Center for Geospatial Intelligence

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The Center for Geospatial Intelligence (CGI) is an interdisciplinary center at the University of Missouri - Columbia (MU) that currently involves 15 faculty and dozens of other researchers in Electrical & Computer Engineering, Computer Science, Geography, Civil & Environmental Engineering, and Geological Sciences. The center was formally established in February 2004 and is designated and supported as a "Signature Program" by the MU College of Engineering. The center maintains a multi-million dollar R&D program at MU in the areas of satellite, airborne, and ground remote sensing; automated feature extraction; change detection; target detection, recognition, geolocation, and tracking; video surveillance; computer vision; intelligent databases and information retrieval; advanced geospatial data processing; geoweb applications and services; and the detection and characterization of underground structures. Since 2007, the CGI has been located in a $2.5M, 6500 sq. ft., state-of-the-art facility that contains a wide variety of specialized laboratories dedicated to advanced geospatial R&D.  By leveraging the multi-disciplinary research skills of its faculty and its unique facilities, the CGI conducts leading-edge research focused on geospatial intelligence needs critical for national security, homeland defense, and military combat support.

 

GeoCDX Change Detection Used for Joplin Tornado Damage Assessment

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Joplin, MO GeoCDX Change DetectionIn response to the recent tornado that destroyed portions of Joplin, MO, the Center for Geospatial Intelligence used GeoCDX—its fully-automated change detection system—to produce a rapid assessment of the destruction.  This information was then passed on to state agencies for their use.  The following overview image clearly shows the path of the tornado from the lower left corner to the right edge. This result was produced using a 2010 NAIP image and MJ Harden imagery collected just days after the tornado.

Using the GeoCDX change map, The Missouri Spatial Data Information Service produced two maps that isolate the tornado path and provide the ability for more detailed inspection (original and recolorized).  Additionally, ESRI also produced a mapping service that illustrates the extent of the damage within the path of the tornado.

 

Paper wins IEEE Geoscience and Remote Sensing Society 2011 Interactive Symposium Prize

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IEEEOzy Sjahputera presented the paper "Clustering of Detected Changes in Satellite Imagery Using Fuzzy C-Means Algorithm" at IGARSS 2010 in Honolulu, HI.  This paper was co-authored by Grant Scott, Matt Klaric, Brian Claywell, Nick Hudson, James Keller and Curt Davis.  On April 21, 2011 this paper was selected for the IEEE Geoscience and Remote Sensing Society 2011 Interactive Symposium Prize Paper Award.  This Award consists of a certificate and a shared honorarium.  The Award will be presented at the Banquet of IGARSS 2011 in Vancouver on Thursday, July 28, 2011.  Additionally, a journal paper will appear in the IEEE Transactions on Geoscience and Remote Sensing later this year.

 

Dr. Tony Han Receives NGA NURI Award

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Dr. Tony Han was awarded an NGA NURI grant for a project entitled Video Positioning System: A Hierarchical Video Retrieval Approach for Multimodal Source.

In this research project, we propose to investigate advanced theories and algorithms to infer the geolocations of videos captured by hand-held DVs or cell phones. The inference of the geolocation of a hand-held/cell phone video can be achieved through a hierarchical video retrieval approach as shown in the figure below. A hand-held video of interest is treated as a query video clip in the framework of video retrieval. Using the hand-held video as the query video clip, a video search is carried out in a video database, in which the geolocation of each video clip is available. We will infer the geolocation of the query video clip from the geolocations of the retrieved video clips.

 

Dr. Ye Duan Receives NGA NURI Award

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Dr. Ye Duan was awarded an NGA NURI grant for a project entitled Geometry & Statistics Driven Point Clouds Compression.

The goal of this project is to develop geometry and statistics driven LIDAR point-cloud compression methods which combine both application knowledge and scene content to enable effective storage and retrieval, fast transmission from the sensor platform, faster exploitation algorithms and visualization while preserving geometric properties of objects within a scene. The proposed system will be robust against noise, clutter, and is applicable to scenes of varying complexity. The proposed system will also be very useful for applications such as path/route planning, vertical obstructions detection, and landing zones identification, etc.

 
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