  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
https://github.com/seai-lab/TorchSpatial.
