Land use and land cover are continuously changing in today’s world. Both domains, therefore, have to rely on updates of external information sources from which the relevant land use/land cover (classification) is extracted. Satellite images are frequent candidates due to their temporal and spatial resolution. On the contrary, the extraction of relevant land use/land cover information is demanding in terms of knowledge base and time. The presented approach offers a proof-of-concept machine-learning pipeline that takes care of the entire complex process in the following manner. The relevant Sentinel-2 images are obtained through the pipeline. Later, cloud masking is performed, including the linear interpolation of merged-feature time frames. Subsequently, four-dimensional arrays are created with all potential training data to become a basis for estimators from the scikit-learn library; the LightGBM estimator is then used. Finally, the classified content is applied to the open land use and open land cover databases. The verification of the provided experiment was conducted against detailed cadastral data, to which Shannon’s entropy was applied since the number of cadaster information classes was naturally consistent. The experiment showed a good overall accuracy (OA) of 85.9%. It yielded a classified land use/land cover map of the study area consisting of 7188 km2 in the southern part of the South Moravian Region in the Czech Republic. The developed proof-of-concept machine-learning pipeline is replicable to any other area of interest so far as the requirements for input data are met.

中文翻译：

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基于机器学习的处理概念验证管道，用于半自动Sentinel-2图像下载，云量过滤，开放土地使用/土地覆盖数据集的分类和更新

在当今世界，土地使用和土地覆盖正在不断变化。因此，这两个领域都必须依靠外部信息源的更新，从中提取相关的土地利用/土地覆盖（分类）。卫星图像由于其时间和空间分辨率而成为经常的候选者。相反，就知识库和时间而言，需要提取相关的土地利用/土地覆盖信息。提出的方法提供了一种概念验证的机器学习管道，该管道以以下方式处理了整个复杂过程。相关的Sentinel-2图像是通过管道获得的。之后，执行云遮罩，包括合并特征时间帧的线性插值。随后，创建具有所有潜在训练数据的四维数组，以成为scikit-learn库中估计量的基础；然后使用LightGBM估算器。最后，将分类的内容应用于开放土地使用和开放土地覆盖数据库。所提供实验的验证是针对详细的地籍数据进行的，因为地籍信息类别的数量自然是一致的，因此应用了香农熵。实验显示出良好的总体准确度（OA）为85.9％。它得出了研究区域的分类土地利用/土地覆盖图，该区域由捷克共和国南摩拉维亚地区南部的7188平方公里组成。只要满足输入数据的要求，开发的概念验证机器学习管道就可以复制到其他任何感兴趣的领域。