Neural Network Driven by Density and Parallel Features for Field-Road Mode Mining

Field-road mode mining (FRMM) has gained increasing attention because of its crucial role in machinery management. As the Global Navigation Satellite System (GNSS) is widely applied in agricultural machinery, many methods based on motion and spatial-temporal features of GNSS trajectory data have been proposed for FRMM. However, these methods ignore the density and parallel features of GNSS points. The two features are useful for FRMM because field points usually have a higher density and more parallel points than road points. Therefore, a neural network driven by density and parallel features is proposed for accurate FRMM. Firstly, a statistical method is designed to extract the density feature (i.e., the number of its neighbor points) and parallel feature (i.e., the number of approximately parallel points in its neighbors) of each point. Then, the two features and eight motion features (e.g., speed, direction, etc.) are fed into a neural network to extract valuable latent features. Finally, a linear classifier is used to identify the field and road categories of GNSS points based on the latent features. Experimental results show that our method outperforms state-of-the-art methods and achieves the accuracy of 91.69% and 86.44% on public Wheat and Paddy datasets, respectively.