TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning скачать в хорошем качестве

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

We have been developing a Python Package called TorchSpatial, a comprehensive framework and benchmark suite designed to advance spatial representation learning (SRL). It includes a unified location encoding framework that supports location encoding model development and the LocBench benchmark tasks that support location encoding model evaluation. Additionally, TorchSpatial introduces the Geo-Bias Score, a novel metric to evaluate model performance and geographic bias, promoting spatial fairness in GeoAI applications. In this talk, we will explain and demonstrate how we further developed TorchSpatial during I-GUIDE's 2024-25 Spatial AI Challenge. Qian Cao & Nemin Wu University of Georgia Recorded on: Oct 8, 2025