msuav500k: Foundational dataset for multispectral and RGB uncrewed aerial vehicle imagery

This dataset comprises a curated collection of UAV RGB and multispectral imagery sourced from multiple open-access platforms. It contains the largest set of UAV imagery covering at least four spectral bands (Green, Red, RedEdge, and Near-Infrared) and is available as either orthomosaic or raw sensor data. A systematic calibration process was employed to ensure radiometric consistency across the diverse sensor types (DJI Mavic 3 M, DJI Phantom 4 Multispectral, Parrot Sequoia, MicaSense RedEdge, and MicaSense Altum(PT)). This involved correcting raw digital numbers (DNs) for sensor-specific variables (e.g., black level, vignetting) and normalizing pixel values with internal sunlight sensors. The resultant imagery is provided as UINT8 5-channel multispectral TIFF files and RGB 3-channel JPG files for robust cross-study comparison. Comprehensive metadata, processing scripts, and calibration details are publicly available in the accompanying repository folder. This dataset offers a valuable resource for researchers and practitioners seeking consistent, high-quality UAV multispectral data to train or fine-tune foundational models in computer vision and remote sensing applications.

Authors

Institutions

Publication Details

Journal
Data in Brief
Published
2025-10-10
DOI
https://doi.org/10.1016/j.dib.2025.112128
Citations
2
Primary Topic
Advanced Image Fusion Techniques
Type
article
Field-Weighted Citation Impact
2.27

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

msuav500k: Foundational dataset for multispectral and RGB uncrewed aerial vehicle imagery

Önder Babur, Jurrian Doornbos
2 citations
Data in Brief
Advanced Image Fusion Techniques
2.27
article

msuav500k: Foundational dataset for multispectral and RGB uncrewed aerial vehicle imagery

Önder Babur, Jurrian Doornbos
article en
2 citations

Abstract

This dataset comprises a curated collection of UAV RGB and multispectral imagery sourced from multiple open-access platforms. It contains the largest set of UAV imagery covering at least four spectral bands (Green, Red, RedEdge, and Near-Infrared) and is available as either orthomosaic or raw sensor data. A systematic calibration process was employed to ensure radiometric consistency across the diverse sensor types (DJI Mavic 3 M, DJI Phantom 4 Multispectral, Parrot Sequoia, MicaSense RedEdge, and MicaSense Altum(PT)). This involved correcting raw digital numbers (DNs) for sensor-specific variables (e.g., black level, vignetting) and normalizing pixel values with internal sunlight sensors. The resultant imagery is provided as UINT8 5-channel multispectral TIFF files and RGB 3-channel JPG files for robust cross-study comparison. Comprehensive metadata, processing scripts, and calibration details are publicly available in the accompanying repository folder. This dataset offers a valuable resource for researchers and practitioners seeking consistent, high-quality UAV multispectral data to train or fine-tune foundational models in computer vision and remote sensing applications.

Data in BriefVol. 63
Eindhoven University of Technology (NL), Wageningen University & Research (NL)
European Commission
Openalex Percentile: Top 9%
Advanced Image Fusion Techniques
2.27
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.