Tracking U.S. Land Cover Changes: A Dataset of Sentinel-2 Imagery and Dynamic World Labels (2016–2024)
Monitoring land cover changes is crucial for understanding how natural processes and human activities such as deforestation, urbanization, and agriculture reshape the environment. We introduce a publicly available dataset covering the entire United States from 2016 to 2024, integrating six spectral...
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| Format: | Article |
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MDPI AG
2025-05-01
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| author | Antonio Rangel Juan Terven Diana-Margarita Córdova-Esparza Julio-Alejandro Romero-González Alfonso Ramírez-Pedraza Edgar A. Chávez-Urbiola Francisco. J. Willars-Rodríguez Gendry Alfonso-Francia |
| author_facet | Antonio Rangel Juan Terven Diana-Margarita Córdova-Esparza Julio-Alejandro Romero-González Alfonso Ramírez-Pedraza Edgar A. Chávez-Urbiola Francisco. J. Willars-Rodríguez Gendry Alfonso-Francia |
| author_sort | Antonio Rangel |
| collection | DOAJ |
| description | Monitoring land cover changes is crucial for understanding how natural processes and human activities such as deforestation, urbanization, and agriculture reshape the environment. We introduce a publicly available dataset covering the entire United States from 2016 to 2024, integrating six spectral bands (Red, Green, Blue, NIR, SWIR1, and SWIR2) from Sentinel-2 imagery with pixel-level land cover annotations from the Dynamic World dataset. This combined resource provides a consistent, high-resolution view of the nation’s landscapes, enabling detailed analysis of both short- and long-term changes. To ease the complexities of remote sensing data handling, we supply comprehensive code for data loading, basic analysis, and visualization. We also demonstrate an example application—semantic segmentation with state-of-the-art models—to evaluate dataset quality and reveal challenges associated with minority classes. The dataset and accompanying tools facilitate research in environmental monitoring, urban planning, and climate adaptation, offering a valuable asset for understanding evolving land cover dynamics over time. |
| format | Article |
| id | doaj-art-d633e8ccfd024de0bce13c49e1768e88 |
| institution | OA Journals |
| issn | 2306-5729 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj-art-d633e8ccfd024de0bce13c49e1768e882025-08-20T02:33:44ZengMDPI AGData2306-57292025-05-011056710.3390/data10050067Tracking U.S. Land Cover Changes: A Dataset of Sentinel-2 Imagery and Dynamic World Labels (2016–2024)Antonio Rangel0Juan Terven1Diana-Margarita Córdova-Esparza2Julio-Alejandro Romero-González3Alfonso Ramírez-Pedraza4Edgar A. Chávez-Urbiola5Francisco. J. Willars-Rodríguez6Gendry Alfonso-Francia7CICATA-Qro, Instituto Politecnico Nacional, Queretaro 76090, MexicoCICATA-Qro, Instituto Politecnico Nacional, Queretaro 76090, MexicoFacultad de Informática, Universidad Autónoma de Querétaro, Queretaro 76230, MexicoFacultad de Informática, Universidad Autónoma de Querétaro, Queretaro 76230, MexicoCICATA-Qro, Instituto Politecnico Nacional, Queretaro 76090, MexicoCICATA-Qro, Instituto Politecnico Nacional, Queretaro 76090, MexicoCICATA-Qro, Instituto Politecnico Nacional, Queretaro 76090, MexicoCICATA-Qro, Instituto Politecnico Nacional, Queretaro 76090, MexicoMonitoring land cover changes is crucial for understanding how natural processes and human activities such as deforestation, urbanization, and agriculture reshape the environment. We introduce a publicly available dataset covering the entire United States from 2016 to 2024, integrating six spectral bands (Red, Green, Blue, NIR, SWIR1, and SWIR2) from Sentinel-2 imagery with pixel-level land cover annotations from the Dynamic World dataset. This combined resource provides a consistent, high-resolution view of the nation’s landscapes, enabling detailed analysis of both short- and long-term changes. To ease the complexities of remote sensing data handling, we supply comprehensive code for data loading, basic analysis, and visualization. We also demonstrate an example application—semantic segmentation with state-of-the-art models—to evaluate dataset quality and reveal challenges associated with minority classes. The dataset and accompanying tools facilitate research in environmental monitoring, urban planning, and climate adaptation, offering a valuable asset for understanding evolving land cover dynamics over time.https://www.mdpi.com/2306-5729/10/5/67LULCchange detectionremote sensing |
| spellingShingle | Antonio Rangel Juan Terven Diana-Margarita Córdova-Esparza Julio-Alejandro Romero-González Alfonso Ramírez-Pedraza Edgar A. Chávez-Urbiola Francisco. J. Willars-Rodríguez Gendry Alfonso-Francia Tracking U.S. Land Cover Changes: A Dataset of Sentinel-2 Imagery and Dynamic World Labels (2016–2024) Data LULC change detection remote sensing |
| title | Tracking U.S. Land Cover Changes: A Dataset of Sentinel-2 Imagery and Dynamic World Labels (2016–2024) |
| title_full | Tracking U.S. Land Cover Changes: A Dataset of Sentinel-2 Imagery and Dynamic World Labels (2016–2024) |
| title_fullStr | Tracking U.S. Land Cover Changes: A Dataset of Sentinel-2 Imagery and Dynamic World Labels (2016–2024) |
| title_full_unstemmed | Tracking U.S. Land Cover Changes: A Dataset of Sentinel-2 Imagery and Dynamic World Labels (2016–2024) |
| title_short | Tracking U.S. Land Cover Changes: A Dataset of Sentinel-2 Imagery and Dynamic World Labels (2016–2024) |
| title_sort | tracking u s land cover changes a dataset of sentinel 2 imagery and dynamic world labels 2016 2024 |
| topic | LULC change detection remote sensing |
| url | https://www.mdpi.com/2306-5729/10/5/67 |
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