DOI: 10.5281/zenodo.17339095
title: An Experimental Dataset Using UAVs and LoRa Technology in Avalanche Scenarios

description: Overview:

The dataset contains measurements of Received Signal Strength Indicator (RSSI) and Signal-to-Noise Ratio (SNR) collected from Long-Range (LoRa) devices in avalanche Search and Rescue (SAR) scenarios. Data were collected on a plateau located in Col de Mez (Soraga, Italy) at 1,870 m in the Italian Dolomites, at three different times: April 2024, February 2025 and April 2025. The depth and conditions of the snow vary due to various environmental factors.

Tests consist of one buried transmitter and one receiver mounted on the bottom of a quadcopter professional drone, which performs several different paths over the plateau.

 

The dataset includes several path typologies, summerized as follows:





The drone stands on 121 measurement points, creating a precise grid covering an area of 100 m x 100 m, with the burial location at the center.

The UAV flies over the same area of 100 m x 100 m following: (i) a compact Greek-key pattern, (ii) a perimeter with diagonals path, (iii) a zigzag path, and (iv) free-form manual routes.

The UAV flies over an area exceeding 50,000 square meters following: (i) a perimeter flight of the area, including both diagonals, (ii) a zigzag path, (iii) a Greek-key pattern, both compact and wide, and (iv) a double cross-pattern.



 

All the tests include precise Ground Truth (GT) annotations, indicating the exact positions of the UAV, namely latitude and longitude, the height of the UAV above ground level [a.g.l.], the altitude above sea level [a.s.l.], and the speed. Moreover, annotations include the depth of the buried transmitter under the snow and the run id. 

 

The dataset aims to assess the ability to locate a victim in an avalanche scenario. The collected data allow for the evaluation of the quality of the LoRa signal in various environmental conditions, as well as the snow depth and snowpack profile. By using precise Ground Truth annotations, it is possible to assess the potential performance of a localization system. In a separate folder, the snow profiles for the three data collection periods are also included, according to the AINEVA Model 4.

 

How to use the dataset:

 

Please, read the README file detailing the dataset's format and the data collection campaign. In summary, collected data include:





dateString

timestamp

rssi

snr

longitude

latitude

height

altitude

speed

depth

runID



 

Moreover, the dataset include for each test a UAV telemetry file, which features a finer temporal resolution of UAV telemetry data. The fields are:





dateString

timestamp

longitude

latitude

height

altitude

speed

runID



 

Lastly, in each test folder, we include a third file that reports the exact position of the transmitter, i.e. longitude and latitude in WG84 reference system. 

 

How to cite this dataset:

 

- DOI number of this datsaset: 10.5281/zenodo.16572816

 

- Mavilia F., La Rosa D., Berton A., Girolami M. "An Experimental Dataset Using UAVs and LoRa Technology in Avalanche Scenarios", ---paper under review

 

For further information concerning this dataset, please refer to:

M. Girolami, F. Mavilia, A. Berton, G. Marrocco and G. Maria Bianco, "An Experimental Dataset for Search and Rescue Operations in Avalanche Scenarios Based on LoRa Technology," in IEEE Access, vol. 12, pp. 171015-171035, 2024, doi: 10.1109/ACCESS.2024.3497654.