mlsanityData to 14 Oct 2026

A Review on Deep Learning in UAV Remote Sensing

arXiv 2101.10861 ↗First posted 22 Jan 2021cs.CV cs.AIcited by 32

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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.

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

95 of 241 references.

References · 95

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Citing papers per year: 2021: 2; 2022: 5; 2023: 10; 2024: 7; 2025: 6; 2026: 2.

32 papers.

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  1. 2023The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shotcited by 83
  2. 2023Semantic Segmentation using Vision Transformers: A surveycited by 73
  3. 2023VDD: Varied Drone Dataset for Semantic Segmentationcited by 40
  4. 2021Deep Learning for UAV-based Object Detection and Tracking: A Surveycited by 33
  5. 2025A Survey on Remote Sensing Foundation Models: From Vision to Multimodalitycited by 19
  6. 2023The Potential of Visual ChatGPT For Remote Sensingcited by 10
  7. 2023An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mappingcited by 8
  8. 2022AiTLAS: Artificial Intelligence Toolbox for Earth Observationcited by 7
  9. 2024PalmProbNet: A Probabilistic Approach to Understanding Palm Distributions in Ecuadorian Tropical Forest via Transfer Learningcited by 5
  10. 2024Navigation in a simplified Urban Flow through Deep Reinforcement Learningcited by 5
  11. 2024UEMM-Air: Make Unmanned Aerial Vehicles Perform More Multi-modal Taskscited by 4
  12. 2022Investigation of Factorized Optical Flows as Mid-Level Representationscited by 2
20 more
  1. 2022LOANet: A Lightweight Network Using Object Attention for Extracting Buildings and Roads from UAV Aerial Remote Sensing Imagescited by 2
  2. 2024An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo datasetcited by 2
  3. 2024Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images using SegFormercited by 2
  4. 2025Machine learning applications in archaeological practices: a reviewcited by 2
  5. 2025AD-Det: Boosting Object Detection in UAV Images with Focused Small Objects and Balanced Tail Classescited by 2
  6. 2025FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Reviewcited by 2
  7. 2025Unlocking Thermal Aerial Imaging: Synthetic Enhancement of UAV Datasetscited by 2
  8. 2021A Deep Learning Approach Based on Graphs to Detect Plantation Linescited by 1
  9. 2022Exploiting Digital Surface Models for Inferring Super-Resolution for Remotely Sensed Imagescited by 1
  10. 2023A generic self-supervised learning (SSL) framework for representation learning from spectra-spatial feature of unlabeled remote sensing imagerycited by 1
  11. 2023A Comprehensive Review on Tree Detection Methods Using Point Cloud and Aerial Imagery from Unmanned Aerial Vehiclescited by 1
  12. 2024DeepAAT: Deep Automated Aerial Triangulation for Fast UAV-based Mappingcited by 1
  13. 2024Deep Learning Enhanced Road Traffic Analysis: Scalable Vehicle Detection and Velocity Estimation Using PlanetScope Imagerycited by 1
  14. 2026A Review of Vision-Based Vehicle Detection for UAV-Based Traffic Monitoring: Experimental Insights and Future Directionscited by 1
  15. 2022Land Classification in Satellite Images by Injecting Traditional Features to CNN Modelscited by 0
  16. 2023RemoteNet: Remote Sensing Image Segmentation Network based on Global-Local Informationcited by 0
  17. 2023Automatic detection of aerial survey ground control points based on Yolov5-OBBcited by 0
  18. 2023SuperpixelGraph: Semi-automatic generation of building footprint through semantic-sensitive superpixel and neural graph networkscited by 0
  19. 2025Navigation in a Three-Dimensional Urban Flow using Deep Reinforcement Learningcited by 0
  20. 2026Mixture-of-Experts in Remote Sensing: A Surveycited by 0