Les données sont optimisées pour l’usage dans la plateforme ArcGIS. Plus d’information sur les offres sur le site web Esri France. Contactez-nous avec des questions ou des commentaires via livingatlas@esrifrance.fr.

Ce jeu de données correspond à l'offre mobilité du réseau interurbain Rémi en Centre-Val de Loire. Les données proviennent de JVMALIN, la plateforme multimodale sur les transports en région. La plateforme est alimentée par les différents délégataires du réseau Rémi qui mettent à jour régulièrement les arrêts, les lignes, les itinéraires et les horaires qu'ils désservent. Les données sont actualisées sur ce jeu de données de manière hebdomadaire.

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: "France: **Planet** Road Surface". The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 15.7615 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 6.2742 million km (39.8068%) - Unpaved roads: 1.0159 million km (6.4456%) About 9.1728 million km (58.1974%) of roads lack surface information in OSM. To reduce this gap, the Mapillary-derived surface dataset provides an additional 0.6775 million km (7.3862%) 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

Road network across the area tagged `highway=*`: motorways, residential streets, service roads, footways, and trails. Useful for routing, accessibility analysis, and evacuation planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: highway - Part of the country exports on HDX by HOT

Railway lines and stations tagged `railway=*`: rail lines, tram tracks, and stations. Useful for transport accessibility analysis and basemap context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: railway - Part of the country exports on HDX by HOT

Airports and aviation infrastructure: airports, heliports, helipads, runways, terminals, and emergency landing sites tagged via `aeroway`, `building`, or `emergency`. Useful for logistics, evacuation routing, and aerial-response planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: aeroway, building, emergency - Part of the country exports on HDX by HOT