A Sentinel-2 Dataset for Railway Presence Classification Across Metropolitan France

Published: 1 July 2026| Version 1 | DOI: 10.17632/f2m5jwrjss.1
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Description

This dataset contains 10,000 Sentinel-2 true-color image chips for binary railway presence classification across metropolitan France. The collection comprises 5,000 chips labelled railway and 5,000 chips labelled no_railway. Each image is provided as a 224 × 224 pixel RGB PNG file and represents a 512 m × 512 m geographic footprint. Railway sample locations were derived from OpenStreetMap geometries tagged railway=rail. Railway locations were spatially balanced across the retained railway network and selected using distance thinning. Railway-absent locations were generated in the local surroundings of railway samples and were accepted only when their complete footprint did not intersect retained railway geometries or overlap with accepted image footprints. Image chips were generated from Sentinel-2 Level-2A imagery using bands B04, B03 and B02 as red, green and blue channels. Imagery was retrieved through the Copernicus Data Space Ecosystem Sentinel Hub Process API for the period from 23 June 2025 to 23 June 2026. The collection was generated using a maximum cloud-cover threshold of 10 percent and least-cloud-cover mosaicking. The repository includes the image collection and a CSV metadata table containing the image filename, class label and stable candidate identifier. The dataset can be used for supervised image classification, benchmarking of computer vision models, geographically structured validation, representation learning and railway-infrastructure mapping.

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Categories

Geography, Spatial Analysis, Rail Road Safety, Railway, Railtrack Design, Transport Infrastructure, Infrastructure, Satellite Image, Rail Infrastructure, Binary Classification

Funders

  • Helmut-Schmidt-University/University of the Federal Armed Forces Hamburg
    Grant ID: Open-Access-Publication-Fund

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