Towards a General-Purpose Framework for Spatial Representation Learning

Keywords: Spatial representation learning, spatially explicit artificial intelligence, location encoding, polygon encoding
Abstract Type: Paper Abstract

Abstract

Representation learning (RL) techniques are widely adopted in areas such as natural language processing and computer vision, with prominent examples such as attention and ConvNet architectures. In comparison, many GeoAI works still rely on feature engineering or data conversion to represent spatial data (e.g., points, polylines, polygons, 3D building models, etc.) as features in formats that are easier for neural networks to handle. The neural network architectures remain unchanged, and the need for feature engineering has become a bottleneck for applying deep learning to new tasks in the age of big data. In this paper, we advocate the idea of developing learnable spatial representation modules, which not only enable spatial reasoning but also enable neural nets to directly consume (i.e., encoding) or generate (i.e., decoding) spatial data.We propose Spatial Representation Learning (SRL), a new general-purpose representation learning framework for spatial reasoning. We discuss the key challenges of spatial representation learning including multi-scale RL, continuous RL, shape-centric RL, noise-robust RL, heterogeneity-aware RL, and fairness-aware RL. We also discuss the critical role and potential of \frameworkShort~in various geospatial subdomains and how this technique can lead to a new generation of GeoAI.