Construction of 3-D Terrain Models from BIG Data Sets

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$149,443.00
Award Year:
2013
Program:
STTR
Phase:
Phase I
Contract:
W911NF-13-9-0018
Award Id:
n/a
Agency Tracking Number:
A13A-005-0266
Solicitation Year:
2013
Solicitation Topic Code:
A13A-T005
Solicitation Number:
2013.A
Small Business Information
2400 Huntscroft Ln, Apt 203, Raleigh, NC, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
078886990
Principal Investigator:
PankajAgarwal
Professor
(919) 660-6540
pankaj@cs.duke.edu
Business Contact:
ThomasMoelhave
CEO
(848) 467-6686
thomas@moelhave.com
Research Institute:
Duke University
Pankaj K Agarwal
Department of Computer Science
Box 90129
Durham, NC, 27708-
(919) 660-6540

Abstract
The objective of this proposal is to design, analyze, and implement scalable algorithms for analysis-driven construction of high-resolution 3D terrain models from BIG terrain data sets, and to build a software infrastructure for making analysis-prepared terrain models available to data consumers on multiple platforms. Analysis-driven modeling means that the construction of the model is influenced by, and adapted for, the specific analysis that the terrain model will be used for by data consumers. The algorithms for constructing terrain models will be capable of handling heterogeneous and dynamic data. To handle large volumes of terrain data efficiently, the computational techniques will optimize both the CPU running time and the data communication cost. Models and algorithms will be developed that can construct hierarchical models at different levels of detail. Analysis-driven denoising algorithms, using techniques from persistent homology and machine learning, will be developed to handle noise in the data, and probabilistic models will be developed to handle uncertainty in data and to attach confidence levels to various features computed by the algorithm. Finally, computational methods and software infrastructure will be developed to make terrain models prepared for analysis available to data consumers on multiple different platforms.

* information listed above is at the time of submission.

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