Intelligent Automation of Geospatial Knowledge Graphs

Geospatial Automation

Exploring intelligent automation in dynamic geospatial knowledge graphs.

An aerial view of a landscape featuring a network of roads intersecting over a patchwork of greenery and waterways. A river or large body of water is visible, with several smaller streams branching out. There are clusters of buildings and fields, as well as dense areas of trees and vegetation.

Introduce a time-series graph database (such as TigerGraph) to store dynamic attributes of entities, and regularly update attribute values through automated scripts. For example, in the commercial real estate knowledge graph, the mall traffic data can be automatically refreshed daily based on mobile phone signaling data to form a dynamic triple of "mall→time period→passenger traffic".

Use change detection algorithms (such as remote sensing image difference analysis) to identify changes in geographic entities and automatically trigger the knowledge graph update process. A natural resource monitoring system can automatically detect deforestation areas and update vegetation coverage entity attributes through satellite image comparison, shortening the response time from 2 weeks of manual processing to 48 hours.

woman wearing yellow long-sleeved dress under white clouds and blue sky during daytime

Spatiotemporal dynamism

The location, attributes, and relationships of geographic entities (such as transportation facilities, buildings, and natural phenomena) change over time and need to be dynamically updated through real-time sensor data (such as GPS, remote sensing images, and IoT devices) and historical data (such as satellite images and urban planning documents). For example, the SuperMap AIF technology base uses the built-in remote sensing interpretation pre-trained model (LIM) to achieve real-time processing of remote sensing images, automatically detect changes in objects, and update entity information in the knowledge graph.

an abstract photo of a curved building with a blue sky in the background

Multi-source data access: Real-time acquisition of GPS tracks, remote sensing images, traffic flow and other data through API interfaces, message queues (such as Kafka) or distributed file systems (such as HDFS), and support batch import of static data (such as OpenStreetMap, POI data).

Automated cleaning and conversion: Use AI models (such as G-SAM visual large model) to remove clouds and correct geometric deformation in remote sensing images, and use NLP technology to parse geographic entities (such as place names, institution names) in text and associate coordinate information.