  Land use as contained in geospatial databases constitutes an essential input
for different applica-tions 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 net-work (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 hier-archical 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 underrepre-sented categories, despite their
different characteristics.
