The DOI matches its Crossref record; 200 of the 200 references it cites were found in a registry or an index.
A review on deep learning in UAV remote sensing
Open accessAbstract
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 Vehicle (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 the 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 materials and evaluated their characteristics regarding the application, sensor, and technique used. We discuss 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. This revision consisting of an 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.
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Cites
1 retracted
Zhu, R., Li, Y., Mo, N., & Liu, Y. (2019) · Remote Sensing · doi:10.3390/rs11171996 · Cited by 45
RetractedDeep Residual Learning for Image Recognition
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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Azari, M. M., Solanki, S., Chatzinotas, S., Kodheli, O., Sallouha, H., Colpaert, A., Mendoza Montoya, J. F., Pollin, S., Haqiqatnejad, A., Mostaani, A., et al. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3199901 · Cited by 668
Deep learning for unmanned aerial vehicle-based object detection and tracking: a survey
Wu, X., Li, W., Hong, D., Tao, R., & Du, Q. (2022) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2021.3115137 · Cited by 421
A Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs)
Telli, K., Kraa, O., Himeur, Y., Ouamane, A., Boumehraz, M., Atalla, S., & Mansoor, W. (2023) · Systems · doi:10.3390/systems11080400 · Cited by 331
Semantic segmentation using Vision Transformers: A survey
Thisanke, H., Deshan, C., Chamith, K., Seneviratne, S., Vidanaarachchi, R., & Herath, D. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106669 · Cited by 288
A Review of Digital Twin Applications in Civil and Infrastructure Emergency Management
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Metadata
- Authors
- Osco, L. P., Marcato Junior, J., Marques Ramos, A. P., de Castro Jorge, L. A., Fatholahi, S. N., de Andrade Silva, J., Matsubara, E. T., Pistori, H., Gonçalves, W. N., & Li, J.
- Venue
- International Journal of Applied Earth Observation and Geoinformation
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 102
- Pages
- 102456
- ISSN
- 0303-2434 (ISSN-L)
- 1569-8432
- OpenAlex
- W3125664696
- Cited by
- 458
- References
- 200
- FWCI
- 35.59
- Type
- journal article
- Language
- en
- Open access
- gold
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- The paper
- Osco, L. P., Marcato Junior, J., Marques Ramos, A. P., de Castro Jorge, L. A., Fatholahi, S. N., de Andrade Silva, J., Matsubara, E. T., Pistori, H., Gonçalves, W. N., & Li, J. (2021). A review on deep learning in UAV remote sensing. International Journal of Applied Earth Observation and Geoinformation. https://doi.org/10.1016/j.jag.2021.102456
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