Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells

Understand

Unsupervised text encoding models have recently fueled substantial progress in NLP.

• The key idea is to use neural networks to convert words in texts to vector space representations based on word positions in a sentence and their contexts, which are suitable for end-to-end training of downstream tasks.
• We see a strikingly similar situation in spatial analysis, which focuses on incorporating both absolute positions and spatial contexts of geographic objects such as POIs into models.
• A general-purpose representation model for space is valuable for a multitude of tasks.