Tile2Vec: Unsupervised Representation Learning for Spatially Distributed Data

  • Neal Jean Stanford University
  • Sherrie Wang Stanford University
  • Anshul Samar Stanford University
  • George Azzari Stanford University
  • David Lobell Stanford University
  • Stefano Ermon Stanford University


Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks. To fill this gap, we introduce Tile2Vec, an unsupervised representation learning algorithm that extends the distributional hypothesis from natural language — words appearing in similar contexts tend to have similar meanings — to spatially distributed data. We demonstrate empirically that Tile2Vec learns semantically meaningful representations for both image and non-image datasets. Our learned representations significantly improve performance in downstream classification tasks and, similarly to word vectors, allow visual analogies to be obtained via simple arithmetic in the latent space.