  The land-use map is an important data that can reflect the use and
transformation of human land, and can provide valuable reference for land-use
planning. For the traditional image classification method, producing a high
spatial resolution (HSR), land-use map in large-scale is a big project that
requires a lot of human labor, time, and financial expenditure. The rise of the
deep learning technique provides a new solution to the problems above. This
paper proposes a fast and precise method that can achieve large-scale land-use
classification based on deep convolutional neural network (DCNN). In this
paper, we optimize the data tiling method and the structure of DCNN for the
multi-channel data and the splicing edge effect, which are unique to remote
sensing deep learning, and improve the accuracy of land-use classification. We
apply our improved methods in the Guangdong Province of China using GF-1
images, and achieve the land-use classification accuracy of 81.52%. It takes
only 13 hours to complete the work, which will take several months for human
labor.
