RGB and near-infrared aerial imagery dataset of a forested area with RTK-referenced UAV trajectories for visual SLAM and multispectral navigation development and benchmarking
DOI: https://doi.org/10.3846/aviation.2026.28129Abstract
Visual simultaneous localization and mapping (SLAM) is increasingly utilized for autonomous unmanned aerial vehicle operations in GNSS-degraded environments. However, there are limited public datasets that provide multispectral aerial imagery alongside the centimeter-level trajectory ground truth which is necessary for such algorithms, particularly in forested areas. This paper presents a dataset that addresses this deficiency. The study validates both the acquisition methodology and the benchmark quality of the resulting data. A custom RTK-referenced UAV was employed to capture time-synchronized RGB and near-infrared imagery over a 2.51 ha mixed coniferous-deciduous forest polygon in the Vilnius district of Lithuania, conducted across seven sessions in spring 2026 to reveal inter-day variations in illumination and weather conditions. Two intentionally distinct sets of cameras were utilized: an economical rolling-shutter camera pair and a global-shutter camera pair facilitate the evaluation of the same algorithm across varying shutter regimes and radiometric setups. Each session combines a photogrammetric grid mission with linear and diagonal localization transects at 80 meters above ground level. The release includes 629 raw and rectified RGB-NIR image pairs, intrinsic and stereo calibration, comprehensive per-frame telemetry, per-session METAR records, and mission waypoints, facilitating reproducible benchmarking of visual SLAM, visual odometry, and multispectral navigation in forested environments.
Keywords:
multispectral imagery, near-infrared, unmanned aerial vehicle, visual simultaneous localization and mapping, real-time kinematic positioning, dataset, forest remote sensing, normalised difference vegetation index, navigation algorithm benchmarkHow to Cite
Share
License
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.

This work is licensed under a Creative Commons Attribution 4.0 International License.
References
AllMetSat. (2026). METAR and TAF reports for Vilnius Airport (EYVI), Lithuania. https://en.allmetsat.com/metar-taf/estonia-latvia-lithuania.php?icao=EYVI
Baniulis, R., Galinauskas, K., Marozas, L., Paršeliūnas, E., & Petniūnas, M. (2017). An analysis of RTK network LitPOS performance. In 10th International Conference on Environmental Engineering (ICEE 2017) (Vol. 10, pp. 1–9). Vilnius TECH Conferences. https://doi.org/10.3846/enviro.2017.161
Bouguet, J.-Y. (2003). Camera calibration toolbox for MATLAB. https://robots.stanford.edu/cs223b04/JeanYvesCalib/
Bradski, G. (2000). The OpenCV library. Dr. Dobb’s Journal of Software Tools, 25(11), 120–125.
Cai, Y., Lam, E., Howlett, T., & Cai, A. (2020). Spatiotemporal analysis of “jello effect” in drone videos. In J. Chen (Rds), Advances in Human Factors in Robots and Unmaned Systems. AHFE 2019. Advances in Intelligent Systems and Computing (Vol. 962, pp. 197–207). Springer. https://doi.org/10.1007/978-3-030-20467-9_18
Campos, C., Elvira, R., Rodríguez, J. J. G., Montiel, J. M. M., & Tardós, J. D. (2021). ORB-SLAM3: An accurate open-source library for visual, visual-inertial, and multimap SLAM. IEEE Transactions on Robotics, 37(6), 1874–1890. https://doi.org/10.1109/TRO.2021.3075644
Esri. (2026). World imagery [Map]. https://developers.arcgis.com/documentation/security-and-authentication/
Fonseka, C. L. I. S., Halloluwa, T., Hewagamage, K. P., Rathnayake, U., & Bandara, R. M. U. S. (2024). A dataset of unmanned aerial vehicle multispectral images acquired over a field to identify nitrogen requirements. Data in Brief, 54, Article 110479. https://doi.org/10.1016/j.dib.2024.110479
Hartley, R., & Zisserman, A. (2004). Multiple view geometry in computer vision (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511811685
Kazemi, F., & Parmehr, E. G. (2023). Evaluation of RGB vegetation indices derived from UAV images for rice crop growth monitoring. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-4/W1-2022, 385–390. https://doi.org/10.5194/isprs-annals-X-4-W1-2022-385-2023
Kim, H., Hyun, C.-U., Park, H.-D., & Cha, J. (2023). Image mapping accuracy evaluation using UAV with standalone, differential (RTK), and PPP GNSS positioning techniques in an abandoned mine site. Sensors, 23(13), Article 5858. https://doi.org/10.3390/s23135858
Kurokesu. (2026). Optical filters for cameras: IR-cut and NIR long-pass. https://www.kurokesu.com/item/FILT
Lietuvos Respublikos aplinkos ministerija. (2016). Miško savininko elementorius [Forest owner’s primer] (3rd ed.). Baltijos kopija.
LitPOS. (2026). LitPOS service: Lithuanian permanent GNSS reference station network. https://www.geoportal.lt/geoportal/web/litpos-paslauga
Liu, Y., Feng, Z., Zhang, H., & Dong, W. (2025). Post-integration based point-line feature visual SLAM in low-texture environments. Scientific Reports, 15, Article 14606. https://doi.org/10.1038/s41598-025-97250-6
OmniVision Technologies. (2009). OV5647: 5-megapixel CMOS image sensor [Data sheet]. https://cdn.sparkfun.com/datasheets/Dev/RaspberryPi/ov5647_full.pdf
Park, Y., & Jeon, B. (2020). An acquisition method for visible and near infrared images from single CMYG color filter array-based sensor. Sensors, 20(19), Article 5578. https://doi.org/10.3390/s20195578
Sa, I., Popović, M., Khanna, R., Chen, Z., Lottes, P., Liebisch, F., Nieto, J., Stachniss, C., Walter, A., & Siegwart, R. (2018). WeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming. Remote Sensing, 10(9), Article 1423. https://doi.org/10.3390/rs10091423
Vélez, S., Ariza-Sentís, M., & Valente, J. (2023). Dataset on unmanned aerial vehicle multispectral images acquired over a vineyard affected by Botrytis cinerea in northern Spain. Data in Brief, 46, Article 108876. https://doi.org/10.1016/j.dib.2022.108876
Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330–1334. https://doi.org/10.1109/34.888718
View article in other formats
Published
Issue
Section
Copyright
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
License

This work is licensed under a Creative Commons Attribution 4.0 International License.