A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning

TL;DR

This paper introduces a fast, accurate deep learning-based method for large-scale land-use mapping that significantly reduces processing time while maintaining high classification accuracy.

Contribution

It presents optimized data tiling and DCNN structures tailored for remote sensing, enabling efficient large-scale land-use classification.

Findings

Achieved 81.52% classification accuracy on GF-1 images.

Completed large-scale land-use mapping in 13 hours.

Reduced manual labor from months to less than a day.

Abstract

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.