Learning From GPS Trajectories of Floating Car for CNN-Based Urban Road Extraction With High-Resolution Satellite Imagery

Deep learning has achieved great success in recent years, among which the convolutional neural network (CNN) method is outstanding in image segmentation and image recognition. It is also widely used in satellite imagery road extraction and, generally, can obtain accurate and extraction results. However, at present, the extraction of roads based on CNN still requires a lot of manual preparation work, and a large number of samples can be marked to achieve extraction, which has to take long drawing cycle and high production cost. In this article, a new CNN sample set production method is proposed, which uses the GPS trajectories of floating car as training set (GPSTasST), for the multilevel urban roads extraction from high-resolution remote sensing imagery. This method rasterizes the GPS trajectories of floating car into a raster map and uses the processed raster map to label the satellite image to obtain a road extraction sample set. CNN can extract roads from remote sensing imagery by learning the training set. The results show that the method achieves a harmonic mean of precision and recall higher than road extraction method from single data source while eliminating the manual labeling work, which shows the effectiveness of this work.

Automated summary

This article proposes a new method using GPS trajectories to generate sample sets for the extraction of multi-level urban roads from high-resolution remote sensing imagery. By eliminating the manual labeling work, the method achieves a higher harmonic mean of precision and recall compared to traditional methods of road extraction from single data sources.