A Review on Deep Learning in UAV Remote Sensing

By Lucas Prado Osco, José Marcato Junior, Ana Paula Marques Ramos, Lucio Andre de Castro Jorge, Sarah Narges Fatholahi, Jonathan de Andrade Silva, Edson Takashi Matsubara, Hemerson Pistori, Wesley Nunes Gonçalves, Jonathan Li. International Journal of Applied Earth Observation and Geoinformation, 2021

471 citations recorded by the source corpus.

Deep Neural Networks (DNNs) learn representation from data with an impressive capability, and brought important breakthroughs for processing images, time-series, natural language, audio, video, and many others. In the remote sensing field, surveys and literature revisions specifically involving DNNs algorithms' applications have been conducted in an attempt to summarize the amount of information produced in its subfields. Recently, Unmanned Aerial Vehicles (UAV) based applications have dominated aerial sensing research. However, a literature revision that combines both"deep learning"and"UAV remote sensing"thematics has not yet been conducted. The motivation for our work was to present a comprehensive review of the fundamentals of Deep Learning (DL) applied in UAV-based imagery. We focused mainly on describing classification and regression techniques used in recent applications with UAV-acquired data. For that, a total of 232 papers published in international scientific journal databases was examined. We gathered the published material and evaluated their characteristics regarding application, sensor, and technique used. We relate how DL presents promising results and has the potential for processing tasks associated with UAV-based image data. Lastly, we project future perspectives, commentating on prominent DL paths to be explored in the UAV remote sensing field. Our revision consists of a friendly-approach to introduce, commentate, and summarize the state-of-the-art in UAV-based image applications with DNNs algorithms in diverse subfields of remote sensing, grouping it in the environmental, urban, and agricultural contexts.

Subfields: Computer Vision and Pattern Recognition, Environmental Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/6a78801c67313c67e20c76375d6a704964f50abb

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Citations in the corpus

Comparing YOLOv3, YOLOv4 and YOLOv5 for Autonomous Landing Spot Detection in Faulty UAVs (2022, background): “Recently, UAVs have come to dominate aerial sensing research with the use of deep neural networks in the urban, environmental, and agricultural contexts [2].”
The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot (2023, background): “The remote sensing field has experienced impressive advancements in recent years, largely driven by improvements in aerial and orbital platform technologies, sensor capabilities, and computational resources [56, 44].”
Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A survey (2021, background): “Although reviews in [39]– [44] present some DL-based static object detection (SOD) approaches for UAV images, and the one in [45] presents traditional object tracking for UAV video, a complete survey of object and tracking along with the most recent advances is still lacking.”
Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey (2021, background): “learning techniques used in remote sensing [274], [275], [276] and security [277] applications of airborne/satellite communications.”