# A Review on Deep Learning in UAV Remote Sensing

arXiv: https://arxiv.org/abs/2101.10861 · HTML: https://mlsanity.com/paper/2101.10861/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 95 of its 241 references that are in the map.
- First posted on arXiv 22 Jan 2021.

## Builds on (most cited first, 30 of 95)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)

## Built on by (most cited first, 30 of 32)

- [The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot](https://mlsanity.com/paper/2306.16623/) (2023)
- [Semantic Segmentation using Vision Transformers: A survey](https://mlsanity.com/paper/2305.03273/) (2023)
- [VDD: Varied Drone Dataset for Semantic Segmentation](https://mlsanity.com/paper/2305.13608/) (2023)
- [Deep Learning for UAV-based Object Detection and Tracking: A Survey](https://mlsanity.com/paper/2110.12638/) (2021)
- [A Survey on Remote Sensing Foundation Models: From Vision to Multimodality](https://mlsanity.com/paper/2503.22081/) (2025)
- [The Potential of Visual ChatGPT For Remote Sensing](https://mlsanity.com/paper/2304.13009/) (2023)
- [An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping](https://mlsanity.com/paper/2302.03347/) (2023)
- [AiTLAS: Artificial Intelligence Toolbox for Earth Observation](https://mlsanity.com/paper/2201.08789/) (2022)
- [PalmProbNet: A Probabilistic Approach to Understanding Palm Distributions in Ecuadorian Tropical Forest via Transfer Learning](https://mlsanity.com/paper/2403.03161/) (2024)
- [Navigation in a simplified Urban Flow through Deep Reinforcement Learning](https://mlsanity.com/paper/2409.17922/) (2024)
- [UEMM-Air: Make Unmanned Aerial Vehicles Perform More Multi-modal Tasks](https://mlsanity.com/paper/2406.06230/) (2024)
- [Investigation of Factorized Optical Flows as Mid-Level Representations](https://mlsanity.com/paper/2203.04927/) (2022)
- [LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Images](https://mlsanity.com/paper/2212.08490/) (2022)
- [An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset](https://mlsanity.com/paper/2402.12522/) (2024)
- [Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images using SegFormer](https://mlsanity.com/paper/2410.01092/) (2024)
- [Machine learning applications in archaeological practices: a review](https://mlsanity.com/paper/2501.03840/) (2025)
- [AD-Det: Boosting Object Detection in UAV Images with Focused Small Objects and Balanced Tail Classes](https://mlsanity.com/paper/2504.05601/) (2025)
- [FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review](https://mlsanity.com/paper/2506.03938/) (2025)
- [Unlocking Thermal Aerial Imaging: Synthetic Enhancement of UAV Datasets](https://mlsanity.com/paper/2507.06797/) (2025)
- [A Deep Learning Approach Based on Graphs to Detect Plantation Lines](https://mlsanity.com/paper/2102.03213/) (2021)
- [Exploiting Digital Surface Models for Inferring Super-Resolution for Remotely Sensed Images](https://mlsanity.com/paper/2205.04056/) (2022)
- [A generic self-supervised learning (SSL) framework for representation learning from spectra-spatial feature of unlabeled remote sensing imagery](https://mlsanity.com/paper/2306.15836/) (2023)
- [A Comprehensive Review on Tree Detection Methods Using Point Cloud and Aerial Imagery from Unmanned Aerial Vehicles](https://mlsanity.com/paper/2309.16375/) (2023)
- [DeepAAT: Deep Automated Aerial Triangulation for Fast UAV-based Mapping](https://mlsanity.com/paper/2402.01134/) (2024)
- [Deep Learning Enhanced Road Traffic Analysis: Scalable Vehicle Detection and Velocity Estimation Using PlanetScope Imagery](https://mlsanity.com/paper/2410.14698/) (2024)
- [A Review of Vision-Based Vehicle Detection for UAV-Based Traffic Monitoring: Experimental Insights and Future Directions](https://mlsanity.com/paper/2608.07571/) (2026)
- [Land Classification in Satellite Images by Injecting Traditional Features to CNN Models](https://mlsanity.com/paper/2207.10368/) (2022)
- [RemoteNet: Remote Sensing Image Segmentation Network based on Global-Local Information](https://mlsanity.com/paper/2302.13084/) (2023)
- [Automatic detection of aerial survey ground control points based on Yolov5-OBB](https://mlsanity.com/paper/2303.03041/) (2023)
- [SuperpixelGraph: Semi-automatic generation of building footprint through semantic-sensitive superpixel and neural graph networks](https://mlsanity.com/paper/2304.05661/) (2023)

## Abstract (arXiv)

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.