Geospatial ai method and system for area-based risk and value assessment

A computer system programmed for artificial intelligence (AI) based risk/value assessment of a geographic area, the computer system comprising one or more hardware processors which, alone or in combination, are configured to provide for execution of the following steps:

• performing feature engineering to contextually enrich collected data;
• generating three datasets from the contextually enriched data, wherein a first dataset is generated by combining positive samples of the contextually enriched collected data with hard negative samples of the contextually enriched data, a second dataset is generated by combining the positive samples with soft negative samples of the contextually enriched data, and a third dataset is generated by combining the positive samples, hard negative samples, and soft negative samples;
• and
• training a machine learning model to generate three different types of predictions for the risk/value assessment of the geographic area based on the three generated datasets.

A tangible, non-transitory computer-readable medium for artificial intelligence (AI) based risk/value assessment of a geographic area, the computer-readable medium having instructions thereon, which, upon being executed by one or more processors, provides for execution of the following steps:

• performing feature engineering to contextually enrich collected data;
• generating three datasets from the contextually enriched data, wherein a first dataset is generated by combining positive samples of the contextually enriched collected data with hard negative samples of the contextually enriched data, a second dataset is generated by combining the positive samples with soft negative samples of the contextually enriched data, and a third dataset is generated by combining the positive samples, hard negative samples, and soft negative samples;
• and
• training a machine learning model to generate three different types of predictions for the risk/value assessment of the geographic area based on the three generated datasets.