Recent computer‑vision breakthroughs have driven remote‑sensing capabilities.
Drones, particularly those with RGB cameras, are now common instruments in remote‑sensing workflows.
Optical remote‑sensing literature covers a wide array of tasks and methods, warranting a high‑level synthesis.
The survey compiles key information, including datasets and methodological insights, for newcomers.
No prior survey has presented the field from this overarching, holistic view.
The paper serves as a roadmap for researchers to focus on areas most relevant to their interests.
📖 Full Retelling
The paper titled *A High‑Level Survey of Optical Remote Sensing* was authored by Panagiotis Koletsis, Vasilis Efthymiou, Maria Vakalopoulou, Nikos Komodakis, Anastasios Doulamis, and Georgios Th. Papadopoulos. It was submitted to arXiv on 19 February 2026 and falls under the Computer Vision and Pattern Recognition (cs.CV) and Artificial Intelligence (cs.AI) categories. The study reviews how recent advances in computer vision have accelerated progress in optical remote sensing, especially with the widespread adoption of drones equipped with RGB cameras. The authors catalog the vast literature, datasets, and methodologies that span the field, and aim to provide a comprehensive guide for researchers entering the area, addressing a gap for a holistic perspective that previous surveys have not covered.
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Deep Analysis
Why It Matters
This survey consolidates recent progress in computer vision applied to optical remote sensing, a field that has grown with drone usage. By mapping datasets, tasks, and methods, it offers a roadmap for researchers entering the area. It fills a gap left by previous surveys that focused on narrower topics.
Context & Background
Rapid rise of drones equipped with RGB cameras in industry and research
Advances in computer vision algorithms enabling better image analysis
Existing literature fragmented across specific tasks, lacking a holistic overview
What Happens Next
The paper is expected to become a reference point for new projects and grant proposals. Researchers may use its taxonomy to identify underexplored areas and design datasets. Future work could expand the survey to include multispectral and LiDAR modalities.
Frequently Asked Questions
What is the main contribution of the survey?
It provides a comprehensive, high-level overview of optical remote sensing tasks, datasets, and methods, serving as a guide for newcomers.
Who are the target readers?
Researchers and practitioners in computer vision and remote sensing looking for a consolidated reference.
Will the survey be updated?
Future versions may incorporate emerging modalities and new datasets as the field evolves.
How can I access the full paper?
The paper is available on arXiv with the DOI 10.48550/arXiv.2602.17397 and can be downloaded as PDF or HTML.
Original Source
--> Computer Science > Computer Vision and Pattern Recognition arXiv:2602.17397 [Submitted on 19 Feb 2026] Title: A High-Level Survey of Optical Remote Sensing Authors: Panagiotis Koletsis , Vasilis Efthymiou , Maria Vakalopoulou , Nikos Komodakis , Anastasios Doulamis , Georgios Th. Papadopoulos View a PDF of the paper titled A High-Level Survey of Optical Remote Sensing, by Panagiotis Koletsis and 5 other authors View PDF HTML Abstract: In recent years, significant advances in computer vision have also propelled progress in remote sensing. Concurrently, the use of drones has expanded, with many organizations incorporating them into their operations. Most drones are equipped by default with RGB cameras, which are both robust and among the easiest sensors to use and interpret. The body of literature on optical remote sensing is vast, encompassing diverse tasks, capabilities, and methodologies. Each task or methodology could warrant a dedicated survey. This work provides a comprehensive overview of the capabilities of the field, while also presenting key information, such as datasets and insights. It aims to serve as a guide for researchers entering the field, offering high-level insights and helping them focus on areas most relevant to their interests. To the best of our knowledge, no existing survey addresses this holistic perspective. Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2602.17397 [cs.CV] (or arXiv:2602.17397v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2602.17397 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Panagiotis Koletsis [ view email ] [v1] Thu, 19 Feb 2026 14:26:26 UTC (371 KB) Full-text links: Access Paper: View a PDF of the paper titled A High-Level Survey of Optical Remote Sensing, by Panagiotis Koletsis and 5 other authors View PDF HTML TeX Source view license Current browse context: cs.CV < prev | next > new | recen...
