Natalia Flores
Hello, everyone! I’m Mariana Ortega, a dedicated professional specializing in the intelligent automation of Dynamic Geospatial Knowledge Graphs (DGKG). With a profound passion for integrating cutting - edge technologies into geospatial data management, I have spent [X] years at the forefront of this rapidly evolving field, driving innovation and efficiency across diverse industries.
My journey in DGKG intelligent automation began with a strong academic foundation in Geographic Information Systems (GIS) and a master's degree in Artificial Intelligence. This interdisciplinary background equipped me with the unique ability to bridge the gap between geospatial science and automation technologies. I quickly recognized the potential of DGKGs to transform how we understand, analyze, and utilize spatial data, especially when enhanced by intelligent automation.
In my career, I have led and contributed to numerous projects that showcase the power of DGKG - based intelligent automation. One of my notable achievements was developing a real - time traffic management system for a major city. By integrating IoT sensors, satellite imagery, and historical traffic data, I automated the construction and continuous update of a dynamic geospatial knowledge graph. Using machine learning algorithms, the system could predict traffic congestion with an accuracy rate of over 85% up to two hours in advance. This automation not only optimized traffic flow but also reduced travel time for commuters by an average of 20%.
Another significant project involved environmental monitoring. I designed an automated DGKG system that processed data from a network of air quality sensors, weather stations, and satellite - borne spectrometers. The system could automatically identify pollution sources, analyze the spread of pollutants, and predict environmental changes. As a result, local environmental agencies could make more informed decisions, leading to a 15% improvement in overall air quality within six months of implementation.
Beyond technical work, I possess strong project management and communication skills. I have successfully coordinated cross - functional teams, including geospatial analysts, data scientists, and software engineers, to ensure the seamless implementation of DGKG - based automation projects. I understand the importance of clear communication in translating complex technical concepts into actionable strategies for stakeholders.
In summary, I am excited about the endless possibilities that intelligent automation of Dynamic Geospatial Knowledge Graphs offers. I look forward to leveraging my skills and experience to drive further innovation, help organizations make data - driven decisions, and contribute to a more sustainable and intelligent future.
Integrate satellite remote sensing (such as Changguang satellite constellation, revisit period ≤ 1 day), drone clusters (equipped with laser radar, point cloud density 200 points/square meter) and IoT sensors (5G+ Beidou dual-mode positioning, accuracy up to centimeter level) to form a dynamic data collection matrix covering the entire area. For example, a smart city project generates 2TB of spatiotemporal data every day through 2,000+ smart cameras and 500 air quality sensors.
SuperMap Software's SkySense visual large model achieves intelligent recognition of seven types of land features, such as buildings and roads, through efficient fine-tuning, with an overall accuracy exceeding 80%, and five types reaching over 90%.
The BERT-geo pre-trained model was used to eliminate the differences in the expressions of "subway station" and "metro station", and OCR technology was used to extract the causal relationship of "road construction → traffic control" from engineering drawings, increasing efficiency by 10 times.