Multi-scale global land use model based on points of interest

A method implemented by a data processing system for characterizing land use within an area of interest (AOI), the method comprising:

• retrieving, from multiple geospatial data sources, information regarding points of interest (POIs) within a defined AOI, where the information includes spatial coordinates and categorical identifiers for each POI;
• applying a standardized tagging schema to normalize categorical identifiers across POIs from different data sources, creating a unified semantic format for subsequent processing;
• constructing a multi-level spatial hierarchy by:
• accessing hierarchical boundary data representing physical infrastructure, such as road networks, associated with the AOI, the hierarchy comprising multiple spatial levels representing progressively broader regions within the AOI;
• and
• segmenting the AOI into nested polygons according to hierarchical boundary levels, creating a spatial structure where each polygon at a given level is nested within polygons at higher levels;
• mapping each POI to a set of spatial polygons across hierarchical levels that contain the POI's spatial coordinates, generating hierarchical associations that encode both local and regional spatial context;
• generating a high-dimensional semantic vector for each POI based on its standardized categorical identifier and assigned hierarchical polygons, wherein each vector captures both the spatial and semantic attributes of the POI;
• computing an aggregate AOI embedding by combining the semantic vectors of POIs located within the AOI, applying term frequency-inverse document frequency (TF-IDF) weighting to emphasize distinctive POIs relevant to the AOI's land use characteristics;
• training a classifier model using the computed AOI embeddings from multiple AOIs labeled with land use categories, the model configured to predict land use type based on the spatial and semantic information encoded in the AOI embeddings;
• and
• using the trained classifier model to predict a land use category for the AOI by analyzing the aggregate AOI embedding.

A system for predicting land use categories within a user-selected area of interest (AOI), the system comprising:

• a data processing apparatus configured to perform operations comprising:
• retrieving, from multiple geospatial data sources, information regarding points of interest (POIs) within a defined AOI, the information comprising spatial coordinates and categorical identifiers for each POI;
• applying a standardized tagging schema to normalize categorical identifiers across POIs from diverse data sources, creating a unified semantic format for subsequent processing, including the removal of duplicate POIs and integration of POIs based on their harmonized attributes;
• constructing a multi-level spatial hierarchy by:
• accessing hierarchical boundary data representing physical infrastructure, including road networks, associated with the AOI, the hierarchy comprising spatial levels that represent progressively broader regions within the AOI, and
• segmenting the AOI into nested polygons according to the hierarchical boundary levels, creating a spatial structure where each polygon at a given level is nested within polygons at higher levels;
• mapping each POI to a set of spatial polygons across hierarchical levels that contain the POI's spatial coordinates, thereby generating hierarchical associations that encode both local and regional spatial context for each POI;
• generating a high-dimensional semantic vector for each POI based on its standardized categorical identifier and assigned hierarchical polygons, wherein each vector captures both spatial and semantic attributes of the POI;
• computing an aggregate AOI embedding by combining the semantic vectors of POIs within the AOI, applying term frequency-inverse document frequency (TF-IDF) weighting to emphasize distinctive POIs relevant to the AOI's land use characteristics;
• training a classifier model using the computed AOI embeddings from multiple AOIs labeled with land use categories, the model configured to predict land use type based on the spatial and semantic information encoded in the AOI embeddings;
• and
• a user interface configured to enable a user to:
• interact with an interactive map displayed on the user interface to navigate, zoom, and pan across different geographic regions to locate an AOI of interest;
• select and define the AOI within the map by specifying boundaries at varying spatial granularities through hierarchical polygon levels, each selection dynamically updating the classifier model's analysis based on the harmonized POI data within the selected AOI;
• refine the AOI boundaries within the selected granularity level by adjusting the AOI's spatial extent on the map, thereby customizing the area covered in the classifier model's land use prediction;
• initiate a prediction operation to receive a displayed output of the predicted land use category for the selected AOI, generated by the classifier model and derived from the unified semantic and spatial embeddings of POIs within the AOI;
• and
• display confidence metrics for the predicted land use category, save the selected AOI, its predicted category, and associated prediction details, facilitating comparative analysis across multiple AOIs at varying spatial granularities.