PLA4MS: Curated Georeferenced Dataset for Cloud Removal in Remote Sensing
The presence of meticulously curated extensive training datasets plays a crucial role in advancing the performance of deep learning techniques that generalize well for extracting geoinformation from multisensor remote sensing imagery. Despite numerous datasets being published by the research communi...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10949711/ |
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| author | Sushil Ghildiyal Ashutosh Kumar Neeraj Goel Mukesh Saini Abdulmotaleb El Saddik |
| author_facet | Sushil Ghildiyal Ashutosh Kumar Neeraj Goel Mukesh Saini Abdulmotaleb El Saddik |
| author_sort | Sushil Ghildiyal |
| collection | DOAJ |
| description | The presence of meticulously curated extensive training datasets plays a crucial role in advancing the performance of deep learning techniques that generalize well for extracting geoinformation from multisensor remote sensing imagery. Despite numerous datasets being published by the research community, a substantial portion of them are hampered by significant constraints, such as low spatial resolution, lack of ground details over time, and insufficient sample quantity. This article utilizes the openly accessible data obtained from the Planetscope satellites managed by Planet Labs. We have curated a dataset called PLA4MS comprising 64 557 pairs of images depicting cloudy and cloud-free conditions. The study focuses on the Ropar region of Punjab, India, as the primary area of interest, ensuring precise georeferencing at a spatial resolution of approximately 3 m across all meteorological seasons. This work presents a comprehensive cloud-removal dataset aimed at advancing remote sensing techniques, with cloud removal as the primary focus. This dataset is created to serve the research community, as it offers the potential to support broader remote sensing applications by enabling researchers to generate cloud-free images for time-series analysis, land cover land use classification, change detection, and other agricultural tasks. |
| format | Article |
| id | doaj-art-ace9217843674854a5bcf5e53a6f892c |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-ace9217843674854a5bcf5e53a6f892c2025-08-20T03:17:46ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118101201013010.1109/JSTARS.2025.355795410949711PLA4MS: Curated Georeferenced Dataset for Cloud Removal in Remote SensingSushil Ghildiyal0https://orcid.org/0000-0002-4862-4846Ashutosh Kumar1Neeraj Goel2https://orcid.org/0000-0003-3824-9437Mukesh Saini3https://orcid.org/0000-0003-2215-9365Abdulmotaleb El Saddik4https://orcid.org/0000-0002-7690-8547Department of Computer and Engineering, Indian Institute of Technology Ropar, Rupnagar, IndiaDepartment of Computer and Engineering, Indian Institute of Technology Ropar, Rupnagar, IndiaDepartment of Computer and Engineering, Indian Institute of Technology Ropar, Rupnagar, IndiaDepartment of Computer and Engineering, Indian Institute of Technology Ropar, Rupnagar, IndiaSchool of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, CanadaThe presence of meticulously curated extensive training datasets plays a crucial role in advancing the performance of deep learning techniques that generalize well for extracting geoinformation from multisensor remote sensing imagery. Despite numerous datasets being published by the research community, a substantial portion of them are hampered by significant constraints, such as low spatial resolution, lack of ground details over time, and insufficient sample quantity. This article utilizes the openly accessible data obtained from the Planetscope satellites managed by Planet Labs. We have curated a dataset called PLA4MS comprising 64 557 pairs of images depicting cloudy and cloud-free conditions. The study focuses on the Ropar region of Punjab, India, as the primary area of interest, ensuring precise georeferencing at a spatial resolution of approximately 3 m across all meteorological seasons. This work presents a comprehensive cloud-removal dataset aimed at advancing remote sensing techniques, with cloud removal as the primary focus. This dataset is created to serve the research community, as it offers the potential to support broader remote sensing applications by enabling researchers to generate cloud-free images for time-series analysis, land cover land use classification, change detection, and other agricultural tasks.https://ieeexplore.ieee.org/document/10949711/Cloud removalcrop growth monitoringdatasetdeep learningmachine learningremote sensing |
| spellingShingle | Sushil Ghildiyal Ashutosh Kumar Neeraj Goel Mukesh Saini Abdulmotaleb El Saddik PLA4MS: Curated Georeferenced Dataset for Cloud Removal in Remote Sensing IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Cloud removal crop growth monitoring dataset deep learning machine learning remote sensing |
| title | PLA4MS: Curated Georeferenced Dataset for Cloud Removal in Remote Sensing |
| title_full | PLA4MS: Curated Georeferenced Dataset for Cloud Removal in Remote Sensing |
| title_fullStr | PLA4MS: Curated Georeferenced Dataset for Cloud Removal in Remote Sensing |
| title_full_unstemmed | PLA4MS: Curated Georeferenced Dataset for Cloud Removal in Remote Sensing |
| title_short | PLA4MS: Curated Georeferenced Dataset for Cloud Removal in Remote Sensing |
| title_sort | pla4ms curated georeferenced dataset for cloud removal in remote sensing |
| topic | Cloud removal crop growth monitoring dataset deep learning machine learning remote sensing |
| url | https://ieeexplore.ieee.org/document/10949711/ |
| work_keys_str_mv | AT sushilghildiyal pla4mscuratedgeoreferenceddatasetforcloudremovalinremotesensing AT ashutoshkumar pla4mscuratedgeoreferenceddatasetforcloudremovalinremotesensing AT neerajgoel pla4mscuratedgeoreferenceddatasetforcloudremovalinremotesensing AT mukeshsaini pla4mscuratedgeoreferenceddatasetforcloudremovalinremotesensing AT abdulmotalebelsaddik pla4mscuratedgeoreferenceddatasetforcloudremovalinremotesensing |