The Spectrascapes Dataset: Street-view imagery beyond the visible captured using a mobile platform

TL;DR

The Spectrascapes Dataset offers a comprehensive multi-spectral street-view imagery collection captured via a mobile platform, enabling advanced urban analysis beyond visible spectra.

Contribution

It introduces the first open-access multi-spectral street-view dataset with detailed calibration, collected using a novel mobile method across diverse urban environments.

Findings

Dataset includes 17,718 multi-spectral images with RGB, NIR, and Thermal data.

Demonstrated two downstream applications in urban analysis.

Provides detailed methodology for data collection and calibration.

Abstract

High-resolution data in spatial and temporal contexts is imperative for developing climate resilient cities. Current datasets for monitoring urban parameters are developed primarily using manual inspections, embedded-sensing, remote sensing, or standard street-view imagery (RGB). These methods and datasets are often constrained respectively by poor scalability, inconsistent spatio-temporal resolutions, overhead views or low spectral information. We present a novel method and its open implementation: a multi-spectral terrestrial-view dataset that circumvents these limitations. This dataset consists of 17,718 street level multi-spectral images captured with RGB, Near-infrared, and Thermal imaging sensors on bikes, across diverse urban morphologies (village, town, small city, and big urban area) in the Netherlands. Strict emphasis is put on data calibration and quality while also providing the details of our data collection methodology (including the hardware and software details). To the best of our knowledge, Spectrascapes is the first open-access dataset of its kind. Finally, we demonstrate two downstream use-cases enabled using this dataset and provide potential research directions in the machine learning, urban planning and remote sensing domains.