Conflation of geospatial points of interest and ground-level imagery

A process comprising:

• harvesting geo-tagged ground-level images through a scene scene detection algorithm executed by an image classifier;
• receiving point of interest data representing structures or events through a transceiver;
• tagging each of the geo-tagged ground-level images with a probability object describing a tag classification;
• tagging each of the point of interest data with a hierarchical level genre classification object;
• encoding tagged geo-tagged ground-level images as encoded vectors to render a first plurality of nodes and a first edge on a proximity graph;
• encoding tagged point of interest data as a similarity vector to render a second plurality of nodes and a second edge on the proximity graph associated with the first node by a calculated semantic distance measurement;
• splitting the proximity graph into a training subgraph and a testing subgraph;
• initiating a training session of a neural network by processing the training subgraph during a training session that includes aggregating information at a selected node within the training subgraph by sampling information from a neighboring node within the training subgraph;
• and
• terminating the training session when a binary cross-entropy loss measurement based on the training subgraph falls below a predetermined threshold.

An edge prediction system comprising:

• an image classifier that harvests geo-tagged ground-level images through one or more scene scene detection algorithms;
• a transceiver that receives point of interest data representing structures or events through a transceiver;
• a processor that tags each of the geo-tagged ground-level images with a probability object describing a tag classification and tags each of the point of interest data with one of a plurality of hierarchical level genre classification objects;
• an encoder that encodes tagged geo-tagged ground-level images as hot encoded vectors to render a first plurality of nodes and a first edge on a proximity graph, and encodes tagged point of interest data as a similarity vector to render a second plurality of nodes and a second edge on the proximity graph associated with the first plurality of nodes by a calculated semantic distance measurement;
• a partitioning engine that splits the proximity graph into a training subgraph and a testing subgraph;
• a training engine that processes the training subgraph through a neural network during a training session that includes aggregating information at a plurality of selected nodes within the training subgraph by sampling information from one more neighboring node within the training subgraph, and terminates the training session when a binary cross-entropy loss measurement based on the training subgraph falls below a predetermined threshold;
• and
• a testing engine that evaluates the neural network by processing the testing subgraph.