  A common need for artificial intelligence models in the broader geoscience is
to represent and encode various types of spatial data, such as points (e.g.,
points of interest), polylines (e.g., trajectories), polygons (e.g.,
administrative regions), graphs (e.g., transportation networks), or rasters
(e.g., remote sensing images), in a hidden embedding space so that they can be
readily incorporated into deep learning models. One fundamental step is to
encode a single point location into an embedding space, such that this
embedding is learning-friendly for downstream machine learning models such as
support vector machines and neural networks. We call this process location
encoding. However, there lacks a systematic review on the concept of location
encoding, its potential applications, and key challenges that need to be
addressed. This paper aims to fill this gap. We first provide a formal
definition of location encoding, and discuss the necessity of location encoding
for GeoAI research from a machine learning perspective. Next, we provide a
comprehensive survey and discussion about the current landscape of location
encoding research. We classify location encoding models into different
categories based on their inputs and encoding methods, and compare them based
on whether they are parametric, multi-scale, distance preserving, and direction
aware. We demonstrate that existing location encoding models can be unified
under a shared formulation framework. We also discuss the application of
location encoding for different types of spatial data. Finally, we point out
several challenges in location encoding research that need to be solved in the
future.
