A Review of Practical AI for Remote Sensing in Earth Sciences
Integrating Artificial Intelligence (AI) techniques with remote sensing holds great potential for revolutionizing data analysis and applications in many domains of Earth sciences. This review paper synthesizes the existing literature on AI applications in remote sensing, consolidating and analyzing...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/16/4112 |
| _version_ | 1797583275686887424 |
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| author | Bhargavi Janga Gokul Prathin Asamani Ziheng Sun Nicoleta Cristea |
| author_facet | Bhargavi Janga Gokul Prathin Asamani Ziheng Sun Nicoleta Cristea |
| author_sort | Bhargavi Janga |
| collection | DOAJ |
| description | Integrating Artificial Intelligence (AI) techniques with remote sensing holds great potential for revolutionizing data analysis and applications in many domains of Earth sciences. This review paper synthesizes the existing literature on AI applications in remote sensing, consolidating and analyzing AI methodologies, outcomes, and limitations. The primary objectives are to identify research gaps, assess the effectiveness of AI approaches in practice, and highlight emerging trends and challenges. We explore diverse applications of AI in remote sensing, including image classification, land cover mapping, object detection, change detection, hyperspectral and radar data analysis, and data fusion. We present an overview of the remote sensing technologies, methods employed, and relevant use cases. We further explore challenges associated with practical AI in remote sensing, such as data quality and availability, model uncertainty and interpretability, and integration with domain expertise as well as potential solutions, advancements, and future directions. We provide a comprehensive overview for researchers, practitioners, and decision makers, informing future research and applications at the exciting intersection of AI and remote sensing. |
| first_indexed | 2024-03-10T23:36:35Z |
| format | Article |
| id | doaj.art-5c0a7084e44a4d51a2228551de3bf877 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T23:36:35Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-5c0a7084e44a4d51a2228551de3bf8772023-11-19T02:54:43ZengMDPI AGRemote Sensing2072-42922023-08-011516411210.3390/rs15164112A Review of Practical AI for Remote Sensing in Earth SciencesBhargavi Janga0Gokul Prathin Asamani1Ziheng Sun2Nicoleta Cristea3Center for Spatial Information Science and Systems, College of Science, George Mason University, 4400 University Drive, MSN 6E1, Fairfax, VA 22030, USACenter for Spatial Information Science and Systems, College of Science, George Mason University, 4400 University Drive, MSN 6E1, Fairfax, VA 22030, USACenter for Spatial Information Science and Systems, College of Science, George Mason University, 4400 University Drive, MSN 6E1, Fairfax, VA 22030, USADepartment of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USAIntegrating Artificial Intelligence (AI) techniques with remote sensing holds great potential for revolutionizing data analysis and applications in many domains of Earth sciences. This review paper synthesizes the existing literature on AI applications in remote sensing, consolidating and analyzing AI methodologies, outcomes, and limitations. The primary objectives are to identify research gaps, assess the effectiveness of AI approaches in practice, and highlight emerging trends and challenges. We explore diverse applications of AI in remote sensing, including image classification, land cover mapping, object detection, change detection, hyperspectral and radar data analysis, and data fusion. We present an overview of the remote sensing technologies, methods employed, and relevant use cases. We further explore challenges associated with practical AI in remote sensing, such as data quality and availability, model uncertainty and interpretability, and integration with domain expertise as well as potential solutions, advancements, and future directions. We provide a comprehensive overview for researchers, practitioners, and decision makers, informing future research and applications at the exciting intersection of AI and remote sensing.https://www.mdpi.com/2072-4292/15/16/4112Artificial Intelligenceremote sensing technologydeep learningLiDARimage classificationobject detection |
| spellingShingle | Bhargavi Janga Gokul Prathin Asamani Ziheng Sun Nicoleta Cristea A Review of Practical AI for Remote Sensing in Earth Sciences Remote Sensing Artificial Intelligence remote sensing technology deep learning LiDAR image classification object detection |
| title | A Review of Practical AI for Remote Sensing in Earth Sciences |
| title_full | A Review of Practical AI for Remote Sensing in Earth Sciences |
| title_fullStr | A Review of Practical AI for Remote Sensing in Earth Sciences |
| title_full_unstemmed | A Review of Practical AI for Remote Sensing in Earth Sciences |
| title_short | A Review of Practical AI for Remote Sensing in Earth Sciences |
| title_sort | review of practical ai for remote sensing in earth sciences |
| topic | Artificial Intelligence remote sensing technology deep learning LiDAR image classification object detection |
| url | https://www.mdpi.com/2072-4292/15/16/4112 |
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