A dataset of universal equipment monitoring for loess landslides in Chengguan District, Lanzhou City (2023–2025)

Landslide monitoring, as the core foundation of landslide warning, is crucial for accurately assessing the development process of disaster hazards. Ground monitoring technology based on universal monitoring equipment has become a key component of the current geological hazard monitoring system, thanks to its significant advantages such as high automation and fine spatiotemporal resolution. Due to its special geographical and geological conditions, Chengguan District in Lanzhou City presents significant characteristics of dense distribution of landslide hidden danger points and concentrated population under threat. Conducting refined monitoring and early warning work has become an urgent practical need for regional geological disaster prevention and reduction. This study focuses on typical loess landslide disaster bodies developed in the area. Based on the existing universal geological disaster monitoring equipment network, literature research, multi-source data automatic collection, data processing and quality control are comprehensively used to construct meteorological environment and deformation evolution datasets for each monitoring point from 2023 to 2025. This dataset mainly includes basic information such as the names, coordinates, threat objects, triggering factors, and scale levels of 117 landslide monitoring points, as well as specific layout location information of integrated rainfall monitoring stations, GNSS displacement meters, crack meters, and multi parameter deformation meters, and real-time monitoring data information such as rainfall, temperature, air pressure, surface displacement, inclination angle, acceleration obtained by each monitoring device. It can be used for the analysis of the disaster mechanism of loess landslides in Chengguan District and surrounding areas with similar climate and geological conditions in Lanzhou City, and for the optimization of early warning models, providing basic scientific data support for the refinement of regional early warning models.