A Review on Deep Learning in UAV Remote Sensing
Lucas Prado Osco, Jos\'e Marcato Junior, Ana Paula Marques Ramos,, L\'ucio Andr\'e de Castro Jorge, Sarah Narges Fatholahi, Jonathan de Andrade, Silva, Edson Takashi Matsubara, Hemerson Pistori, Wesley Nunes Gon\c{c}alves,, Jonathan Li

TL;DR
This paper provides a comprehensive review of deep learning applications in UAV remote sensing, summarizing recent classification and regression techniques used on UAV-acquired imagery across various environmental, urban, and agricultural contexts.
Contribution
It is the first review to specifically combine deep learning and UAV remote sensing, analyzing 232 papers to summarize current methods and future directions.
Findings
Deep learning shows promising results in UAV image processing.
Most applications focus on classification and regression tasks.
Future research paths include advanced deep learning techniques for UAV data.
Abstract
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…
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