Tile2Vec: Unsupervised Representation Learning for Spatially Distributed Data

Authors

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

DOI:

https://doi.org/10.1609/aaai.v33i01.33013967

Abstract

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.

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Published

2019-07-17

How to Cite

Jean, N., Wang, S., Samar, A., Azzari, G., Lobell, D., & Ermon, S. (2019). Tile2Vec: Unsupervised Representation Learning for Spatially Distributed Data. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 3967-3974. https://doi.org/10.1609/aaai.v33i01.33013967

Issue

Section

AAAI Technical Track: Machine Learning