Land use as contained in geospatial databases constitutes an essential input for different applications such as urban management, regional planning and environmental monitoring. In this paper, a hierarchical deep learning framework is proposed to verify the land use information. For this purpose, a two-step strategy is applied. First, given high-resolution aerial images, the land cover information is determined. To achieve this, an encoder-decoder based convolutional neural network (CNN) is proposed. Second, the pixel-wise land cover information along with the aerial images serves as input for another CNN to classify land use. Because the object catalogue of geospatial databases is frequently constructed in a hierarchical manner, we propose a new CNN-based method aiming to predict land use in multiple levels hierarchically and simultaneously. A so called Joint Optimization (JO) is proposed where predictions are made by selecting the hierarchical tuple over all levels which has the maximum joint class scores, providing consistent results across the different levels.

The conducted experiments show that the CNN relying on JO outperforms previous results, achieving an overall accuracy up to 92.5%. In addition to the individual experiments on two test sites, we investigate whether data showing different characteristics can improve the results of land cover and land use classification, when processed together. To do so, we combine the two datasets and undertake some additional experiments. The results show that adding more data helps both land cover and land use classification, especially the identification of underrepresented categories, despite their different characteristics.

地理空间数据库中包含的土地使用构成了城市管理，区域规划和环境监测等不同应用的重要输入。本文提出了一种分层的深度学习框架来验证土地使用信息。为此，应用了两步策略。首先，给定高分辨率的航空图像，确定土地覆盖信息。为此，提出了一种基于编码器-解码器的卷积神经网络（CNN）。其次，逐像素土地覆盖信息与航拍图像一起用作另一个CNN的输入，以对土地用途进行分类。由于地理空间数据库的对象目录通常以分层方式构建，因此我们提出了一种基于CNN的新方法，旨在分层预测多层次的土地利用和同时。提出了一种所谓的联合优化（JO），其中通过在所有级别上选择具有最大联合类得分的分层元组来进行预测，从而在不同级别上提供一致的结果。

进行的实验表明，依靠JO的CNN优于以前的结果，整体准确率高达92.5％。除了在两个测试站点上进行单独的实验之外，我们还研究了显示不同特征的数据在一起处理时是否可以改善土地覆盖和土地利用分类的结果。为此，我们结合了两个数据集并进行了一些额外的实验。结果表明，添加更多数据有助于土地覆被和土地用途分类，特别是识别代表性不足的类别，尽管它们具有不同的特征。