DOI: 10.20350/digitalcsic/17517
title: Drone-derived datasets in the assessment of the 2024 cut-off low event in Valencia

description: [Description of methods used for collection/generation of data] Three different UAV platforms were used for data acquisition, each chosen based on their payload capacity and compatibility with various sensors:: 1) DJI Matrice 300 RTK (M300). This quadcopter supports a maximum payload of 2.5 kg and has a maximum take-off weight (MTOW) of 9 kg. It was equipped with several sensors to capture multispectral imagery, RGB data, and gas concentration measurements: - DJI Zenmuse H20T. A multi-sensor payload featuring a 20 MP RGB zoom camera (1/2.7” CMOS) and a 12 MP wide-angle RGB camera (1/2.3” CMOS). It includes a detachable gimbal and offers a maximum shutter speed of 1/8000 s. Under controlled conditions, the manufacturer specifies an accuracy of 0.2 m plus 0.15% of the target distance. - MicaSense RedEdge-MX. A multispectral dual sensor that captures 10 bands across the visible and near-infrared (NIR) spectrum, including blue (444, 475 nm), green (531, 560 nm), red (650, 668 nm), red edge (705, 717, 740 nm), and NIR (842 nm). The system integrates a Downwelling Light Sensor (DLS) with GPS to account for changes in solar irradiance, along with a radiometric calibration panel (RP04-1924106-0B) to ensure radiometric accuracy. - Sniffer4D V2 RS (Soarability). A multi-gas sensing and mapping system capable of simultaneously detecting total volatile organic compounds (TVOC, ppm), sulfur dioxide (SO₂, μg/m³), ozone and nitrogen dioxide (O₃ + NO₂, μg/m³), particulate matter (PM1.0, PM2.5, PM10, μg/m³), flammable gases (CxHy, %), and hydrogen sulfide (H₂S, μg/m³). It provides real-time visualization in both 2D and 3D. In addition, it was integrated with the Sniffer4D Multi-pass TDLAS Hyper-local Methane Module, enabling quantitative methane detection with a resolution of 1 ppm. 2) DJI Mavic 3E (M3E). A lightweight quadcopter equipped with a 20 MP wide-angle RGB camera (4/3” CMOS) and a 12 MP telephoto RGB camera with 56× hybrid zoom. It has an MTOW of 1,050 g and a maximum flight endurance of 45 minutes. 3) DJI Mavic 2 Enterprise Advanced (M2EA). This UAV features a 48 MP RGB optical sensor with a 1/2” CMOS sensor. It has an MTOW of 1,100 g and a maximum flight duration of 31 minutes.
description: [Methods for processing the data] 1) Structure from Motion (SfM) photogrammetry: Aerial images acquired along pre-programmed flight paths with sufficient overlap were processed using Pix4D Mapper (Pix4D SA, Lausanne, Switzerland, v.4.8.3) to generate high-resolution topographic products. The workflow consisted of image alignment, point cloud generation, digital surface model (DSM) reconstruction, orthomosaic and reflectance maps generation. 2) Gas Sniffer data monitoring: The gas detection sensors were directly connected to the PSDK port of the DJI M300, which provided power and allowed real-time data transmission to the UAV’s remote controller. Measurements were logged at one-second intervals and simultaneously stored on the sensor’s internal SD card. To ensure stable sampling, the device incorporated a rear-mounted active air intake fan, drawing ambient air at a flow rate of roughly 10 L min⁻¹.
description: [Environmental/experimental conditions] UAV flights were conducted in strict accordance with Spanish civil aviation regulations for emergency operations, under the supervision of the Spanish Aviation Safety Agency (AESA). Moreover, missions carried out following the Valencia cut-off low were undertaken within the framework of the Scientific-Technical Advisory Group for Emergency Crises (GADE) of the Spanish National Research Council (CSIC). For field operations, flight plans were pre-programmed using the DJI Pilot application (Shenzhen Dà-Jiang Innovations Sciences and Technologies Ltd., Guangdong, China) installed on the UAV controller. These plans were configured with fixed parameters, including flight altitude, speed, duration, ground sampling distance (GSD), and image overlap.
description: This repository contains the original raw data, along with some of the topographic products generated after the initial data processing, captured immediately after the 2024 Valencia cut-off low event by a drone equipped with RGB, thermal, multispectral, and gas-sniffer sensors. These datasets were used to obtain the results published in Román et al. 2025 (DOI: available soon). The data, together with their rapid and preliminary processing, were also employed in real time during the emergency to support decision-makers in tasks related to prevention, evacuation, and reconstruction. Furthermore, access to these data makes the methodologies described in the manuscript reproducible, thereby contributing to the development of new tools that can enable faster, more efficient, and more accurate management during future emergencies.
description: File List: 6 folders: Dataset 1 - Object Detection Vehicle Yard; Dataset 2 - Object Detection Trash Items; Dataset 3 - Gas Sensors Paiporta; Dataset 4 - Damage Assessment Picanya; Dataset 5 - Damage Assessment Pont Nou; Dataset 6 - Sludge Characterization