Multi-temporal mapping of the Green View Index (GVI) is crucial for understanding how urban residents perceive seasonal changes in streetscape greenness. Compared to street view imagery (SVI), remote sensing data offers higher temporal frequency and broader spatial coverage, enabling large-scale dynamic monitoring. However, most existing GVI estimation methods rely heavily on SVI, limiting their ability to support cross-city and seasonal analysis. To address this gap, we present the Seasonal Green View Index 2023 (SGVI-2023), a GVI mapping dataset derived from multisource remote sensing data and deep learning. Covering 19 major Chinese cities, SGVI-2023 was developed using approximately 1.1 million paired samples of satellite and SVI data collected from 2019 to 2023. All data underwent strict preprocessing and partitioning. Evaluation results show strong accuracy, with Pearson correlations of 0.875 at the point scale and 0.928 at the street scale. As the first cross-city, seasonally resolved GVI dataset based on remote sensing, SGVI-2023 provides valuable support for human-centered urban greenness monitoring and data-driven urban planning.

Multi-temporal mapping of the Green View Index (GVI) is crucial for understanding how urban residents perceive seasonal changes in streetscape greenness. Compared to street view imagery (SVI), remote sensing data offers higher temporal frequency and broader spatial coverage, enabling large-scale dynamic monitoring. However, most existing GVI estimation methods rely heavily on SVI, limiting their ability to support cross-city and seasonal analysis. To address this gap, we present the Seasonal Green View Index 2023 (SGVI-2023), a GVI mapping dataset derived from multisource remote sensing data and deep learning. Covering 19 major Chinese cities, SGVI-2023 was developed using approximately 1.1 million paired samples of satellite and SVI data collected from 2019 to 2023. All data underwent strict preprocessing and partitioning. Evaluation results show strong accuracy, with Pearson correlations of 0.875 at the point scale and 0.928 at the street scale. As the first cross-city, seasonally resolved GVI dataset based on remote sensing, SGVI-2023 provides valuable support for human-centered urban greenness monitoring and data-driven urban planning.

Multi-temporal mapping of the Green View Index (GVI) is essential for understanding how urban residents perceive seasonal variations in streetscape greenness. Compared to street view imagery (SVI), remote sensing data offers greater temporal resolution and broader spatial coverage, providing new opportunities for dynamic monitoring of urban greenery. However, existing GVI estimation methods rely heavily on SVI, making it difficult to achieve cross-city and multi-seasonal mapping. This limitation not only hinders accurate characterization of the spatiotemporal dynamics of GVI but also introduces potential errors and uncertainties in urban studies and related policymaking. To address these challenges, this study leverages multi-source remote sensing data and deep learning techniques to develop the Seasonal Green View Index-2023 (SGVI-2023) dataset, covering 19 major cities across China . During model training, we collected approximately 1.1 million paired samples of multi-source satellite data and SVI from 2019 to 2023 across the 19 cities and applied rigorous standards for data preprocessing and partitioning. Evaluation results demonstrate the high accuracy of SGVI-2023 in GVI estimation, achieving a Pearson correlation coefficient of 0.875 at the point scale and 0.93 at the street scale. To the best of our knowledge, SGVI-2023 is the first cross-city, seasonally resolved GVI dataset derived from remote sensing data. It offers a valuable data resource for dynamic monitoring of urban greenness from a human visual perspective and supports more refined, evidence-based urban planning and policy formulation.