  Generating learning-friendly representations for points in a 2D space is a
fundamental and long-standing problem in machine learning. Recently,
multi-scale encoding schemes (such as Space2Vec) were proposed to directly
encode any point in 2D space as a high-dimensional vector, and has been
successfully applied to various (geo)spatial prediction tasks. However, a map
projection distortion problem rises when applying location encoding models to
large-scale real-world GPS coordinate datasets (e.g., species images taken all
over the world) - all current location encoding models are designed for
encoding points in a 2D (Euclidean) space but not on a spherical surface, e.g.,
earth surface. To solve this problem, we propose a multi-scale location
encoding model called Sphere2V ec which directly encodes point coordinates on a
spherical surface while avoiding the mapprojection distortion problem. We
provide theoretical proof that the Sphere2Vec encoding preserves the spherical
surface distance between any two points. We also developed a unified view of
distance-reserving encoding on spheres based on the Double Fourier Sphere
(DFS). We apply Sphere2V ec to the geo-aware image classification task. Our
analysis shows that Sphere2V ec outperforms other 2D space location encoder
models especially on the polar regions and data-sparse areas for image
classification tasks because of its nature for spherical surface distance
preservation.
