Abstract: Techniques are described that include accessing information about points of interest and images of scenes within the area of interest; encoding the information about each scene image as a respective scene-image vector; encoding the information about each point of interest as a respective point-of-interest vector; constructing a joint semantic graph having nodes and edges by (i) attributing to each node a respective point-of-interest vector or a respective scene-image vector, (ii) determining semantic distances between pairs of point-of-interest vectors, pairs of scene-image vectors, and pairs formed from a point-of-interest vector and a scene-image vector, and (iii) connecting each node with respective edges to a predetermined number of nearest-neighbor nodes having respective vectors with lowest semantic distances to each other. The constructed joint semantic graph can be used to enrich and/or clean the information about the points of interest and/or the images of scenes within the area of interest.

Abstract: A prediction system harvests geo-tagged ground-level images through one or more algorithms. The system receives point of interest data representing structures or events and tags the geo-tagged ground-level images with a probability describing a classification. The system tags point of interest data with a hierarchical genre classification and encodes the tagged geo-tagged ground-level images as vectors to form nodes and edges in a proximity graph. The system encodes tagged points of interest data as similarity vectors to render more nodes and more edges on the proximity graph associated with the tagged geo-tagged ground-level images nodes by calculated semantic distances. The system splits the proximity graph into a training subgraph and a testing subgraph and trains a neural network by aggregating and sampling information from neighboring nodes within the training subgraph graph and validates through the testing subgraph. Training ends when a loss measurement is below a threshold.

Abstract: A prediction system harvests geo-tagged ground-level images through one or more algorithms. The system receives point of interest data representing structures or events and tags the geo-tagged ground-level images with a probability describing a classification. The system tags point of interest data with a hierarchical genre classification and encodes the tagged geo-tagged ground-level images as vectors to form nodes and edges in a proximity graph. The system encodes tagged points of interest data as similarity vectors to render more nodes and more edges on the proximity graph associated with the tagged geo-tagged ground-level images nodes by calculated semantic distances. The system splits the proximity graph into a training subgraph and a testing subgraph and trains a neural network by aggregating and sampling information from neighboring nodes within the training subgraph graph and validates through the testing subgraph. Training ends when a loss measurement is below a threshold.