Open Access
Article
SciPap-2635
When Can UAV RGB Data Be Sufficient? A Problem-Oriented Evidence Synthesis For Sensor Selection
1 Faculty of Economics and Administration, Institute of System Engineering and Informatics, University of Pardubice, Studenská 84, Pardubice 53210, Czechia
2 Faculty of Economics and Administration, Institute of System Engineering and Informatics, University of Pardubice, Studenská 84, Pardubice 53210, Czechia
3 School of Social and Political Science, University of Edinburgh, 15a George Square, Edinburgh EH89LN, United Kingdom of Great Britain and Northern Ireland
* Authors to whom correspondence should be addressed.
Abstract: This paper examines when red-green-blue (RGB) data acquired by unmanned aerial vehicles (UAVs) provide sufficient information for monitoring, mapping, measurement, and diagnostic decisions and when additional sensing modalities are justified. The five CORINE Land Cover Level-1 classes are used to structure the problem-oriented evaluation of evidence: Artificial Surfaces, Agricultural Areas, Forest and Semi-natural Areas, Wetlands, and Water Bodies. Across these classes, RGB is most defensible when the required information is directly represented by visible colour, texture, morphology, spatial arrangement, or photogrammetrically accessible surface geometry. RGB can also support quantitative inference through locally validated visible or structural proxies, although such applications are commonly conditionally sufficient. Its main limitations arise when decisions depend on physical information not reliably represented in visible imagery, including temperature, pre-visual spectral responses, concealed terrain, or subsurface conditions. Additional sensing is most feasible when it resolves a specific information deficit or provides a meaningful improvement in robustness, transferability, or decision accuracy. The synthesis identifies four recurring information pathways-direct visible information, photogrammetric geometry, validated proxy inference, and information outside RGB observability. The synthesis thus provides an evidence-based foundation for a new decision framework for deciding whether RGB is sufficient and when complementary sensing is required
Keywords: Uav, Rgb Imagery, Sensor Selection, Rgb Sufficiency, Decision Framework
JEL classification: C89 - Other, O13 - Agriculture • Natural Resources • Energy • Environment • Other Primary Products, R14 - Land Use Patterns
Received: 25 August 2026 / Revised: 30 August 2026 / Accepted: 31 August 2026 / Published: 31 August 2026
