Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions

no code implementations • 30 Jun 2023 • Gengchen Mai, Yao Xuan, Wenyun Zuo, Yutong He, Jiaming Song, Stefano Ermon, Krzysztof Janowicz, Ni Lao

So when applied to large-scale real-world GPS coordinate datasets, which require distance metric learning on the spherical surface, both types of models can fail due to the map projection distortion problem (2D) and the spherical-to-Euclidean distance approximation error (3D).

Image Classification Metric Learning +2

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

no code implementations • 25 Jan 2022 • Gengchen Mai, Yao Xuan, Wenyun Zuo, Krzysztof Janowicz, Ni Lao

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.

Image Classification Representation Learning

Sphere2Vec: Self-Supervised Location Representation Learning on Spherical Surfaces

no code implementations • 29 Sep 2021 • Gengchen Mai, Yao Xuan, Wenyun Zuo, Yutong He, Stefano Ermon, Jiaming Song, Krzysztof Janowicz, Ni Lao

Location encoding is valuable for a multitude of tasks where both the absolute positions and local contexts (image, text, and other types of metadata) of spatial objects are needed for accurate predictions.

Image Classification Representation Learning +1