Résultats du processus de synthèse de l'article arXiv : 1805.02855v1

Résumé : Remote sensing 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 geospatial data. We demonstrate empirically that Tile2Vec learns semantically meaningful representations on three datasets. Our learned representations significantly improve performance in downstream classification tasks and similarly to word vectors, visual analogies can be obtained by simple arithmetic in the latent space.