2022-2023 Topic: Geospatial Intelligence (GEOINT) for Climate Monitoring
How can GEOSPATIAL Intelligence data be used to monitor, assess, and predict the impact of climate change?
Geospatial Intelligence (GEOINT) is the information obtained for a particular geographical location through the exploitation of imagery and geospatial data. GEOINT uses overhead imagery from various Electro Optical/Infrared (EO/IR) sensors (images) combined with imagery analysis (determining what is in the image) and other geospatial data (characteristic reference information for the location, e.g., elevation, road and utility networks, vegetation, population, geodetic data, etc.). This combination provides the situational awareness of what is occurring or changing at a particular location. In this regard, GEOINT can monitor the effects of climate change. For instance, it would be possible to monitor the amount of precipitation within a region and correlate that with water usage to predict potential shortages which may lead to major population disruptions and potential conflicts. This effort is seeking innovative ways and means to employ GEOINT capabilities to monitor climate change.
It seeks not only to identify the effects of climate change but to correlate these events with activities and patterns to predict areas and regions of concern. It is expected that innovative ideas are supported with examples, simulations or other means to validate the approach along with identification of the types of data sources used and accessed.
Competing against two other finalists – one team from the University of Alabama at Birmingham (UAB) and one from the University of Texas at San Antonio (UTSA), Team BHAM_CS from UAB took home the $25,000 grand prize for their first-place finish. Their submission, “Automating Flood Extent Mapping on Earth Imagery Using Elevation-Guided AI Technology,” was the winning project. Congratulations to student lead Saugat Adhikari, faculty lead Da Yan, and student team members Mirza Sami and Jalal Khalil.
Team BHAM_CS examined the problem of automating the mapping of the extent of flooding on earth imagery using Artificial Intelligence (AI) technology. Their approach rapidly and efficiently used satellite imagery of disaster areas and 3-D elevation mapping to determine the extent of flooding. They would segment the map and apply their AI approach to the separate segments to determine the susceptibility to flooding for the different segments of the image. This alleviates the problem of relying on elevation alone as the key to predicting flooding. They then refined the image using a graphical model with a hidden Markov tree. The result is an accurate prediction of the extent of flooding, which disaster management organizations can use to help guide their response.