UAV in the advent of the twenties: Where we stand and what is next

• Published in: ISPRS journal of photogrammetry and remote sensing Vol. 184; pp. 215 - 242
• Main Authors: Nex, F., Armenakis, C., Cramer, M., Cucci, D.A., Gerke, M., Honkavaara, E., Kukko, A., Persello, C., Skaloud, J.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-02-2022
• Subjects: Data processing; Deep learning; Hyperspectral; LiDAR; Navigation; Photogrammetry; Remote sensing; Sensors; UAV; Deep learning; Navigation; Hyperspectral; LiDAR; Data processing; UAV; Sensors; Photogrammetry; Remote sensing
• ISSN: 0924-2716; 1872-8235
• DOI: 10.1016/j.isprsjprs.2021.12.006

The use of Unmanned Aerial Vehicles (UAVs) has surged in the last two decades, making them popular instruments for a wide range of applications, and leading to a remarkable number of scientific contributions in geoscience, remote sensing and engineering. However, the development of best practices for high quality of UAV mapping are often overlooked representing a drawback for their wider adoption. UAV solutions then require an inter-disciplinary research, integrating different expertise and combining several hardware and software components on the same platform. Despite the high number of peer-reviewed papers on UAVs, little attention has been given to the interaction between research topics from different domains (such as robotics and computer vision) that impact the use of UAV in remote sensing. The aim of this paper is to (i) review best practices for the use of UAVs for remote sensing and mapping applications and (ii) report on current trends - including adjacent domains - for UAV use and discuss their future impact in photogrammetry and remote sensing. Hardware developments, navigation and acquisition strategies, and emerging solutions for data processing in innovative applications are considered in this analysis. As the number and the heterogeneity of debated topics are large, the paper is organized according to very specific questions considered most relevant by the authors.