DOI: 10.20350/digitalcsic/17464
title: Drone-based RGB, thermal, and multispectral datasets for application with MosaicSeadron

description: El dataset se puede consultar y descargar en el siguiente enlace: https://saco.csic.es/s/8fCwJxmybDMi98f
description: [Description of methods used for collection/generation of data] Three different drones were used for data acquisition, equipped with different sensors: i) The quadcopter DJI Matrice 300 (M300) is equipped with both the MicaSense RedEdge-MX dual multispectral sensor and the DJI Zenmuse H20T sensor. The multispectral sensor has ten bands that capture information from the visible to near-infrared (NIR) range, with wavelengths centered, respectively, at the blue (444 and 475 nm), green (531 and 560 nm), red (650 and 668 nm), red edge (705, 717 and 740 nm), and NIR (842 nm) regions. In addition, it includes a Downwelling Light Sensor (DLS2) to account for changes in solar angle and illumination during flight. A calibration panel (RP04-1924106-0B) was used before each UAV flight for radiometric calibration. The DJI Zenmuse H20T sensor includes a 20-megapixel (MP) optical RGB Zoom sensor with a complementary metal oxide semiconductor (CMOS) 1/2.7″, a 12 MP optical RGB wide angle sensor with a CMOS 1/2.3″, and an Uncooled VOx Microbolometer thermal sensor with a focal length of 13.5 mm, capturing 640 × 512 pixel image resolution. The M300 has on-board real-time kinematic (RTK) technology to enhance georeferencing accuracy, achieving 1 cm ± 1 ppm (horizontal) and 1.5 cm ± 1 ppm (vertical) accuracies when RTK is enabled and fixed. ii) The DJI Phantom 4 Pro (P4P) is equipped with the MicaSense RedEdge-MX sensor, including five bands on the electromagnetic spectrum, namely, in the blue (475 nm), green (560 nm), red (668 nm), red edge (717 nm), and NIR (842 nm) regions. This sensor also includes the DLS and is mounted using a 10° 3D-printed mount, resulting in a direct nadir viewing angle while in flight. The P4P does not have RTK technology on board, so the vertical and horizontal georeferencing accuracies are ±0.1 m and ±0.3 m (with vision positioning) and ±0.5 m and ±1.5 m (with GPS positioning), respectively. iii) The DJI Mavic 2 Enterprise Advanced (M2EA) is equipped with an extra RTK module to ensure accurate georeferencing. It features a 48 MP, 1/2″ CMOS optical RGB sensor and a 640 × 512 px Uncooled VOx Microbolometer thermal sensor. This RTK quadcopter achieves a horizontal accuracy of 1 cm ± 1 ppm and a vertical accuracy of 1.5 cm ± 1 ppm, without the requirement of ground control points (GCPs).
description: [Environmental/experimental conditions] Data acquisition was attempted to be carried out during spring tide and clear sky weather conditions. In the vast majority of study areas, this was possible except in the Antarctic locations where cloudiness and wind conditions prevailed during the sampling days. In the flights conducted over the San Pedro River and Estepona, strong winds also hindered the execution of the intended UAV surveys. Flight missions were planned using the Universal Ground Control System (UgCS) client mission planning software (SPH Engineering, Riga, Latvia, v.4.2.156) and Pix4D Capture Pro software (Pix4D SA, Lausanne, Switzerland, v.4.12), taking into account different overlapping settings, altitudes, and flight orientations, which are displayed in Table 1 along with the dates of drone survey execution. To support RTK measurements and improve georeferencing accuracies, a Reach RS2+ RTK GNSS antenna (EMLID) was set as the base station for all drone surveys. Spanish (Spanish Agency for Aviation Safety, AESA), European (European Agency for Aviation Safety, EASA), and U.S. (Federal Aviation Administration, FAA) civil aviation regulations were followed during all UAV operations.
description: This repository contains the original RAW data captured by a drone equipped with RGB, thermal, and multispectral sensors, which were used to obtain the results published in Román et al. 2024 (DOI: https://doi.org/10.3390/rs16020290). The repository also aims to make drone‑acquired datasets from various regions of the world - namely Antarctica, southern Europe, and the United States - readily available for running the pioneering georeferencing and mosaicking algorithm "MosaicSeadron" (https://github.com/SeadroneICMAN/MosaicSeadron/). This open‑source Python algorithm overcomes the main limitations of traditional photogrammetric techniques when working with drones over water surfaces. Indeed, it represents a significant advance in monitoring marine and coastal processes by introducing an innovative, efficient, and accurate workflow that improves local‑scale remote observation of aquatic environments.
description: File List: 6 folders: Dataset 1 - San Pedro river_20230621; Dataset 2 - San Pedro river_20230724; Dataset 3 - Estepona_20230128; Dataset 4 - Guadalquivir river_20230322; Dataset 5 - Whalers Bay_20220130; Dataset 6 - Fumarole Bay_20220201