Tracking U.S. Land Cover Changes: A Dataset of Sentinel-2 Imagery and Dynamic World Labels (2016–2024)

Antonio Rangel, Juan Terven (), Diana-Margarita Córdova-Esparza, Julio-Alejandro Romero-González, Alfonso Ramírez-Pedraza, Edgar A. Chávez-Urbiola, Francisco. J. Willars-Rodríguez and Gendry Alfonso-Francia
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Antonio Rangel: CICATA-Qro, Instituto Politecnico Nacional, Queretaro 76090, Mexico
Juan Terven: CICATA-Qro, Instituto Politecnico Nacional, Queretaro 76090, Mexico
Diana-Margarita Córdova-Esparza: Facultad de Informática, Universidad Autónoma de Querétaro, Queretaro 76230, Mexico
Julio-Alejandro Romero-González: Facultad de Informática, Universidad Autónoma de Querétaro, Queretaro 76230, Mexico
Alfonso Ramírez-Pedraza: CICATA-Qro, Instituto Politecnico Nacional, Queretaro 76090, Mexico
Edgar A. Chávez-Urbiola: CICATA-Qro, Instituto Politecnico Nacional, Queretaro 76090, Mexico
Francisco. J. Willars-Rodríguez: CICATA-Qro, Instituto Politecnico Nacional, Queretaro 76090, Mexico
Gendry Alfonso-Francia: CICATA-Qro, Instituto Politecnico Nacional, Queretaro 76090, Mexico

Data, 2025, vol. 10, issue 5, 1-15

Abstract: 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.

Keywords: LULC; change detection; remote sensing (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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