An experimental dataset using UAVs and LoRa technology in avalanche scenarios

Wireless communication technologies play a critical role in the effectiveness of Search and Rescue (SaR) operations, especially in avalanche scenarios where rapid localization of victims is essential. Traditional systems like ARTVA research beacons have been widely adopted for this purpose, but their performance is strongly affected by environmental factors such as snow depth and snowpack characteristics. The dataset presented in this article explores the feasibility and the performance of LoRa (Long Range) technology on board of a UAV for use in SaR scenarios. The transmitter was buried in snow across a wide area in the Dolomites, simulating the scale and conditions of a typical human-triggered avalanche, while the receiver is mounted on a commercial UAV following different flight trajectories. Specifically, we vary the flying path, duration, covered area and antenna type. For each experiment, we record key communication metrics such as the Received Signal Strength Indicator (RSSI) and the Signal-to-Noise Ratio (SNR), together with precise ground truth transmitter and receiver positions obtained via GPS-RTK. The tests covered both dry and wet snow conditions, allowing evaluation of how snow characteristics impact LoRa performance. This dataset provides strong reuse potential for researchers aiming to improve UAV-assisted localization algorithms in extreme snow environments. It can support the development and benchmarking of positioning methods based on LoRa signal strength and, more broadly, the design of resilient SaR communication systems for avalanche-prone areasBy releasing the data and contextual documentation publicly, we seek to encourage innovation in disaster response technologies and promote safer mountain rescue practices.