DACIA5: a Sentinel-1 and Sentinel-2 dataset for agricultural crop identification applications
Artificial intelligence and data analysis are essential in smart agriculture for enhancing crop productivity and food security. However, progress in this field is often limited by the lack of specialized, error-free labeled datasets. This paper introduces DACIA5, a multispectral image dataset for ag...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-06-01
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| Series: | Big Earth Data |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/20964471.2025.2512685 |
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| author | A. Băicoianu I. C. Plajer M. Debu M. Ștefan M. Ivanovici C. Florea A. Cațaron R. M. Coliban Ș. Popa Ș. Oprișescu A. Racovițeanu Gh. Olteanu K. Marandskiy A. Ghinea A. Kazak L. Majercsik A. Manea L. Dogar |
| author_facet | A. Băicoianu I. C. Plajer M. Debu M. Ștefan M. Ivanovici C. Florea A. Cațaron R. M. Coliban Ș. Popa Ș. Oprișescu A. Racovițeanu Gh. Olteanu K. Marandskiy A. Ghinea A. Kazak L. Majercsik A. Manea L. Dogar |
| author_sort | A. Băicoianu |
| collection | DOAJ |
| description | Artificial intelligence and data analysis are essential in smart agriculture for enhancing crop productivity and food security. However, progress in this field is often limited by the lack of specialized, error-free labeled datasets. This paper introduces DACIA5, a multispectral image dataset for agricultural crop identification, complemented with Sentinel-1 radar data. The dataset consists of 172 Sentinel-2 multispectral images (800 × 450 pixels) and 159 Sentinel-1 radar images, collected over Brașov, Romania, from 2020 to 2024, with precise, in-situ verified labels. Additionally, 6,454 Sentinel-2 and 5,995 Sentinel-1 rectangular patches (32 × 32 pixels) were extracted, exceeding 6 million pixels in total. The cropland parcels considered in our dataset are used for research and are owned and cultivated by the National Institute of Research and Development for Potato and Sugar Beet, ensuring error-free labeling. The labels in our dataset provide detailed information about crop types, offering insights into crop distribution, growth stages, and phenological events. Furthermore, we present a comprehensive dataset analysis and two key use cases: crop identification based on a “past vs. present” approach and early crop identification during the agricultural season. |
| format | Article |
| id | doaj-art-6a46c95490004f8ead683dd2c1735de7 |
| institution | Kabale University |
| issn | 2096-4471 2574-5417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Big Earth Data |
| spelling | doaj-art-6a46c95490004f8ead683dd2c1735de72025-08-20T03:46:04ZengTaylor & Francis GroupBig Earth Data2096-44712574-54172025-06-0113210.1080/20964471.2025.2512685DACIA5: a Sentinel-1 and Sentinel-2 dataset for agricultural crop identification applicationsA. Băicoianu0I. C. Plajer1M. Debu2M. Ștefan3M. Ivanovici4C. Florea5A. Cațaron6R. M. Coliban7Ș. Popa8Ș. Oprișescu9A. Racovițeanu10Gh. Olteanu11K. Marandskiy12A. Ghinea13A. Kazak14L. Majercsik15A. Manea16L. Dogar17Department of Mathematics and Informatics, Transilvania University, Brașov, RomaniaDepartment of Mathematics and Informatics, Transilvania University, Brașov, RomaniaDepartment of Mathematics and Informatics, Transilvania University, Brașov, RomaniaNational Institute of Research and Development for Potato and Sugar Beet, Brașov, RomaniaDepartment of Mathematics and Informatics, Transilvania University, Brașov, RomaniaDepartment of Mathematics and Informatics, Transilvania University, Brașov, RomaniaDepartment of Mathematics and Informatics, Transilvania University, Brașov, RomaniaDepartment of Mathematics and Informatics, Transilvania University, Brașov, RomaniaDepartment of Mathematics and Informatics, Transilvania University, Brașov, RomaniaDepartment of Mathematics and Informatics, Transilvania University, Brașov, RomaniaDepartment of Mathematics and Informatics, Transilvania University, Brașov, RomaniaDepartment of Mathematics and Informatics, Transilvania University, Brașov, RomaniaDepartment of Mathematics and Informatics, Transilvania University, Brașov, RomaniaNational Institute of Research and Development for Potato and Sugar Beet, Brașov, RomaniaDepartment of Mathematics and Informatics, Transilvania University, Brașov, RomaniaDepartment of Mathematics and Informatics, Transilvania University, Brașov, RomaniaDepartment of Mathematics and Informatics, Transilvania University, Brașov, RomaniaDepartment of Mathematics and Informatics, Transilvania University, Brașov, RomaniaArtificial intelligence and data analysis are essential in smart agriculture for enhancing crop productivity and food security. However, progress in this field is often limited by the lack of specialized, error-free labeled datasets. This paper introduces DACIA5, a multispectral image dataset for agricultural crop identification, complemented with Sentinel-1 radar data. The dataset consists of 172 Sentinel-2 multispectral images (800 × 450 pixels) and 159 Sentinel-1 radar images, collected over Brașov, Romania, from 2020 to 2024, with precise, in-situ verified labels. Additionally, 6,454 Sentinel-2 and 5,995 Sentinel-1 rectangular patches (32 × 32 pixels) were extracted, exceeding 6 million pixels in total. The cropland parcels considered in our dataset are used for research and are owned and cultivated by the National Institute of Research and Development for Potato and Sugar Beet, ensuring error-free labeling. The labels in our dataset provide detailed information about crop types, offering insights into crop distribution, growth stages, and phenological events. Furthermore, we present a comprehensive dataset analysis and two key use cases: crop identification based on a “past vs. present” approach and early crop identification during the agricultural season.https://www.tandfonline.com/doi/10.1080/20964471.2025.2512685Sentinel-2 dataSentinel-1 datasmart agricultureartificial intelligencecrop identificationearly crop identification |
| spellingShingle | A. Băicoianu I. C. Plajer M. Debu M. Ștefan M. Ivanovici C. Florea A. Cațaron R. M. Coliban Ș. Popa Ș. Oprișescu A. Racovițeanu Gh. Olteanu K. Marandskiy A. Ghinea A. Kazak L. Majercsik A. Manea L. Dogar DACIA5: a Sentinel-1 and Sentinel-2 dataset for agricultural crop identification applications Big Earth Data Sentinel-2 data Sentinel-1 data smart agriculture artificial intelligence crop identification early crop identification |
| title | DACIA5: a Sentinel-1 and Sentinel-2 dataset for agricultural crop identification applications |
| title_full | DACIA5: a Sentinel-1 and Sentinel-2 dataset for agricultural crop identification applications |
| title_fullStr | DACIA5: a Sentinel-1 and Sentinel-2 dataset for agricultural crop identification applications |
| title_full_unstemmed | DACIA5: a Sentinel-1 and Sentinel-2 dataset for agricultural crop identification applications |
| title_short | DACIA5: a Sentinel-1 and Sentinel-2 dataset for agricultural crop identification applications |
| title_sort | dacia5 a sentinel 1 and sentinel 2 dataset for agricultural crop identification applications |
| topic | Sentinel-2 data Sentinel-1 data smart agriculture artificial intelligence crop identification early crop identification |
| url | https://www.tandfonline.com/doi/10.1080/20964471.2025.2512685 |
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