The impact of Geographic Information Systems (GIS) is rapidly expanding due to the revolutionary improvements in mapping and monitoring technologies as well as near real-time distribution of data using the Internet and wireless technology. Laser altimetry (LIDAR), Real Time Kinematic GPS (RTK-GPS), digital photogrammetry, interferometric sonar and multispectral imagery greatly enhance the capabilities to gather georeferenced data at unprecedented spatial and temporal resolutions. While the state-of-the-art mapping technologies enable the development of critically important applications, such as environmental and disaster management or homeland security support systems, many challenges need to be overcome to make these applications a reality. One of the main challenges is the development of robust, efficient algorithms for terrain modeling and analysis that can handle massive datasets acquired by different technologies (with different properties such as accuracy, density, spatial distribution) and that can rapidly detect and predict changes in the model as the new data is acquired.
The goal of the proposed research is to provide enhanced terrain modeling and analysis capabilities by developing scalable algorithms that function with massive non-standard datasets, such as point clouds, and that produce a confidence level for the results. It will provide core input data at an unprecedented level of detail and accuracy, as well as improved algorithms, for a wide range of models and simulations such as surface hydrology and flood forecasting. We propose to utilize:
This work is funded by the U.S. Army Research Office under award W911NF-04-1-0278.