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

2024-06-21

arXiv.org

AI Summary

Current spatial representation learning (SRL) lacks a unified evaluation framework and standardized metrics for quantifying geographic bias—particularly in point-location encoding and geospatial perception tasks. To address this, we introduce LocBench, the first deep learning benchmark framework specifically designed for point-location encoding. LocBench systematically integrates 15 state-of-the-art encoders and 17 geospatially diverse datasets spanning image classification and regression tasks. We propose the Geo-Bias Score, the first metric to quantify model-induced geographic bias. The framework features a unified PyTorch API, a multi-task evaluation protocol, and an open-source toolchain (TorchSpatial + PyGBS). Extensive experiments comprehensively assess encoder performance and bias across datasets, demonstrating LocBench’s reproducibility, extensibility, and methodological rigor. Our work significantly advances SRL model development efficiency and strengthens fairness-aware research in GeoAI.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Remote Sensing / Geospatial AI
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

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.

Problem

• Spatial Representation Learning
• Geographical Bias
• Evaluation Standards

Innovation

• TorchSpatial
• Spatial Representation Learning
• Geo-Bias Score