Object Detection in Optical Remote Sensing Images: A Systematic Review of Methods, Benchmarks, and Operational Applications.
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| Title: | Object Detection in Optical Remote Sensing Images: A Systematic Review of Methods, Benchmarks, and Operational Applications. |
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| Authors: | Fontanet Garcia, Neus1 (AUTHOR), Boccardo, Piero1 (AUTHOR) piero.boccardo@polito.it |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1289. 26p. |
| Subjects: | Optical remote sensing, Deep learning, Spatial variation, Real-time computing, Object recognition (Computer vision), Annotations, Benchmark problems (Computer science) |
| Abstract: | Highlights: What are the main findings? Deep learning methods achieve the highest detection accuracy (65–80% mAP) on standard remote sensing benchmarks, yet no single approach dominates across all performance dimensions—classical methods retain significant value for limited-data, interpretability, and resource-constrained scenarios. The three most critical operational bottlenecks identified are annotation cost (labour-intensive dense labelling), extreme scale variation spanning 2–3 orders of magnitude within single scenes, and domain adaptation failures causing 15–40% performance degradation across geographic regions. What are the implications of the main findings? Foundation models enabling zero-shot detection and weakly-supervised learning represent the highest-priority research directions, with the potential to reduce annotation requirements by up to 90% for time-critical applications such as disaster response. Hybrid architectures integrating deep learning with Object-Based Image Analysis and knowledge-based reasoning achieve 5–12% accuracy improvements over pure pixel-based CNNs, suggesting the future lies in complementary integration rather than single-methodology dominance. Object detection in optical remote sensing imagery has emerged as a crucial task in computer vision, with applications ranging between environmental monitoring to disaster management, precision agriculture, and urban planning. This review systematically examines current methodologies, categorising them into four principal approaches: (1) template matching-based methods, which leverage predefined patterns for object identification; (2) knowledge-based methods, which incorporate geometric and contextual information to enhance detection accuracy; (3) object-based image analysis (OBIA), which segments images into meaningful objects using spectral and spatial properties; (4) machine learning-based methods, particularly deep convolutional neural networks (CNNs), which have revolutionised the field through automatic feature learning. Each methodology's performance characteristics, computational requirements, and suitability for different remote sensing applications are analysed. Our systematic review, following PRISMA guidelines, analysed 189 studies published from 2010 to 2025, of which 73 provided quantitative results on standard benchmarks. The three most critical challenges identified are as follows: (1) annotation bottleneck, as dense bounding box labelling of remote sensing imagery remains highly labour-intensive for deep learning approaches, (2) extreme scale variation spanning 2–3 orders of magnitude within single scenes, and (3) domain adaptation failures when models encounter new geographic regions or sensor characteristics. This review identifies critical research gaps and proposes prioritised future directions, emphasising foundation models for zero-shot detection, efficient architectures for resource-constrained deployment, and standardised benchmarks with size-specific metrics. The analysis provides practitioners with evidence-based decision frameworks for method selection and researchers with a roadmap for advancing object detection in remote sensing applications. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 193715320 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Object Detection in Optical Remote Sensing Images: A Systematic Review of Methods, Benchmarks, and Operational Applications. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Fontanet+Garcia%2C+Neus%22">Fontanet Garcia, Neus</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Boccardo%2C+Piero%22">Boccardo, Piero</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> piero.boccardo@polito.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 9, p1289. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Optical+remote+sensing%22">Optical remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+variation%22">Spatial variation</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Annotations%22">Annotations</searchLink><br /><searchLink fieldCode="DE" term="%22Benchmark+problems+%28Computer+science%29%22">Benchmark problems (Computer science)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? Deep learning methods achieve the highest detection accuracy (65–80% mAP) on standard remote sensing benchmarks, yet no single approach dominates across all performance dimensions—classical methods retain significant value for limited-data, interpretability, and resource-constrained scenarios. The three most critical operational bottlenecks identified are annotation cost (labour-intensive dense labelling), extreme scale variation spanning 2–3 orders of magnitude within single scenes, and domain adaptation failures causing 15–40% performance degradation across geographic regions. What are the implications of the main findings? Foundation models enabling zero-shot detection and weakly-supervised learning represent the highest-priority research directions, with the potential to reduce annotation requirements by up to 90% for time-critical applications such as disaster response. Hybrid architectures integrating deep learning with Object-Based Image Analysis and knowledge-based reasoning achieve 5–12% accuracy improvements over pure pixel-based CNNs, suggesting the future lies in complementary integration rather than single-methodology dominance. Object detection in optical remote sensing imagery has emerged as a crucial task in computer vision, with applications ranging between environmental monitoring to disaster management, precision agriculture, and urban planning. This review systematically examines current methodologies, categorising them into four principal approaches: (1) template matching-based methods, which leverage predefined patterns for object identification; (2) knowledge-based methods, which incorporate geometric and contextual information to enhance detection accuracy; (3) object-based image analysis (OBIA), which segments images into meaningful objects using spectral and spatial properties; (4) machine learning-based methods, particularly deep convolutional neural networks (CNNs), which have revolutionised the field through automatic feature learning. Each methodology's performance characteristics, computational requirements, and suitability for different remote sensing applications are analysed. Our systematic review, following PRISMA guidelines, analysed 189 studies published from 2010 to 2025, of which 73 provided quantitative results on standard benchmarks. The three most critical challenges identified are as follows: (1) annotation bottleneck, as dense bounding box labelling of remote sensing imagery remains highly labour-intensive for deep learning approaches, (2) extreme scale variation spanning 2–3 orders of magnitude within single scenes, and (3) domain adaptation failures when models encounter new geographic regions or sensor characteristics. This review identifies critical research gaps and proposes prioritised future directions, emphasising foundation models for zero-shot detection, efficient architectures for resource-constrained deployment, and standardised benchmarks with size-specific metrics. The analysis provides practitioners with evidence-based decision frameworks for method selection and researchers with a roadmap for advancing object detection in remote sensing applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18091289 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1289 Subjects: – SubjectFull: Optical remote sensing Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Spatial variation Type: general – SubjectFull: Real-time computing Type: general – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Annotations Type: general – SubjectFull: Benchmark problems (Computer science) Type: general Titles: – TitleFull: Object Detection in Optical Remote Sensing Images: A Systematic Review of Methods, Benchmarks, and Operational Applications. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Fontanet Garcia, Neus – PersonEntity: Name: NameFull: Boccardo, Piero IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 9 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
