TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning (2406.15658v3)

Abstract: 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 10 geo-aware image regression datasets; 3) a comprehensive suite of evaluation metrics to quantify geo-aware model's 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 and LocBench benchmark are available at https://github.com/seai-lab/TorchSpatial, and the Geo-Bias Score evaluation framework is available at https://github.com/seai-lab/PyGBS.

• The paper presents TorchSpatial, a framework that unifies 15 location encoders to enhance spatial representation learning and reproducibility.
• The paper introduces the LocBench benchmark with diverse geo-aware image datasets to rigorously evaluate classification and regression tasks.
• The paper develops the Geo-Bias Score to quantify geographic bias in AI models, underscoring the importance of spatial fairness in GeoAI.

An Overview of TorchSpatial: A Framework and Benchmark for Spatial Representation Learning

The paper "TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning" introduces a structured framework aimed at enhancing spatial representation learning (SRL) within geospatial artificial intelligence (GeoAI) applications. SRL, a fundamental task in GeoAI, strives to derive effective neural representations from spatial data—ranging from discrete points to complex networks—without resorting to manual feature engineering or data transformations. Despite its significance, SRL lacks a standardized framework or benchmark necessary for establishing effective deep learning models. TorchSpatial seeks to fill this void by offering an extendable deep learning framework and a comprehensive benchmark.

TorchSpatial's primary contributions are threefold:

1. TorchSpatial Model Framework: This framework serves as a foundational tool for the development and evaluation of location encoders. It unifies 15 widely-used location encoders, thus promoting scalability and reproducibility in spatial model implementations. These encoders are divided into two groups: 2D location encoders that work on projected two-dimensional spaces and 3D encoders that operate in three-dimensional contexts.
2. LocBench Benchmark: LocBench is a benchmarking tool that includes seven geo-aware image classification and four geo-aware image regression datasets. These datasets enable the systematic evaluation of location encoders across diverse tasks, geographical spread, and dataset sizes, thereby ensuring rigorous evaluation procedures and comparative analyses of model performances.
3. Geographic Bias Evaluation Metrics: The paper introduces a quantitative framework to assess the geographic bias of machine learning models within GeoAI, articulated through the Geo-Bias Score. This evaluation framework is pioneering in its universality, enabling the consistent evaluation of biases in spatial prediction models. This score is essential not only for measuring performance deviations across geographical areas but also for fostering spatial fairness in machine learning.

Empirical Results and Observations

Harnessing the proposed framework, the authors conducted extensive experiments, revealing several intriguing findings. Introducing a location encoder component notably improves the performance of existing image classification models, enhancing the classification accuracy significantly across the LocBench datasets. However, it is also observed that the inclusion of location encoders may exacerbate geographic biases in models, particularly where data is sparsely or unevenly distributed geospatially. The introduction of uniformly-at-random sampling in datasets appears to mitigate this bias, underscoring the importance of considering distribution factors in model evaluations.

Practical and Theoretical Implications

Practically, TorchSpatial provides an invaluable resource for GeoAI researchers, significantly lowering the barrier for SRL model development. The standardized framework promotes consistency across studies and encourages the community to focus on innovation rather than foundational overhead. Theoretically, the introduction of the Geo-Bias Score sets a precedent for evaluating spatial fairness rigorously, which is crucial as AI systems increasingly make geolocation-based predictions that can affect decision-making processes and policy formulations.

Future Developments

The authors propose extending TorchSpatial to support a broader range of spatial data types, fostering more comprehensive SRL models. Such developments could facilitate a deeper understanding of spatial dynamics and opportunities in GeoAI methodologies. Expanding the LocBench with additional geo-aware datasets and tasks, such as sustainability predictions and geolocalization, is proposed to enhance the scope of research endeavors.

In conclusion, TorchSpatial advances the capabilities in spatial representation learning by providing a cohesive framework and benchmarking strategy. By fostering both technical advancements and considerations of spatial equity, this framework lays the groundwork for responsible and effective AI model development in geospatial domains.