ORNL currently applies GeoAI methods to address several national security applications including infrastructure mapping, change detection, damage assessment, socioeconomic analysis, population dynamics, geomatics, maritime safety, geospatial data privacy and trustworthiness of AI systems. In an era where AI has become fundamental and an integral part of new science breakthroughs and decision making in our society we are forming alliances and collaborating to support geospatial science and human security missions.
The World Spatiotemporal Analytics and Mapping Project (World STAMP) applies advanced analytics to information from more than 30 global datasets to identify trends, patterns, anomalies, and changes in national landscapes. Oak Ridge National Laboratory seeks to build anticipatory algorithms that will evaluate future scenarios and outcomes as well as detect geopolitical environments at risk for change or conflict. Applications include alerts to anomalies and emerging conditions of strategic importance, as well as testing of hypothetical scenarios.
With over two decades of experience in creating foundational data layers, ORNL continues to exploit overhead imagery toward developing scalable ways to characterize a wide variety of socioeconomic neighborhoods by using artificial intelligence methods and high performance computing resources. The foundational datasets are being used in applications that include identification of zones for economic stimulus, mapping of unstructured settlements, studying spatial similarities between cities, and supporting population distribution studies.
Automated feature extraction from geospatial data requires constant retraining of machine-learning algorithms on the best available data. This method can cause loss of accuracy due to low-level noise and varying regional conditions. The Remote Sensing Flow (RESFlow) approach partitions images into groups based on similarities and localizes context into buckets to expedite analysis by orders of magnitude while maintaining accuracy.
Automatic feature extraction at scale enables streamlined processing of vast amounts of remote sensing data at high spatial and temporal resolution with minimal human involvement. Oak Ridge National Laboratory employs machine learning, computer vision, and high-performance computing resources toward automated feature extraction under multimodal sensing sources, scarce training data and distribution shifts, to create foundational data layers for building footprints, road networks and solar panels.
This project applies a Bayesian learning approaches to estimate building occupancy at varying times of day and night across a variety of sociocultural environments. A baseline model draws on experiential accounts, prior inferences, and allowances for uncertainty to produce an ensemble distribution of likely building occupancies across a taxonomy of more than 55 facility categories.
Using methods developed by ORNL scientists, Geomatics researchers are adapting artificial intelligence techniques to uncover and better understand complex relationships between geological and topological density patterns. BY incorporating AI into the techniques, researchers are demonstrating the possibility of producing high-resolution gravity maps on a global scale with consistently reliable quality at reduced cost and effort. The resulting Gravity maps are aimed at supporting navigation systems, early detection of potential earthquakes, and measurement of changes in water patterns.
Oak Ridge National Laboratory researchers are designing automated solutions that incorporate advanced database, geoprocessing, and machine-learning techniques to match and conflate features representing real-world objects across a wide array of large and disparate datasets. Applications include automated development of digital nautical charts at unprecedented detail.