在空间与时间维度上获取高分辨率数据，对建设气候韧性城市至关重要。当前用于监测城市参数的数据集主要依赖人工巡检、嵌入式传感、遥感或标准街景影像（RGB）。这些方法及对应数据集分别受限于可扩展性差、时空分辨率不一致、视角为俯视或光谱信息匮乏等问题。本文提出一种新方法及其开源实现：一种多光谱地面视角数据集，可规避上述限制。该数据集包含 17,718 张街景多光谱影像，使用安装于自行车上的 RGB、近红外（Near-infrared）和热红外（Thermal）传感器，在荷兰多种城市形态（村庄、小镇、小城市及大型城区）中采集。本工作高度重视数据校准与质量控制，并详尽提供了数据采集方法（包括硬件与软件细节）。据我们所知，Spectrascapes 是首个同类开源数据集。最后，我们展示了该数据集支持的两个下游应用案例，并指出了其在机器学习、城市规划与遥感领域的潜在研究方向。

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.