Spatial representation learning (SRL) aims at learning general-purpose neural
network representations from various types of spatial data (e.g., points,
polylines, polygons, networks, images, etc.) in their native formats. Learning
good spatial representations is a fundamental problem for various downstream
applications such as species distribution modeling, weather forecasting,
trajectory generation, geographic question answering, etc. Even though SRL has
become the foundation of almost all geospatial artificial intelligence (GeoAI)
research, we have not yet seen significant efforts to develop an extensive deep
learning framework and benchmark to support SRL model development and
evaluation. To fill this gap, we propose TorchSpatial, a learning framework and
benchmark for location (point) encoding, which is one of the most fundamental
data types of spatial representation learning. TorchSpatial contains three key
components: 1) a unified location encoding framework that consolidates 15
commonly recognized location encoders, ensuring scalability and reproducibility
of the implementations; 2) the LocBench benchmark tasks encompassing 7
geo-aware image classification and 4 geo-aware image regression datasets; 3) a
comprehensive suite of evaluation metrics to quantify geo-aware models' overall
performance as well as their geographic bias, with a novel Geo-Bias Score
metric. Finally, we provide a detailed analysis and insights into the model
performance and geographic bias of different location encoders. We believe
TorchSpatial will foster future advancement of spatial representation learning
and spatial fairness in GeoAI research. The TorchSpatial model framework,
LocBench, and Geo-Bias Score evaluation framework are available at
this https URL

Spatial representation learning (SRL) is addressed with the proposed TorchSpatial framework, which includes a unified location encoding framework, benchmark tasks, and evaluation metrics, aiming to support SRL model development, reproducibility, and spatial fairness in GeoAI research.

TorchSpatial: 空间表示学习的位置编码框架和基准

TorchSpatial: A Location Encoding Framework and Benchmark for Spatial  Representation Learning

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.

提出了一种新的多尺度定位编码模型 Sphere2Vec，可以直接编码球形表面上的点坐标，避免地图投影扭曲问题。通过应用于地理感知图像分类任务的实验，证明了该模型在极地和数据稀疏区域的图像分类任务中性能优于其他二维定位编码模型。

Sphere2Vec: 多尺度球面表示学习，用于地理空间预测

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

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.

本文从机器学习的角度提出了位置编码的概念，系统回顾并讨论了当前位置编码研究的现状和挑战，将已有的位置编码模型分类，并探讨了它们与不同类型空间数据的应用，并指出了未来位置编码研究面临的挑战。