DeepAI
Log In Sign Up

Sphere2Vec: Multi-Scale Representation Learning over a Spherical Surface for Geospatial Predictions

01/25/2022
by   Gengchen Mai, et al.
0

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.

READ FULL TEXT

page 14

page 15

11/07/2021

A Review of Location Encoding for GeoAI: Methods and Applications

A common need for artificial intelligence models in the broader geoscien...
02/16/2020

Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells

Unsupervised text encoding models have recently fueled substantial progr...
04/24/2020

Optimal Any-Angle Pathfinding on a Sphere

Pathfinding in Euclidean space is a common problem faced in robotics and...
09/06/2017

360 Panorama Cloning on Sphere

In this paper, we address a novel problem of cloning a patch of the sour...
04/04/2022

Multi-Scale Representation Learning on Proteins

Proteins are fundamental biological entities mediating key roles in cell...
08/05/2019

Rendering Non-Euclidean Geometry in Real-Time Using Spherical and Hyperbolic Trigonometry

This paper introduces a method of calculating and rendering shapes in a ...
02/27/2020

A Free-Energy Principle for Representation Learning

This paper employs a formal connection of machine learning with thermody...