Urban public open spaces (POS) are pivotal in sustainable urban planning, recognized for their positive impacts on the health of residents and environments. However, understanding their physical features in detail via remote sensing remains challenging due to the small and complex area characteristics. Advanced approaches struggle with the requirement for extensive training datasets, which are difficult to obtain and potentially inaccurate in certain settings. Sparse sampling of training data may offer a solution to these challenges, but its inability to consider useful contextual characteristics from objects remains as a significant barrier.

中文翻译：

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城市公共开放空间精准高效的特征分类：一种基于深度学习的多元时间序列方法


城市公共开放空间 （POS） 在可持续城市规划中发挥着关键作用，因其对居民健康和环境的积极影响而得到认可。然而，由于面积小且复杂，通过遥感详细了解它们的物理特征仍然具有挑战性。高级方法难以满足对大量训练数据集的要求，这些数据集很难获得，并且在某些情况下可能不准确。训练数据的稀疏采样可能为这些挑战提供解决方案，但它无法考虑来自对象的有用上下文特征仍然是一个重大障碍。