GPS2Vec

GPS coordinates are fine-grained location indicators that are difficult to be effectively utilized by classifiers in geo-aware applications. Previous GPS embedding methods are mostly tailored for specific problems that are taken place within areas of interest. When it comes to the scale of the entire planet, existing approaches always suffer from extensive computational cost and significant information loss. To solve these issues, we present a novel two-level grid based framework to learn semantic embeddings for geo-coordinates worldwide. The Earth's surface is first discretized by the Universal Transverse Mercator (UTM) coordinate system. Each UTM zone is next processed as a local area of interest that is further divided into fine-grained cells to perform the initial GPS encoding. We train a neural network in each UTM zone to learn the semantic embeddings from the initial GPS encoding. The training labels can be automatically derived from large-scale geotagged documents such as tweets, check-ins, and images that are available from social sharing platforms. We evaluate the effectiveness of our proposed GPS embeddings in geotagged image classification. Improved classification results have been obtained based on a simple early feature fusion technique.