Sphere2Vec: Multi-Scale Representation Learning over a Spherical Surface for Geospatial Predictions. (arXiv:2201.10489v1 [cs.CV])

Web: http://arxiv.org/abs/2201.10489

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 …

arxiv cv geospatial learning predictions scale