A Comparative Study of Various Deep Learning Approaches to Shape Encoding of Planar Geospatial Objects

The shape encoding of geospatial objects is a key problem in the fields of cartography and geoscience. Although traditional geometric-based methods have made great progress, deep learning techniques offer a development opportunity for this classical problem. In this study, a shape encoding framework based on a deep encoder-decoder architecture was proposed, and three different methods for encoding planar geospatial shapes, namely GraphNet, SeqNet, and PixelNet methods, were constructed based on raster-based, graph-based, and sequence-based modeling for shape. The three methods were compared with the existing deep learning-based shape encoding method and two traditional geometric methods. Quantitative evaluation and visual inspection led to the following conclusions: (1) The deep encoder-decoder methods can effectively compute shape features and obtain meaningful shape coding to support the shape measure and retrieval task. (2) Compared with the traditional Fourier transform and turning function methods, the deep encoder-decoder methods showed certain advantages. (3) Compared with the SeqNet and PixelNet methods, GraphNet performed better due to the use of a graph to model the topological relations between nodes and efficient graph convolution and pooling operations to process the node features.

Automated summary

This study presents a deep learning-based shape encoding framework for geospatial objects. Three different methods, namely GraphNet, SeqNet, and PixelNet, were proposed to encode planar geospatial shapes using different modeling approaches. Comparison with existing methods and traditional geometric methods shows that the deep encoder-decoder methods have advantages in computing shape features and coding, with potential applications in shape measurement and retrieval tasks.