New Research Directions for the National Geospatial-Intelligence Agency Workshop Report (2010) / Chapter Skim
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APPENDIX E RESEARCH TOPIC NOTES OF WORKING GROUPS
APPENDIX E RESEARCH TOPIC NOTES OF WORKING GROUPS
Pages 49-60
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From page 49... ...
Develop situationally aware tools: need to have products and analysis tools suited to the purpose Analytic integration: – Using photogrammetry in the aid of social intelligence: (e.g., automated personal identification, crowd estimation, automatic generation of searchable maps) – Use of interactive systems, including gaming, needs to be leveraged by the geo spatial science in a whole different level to support decision science Need to move away from four traditional NGA core areas Blending of computer sciences, statistics, electrical and computer engineering, geodesy, geography, bioinformatics Integration of uncertainty and error into sensor models and analysis – Characterize multiple sources of uncertainty – Sensor errors – Confidence in data (subjective sources)
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From page 50... ...
Emphasize multi-sensor fusion and information extraction – Decrease uncertainty – Exploit redundant capabilities Greater utilization of state-of the art algorithms – Estimation theory – statistics and electrical engineering – Robust nonlinear optimization – numerical analysis – Statistical sensor measurement models - nonlinear filtering – Advanced software – Object oriented C++ Coordination with other government agencies – DARPA, Air Force, Army, Navy Exploitation of knowledge sources beyond image data mining; make relevant knowledge sources available; knowledge-based classification Enhance change analysis – beyond the process of measurement and classification to dynamics, behavior, and prediction (issue of sensor control and tasking) Need more than just the inanimate landscape, but also the dynamic, social environment (e.g., the flux of a living city)
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From page 51... ...
Need more comprehensive metadata Processing to support near-real time processing of constant data streams from drones and UAVs Need to blur processing distinctions between satellite, aerial, and terrestrial data acquisition systems Quality of information – Reliability and integrity of automatically generated spatial information – Scalability – More comprehensive use of supporting information (e.g., environmental) Quality assurance: system calibration, mission planning for different applications Quality control: verifying the quality of the different products at different levels (sensors, data, information, and knowledge)
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From page 52... ...
What about difficult environments where GNSS doesn't work? Establish a geodetic reference frame at sub-millimeter level; research needed at observational level; drives high performance computing research, etc.
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From page 53... ...
, physical domain, social domain, and knowledge domain with GEOINT Use game based analytics: explore data set in terms of games; analyze game strategy and pattern; use information for interview techniques Cognitive effectiveness of geo-spatial technology – Brain scans, MRI, eye/scan patterns, etc. logical physical Broader cross training of students in geo-spatial workforce … computer science, behavior, … – understanding … geophysics, geodesy, … – facilitating interdisciplinary training and research CROSS-CUTTING THEMES Forecasting Challenges – Predicting human behavior - relating social factors to physical factors.
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From page 54... ...
Modeling of human behavior – more interactive and real-time forecasting tools where problem domain is constantly changing. Use of normality modeling and anomaly detection as alternative to deductive based forecasting Computational modeling, prediction, and analysis are important research topics for the future – Potential to guide data collection and assimilation Participatory Sensing Uneven distribution of sensed data Privacy issues Crowd sourced data aggregation methods need to be developed Understanding when crowd sourcing is useful A very powerful way of collecting GIS data What about foreign countries or areas where you can't apply your structure?
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From page 55... ...
Research into security issues of participatory data Participatory sensing: Integration is important! – How to influence social media to generate data that is needed – Research to calibrate and judge quality of sensor in participatory sensing to allow decision making – Data fusion from this data with serious geo information?
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From page 56... ...
– Data fusion – Deal with users Science of interaction: Need to develop adaptive visual analytical methods to support geospatial users Beyond Fusion Data fusion – Relate to geo-space: represent spatial and non-spatial dimensions incorporate spatial structure: spatial variation or spatial correlation couple spatial and non-spatial algorithms
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From page 57... ...
– Applications to GI data not shown – Loss of visibility of space and time at "preferred" scales Powerful, but evaluation methods need to be developed Do not stand alone -- insight needs to be developed alongside – Analyst interaction important Also need methods to understand large disparate data bases – Interrelationships possibly not understood Both broader understanding and uncertainty reduction will likely require complementary, non-GI data Comparison of fusion algorithms from visual analytics with existing fusion algorithms Early fusion, mid fusion, late fusion Bayesian fusion algorithms Hard-soft fusion using hard sensor data and text, human generated, web derived information
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From page 58... ...
to use the same data for different classification algorithms. How to retrieve geospatial documents and extract geospatial information from text is still a challenge How to use existing geospatial ontologies to inform the information extraction process How to enable human computer interaction when complex modeling is involved Develop methodology to create heterogeneous benchmark data sets for research Formulation of standards for methodology and data structures Human Terrain Human landscape is a better term – human condition, biophysical conditions – Economy, sociology, transportation, anthropological, ethnic, religious, cultural, historical Geospatial, social, cultural data integration and analysis – More systematic approaches in collection, coding, displaying, understanding – Categorizing trivial and non-trivial data – Voluntary and non-voluntary contributors may not be aware of the consequence of making data available Data uncertainty, quality, consistency, reliability, disparity, fuzzy – Tools to filter and clean up data – Identify what data is necessary for a given task Collaborative tools for crowd-source data – Interactive tools Proper analysts with specialized knowledge – Human intervention to double check the quality (human in the loop)
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From page 59... ...
identify clues used in a language, relate the outcome in an analytical manner back to the spatial context (to know where the communication took place) Methods to enable analysis in native language Human terrain-based dynamic network analysis seems to serve well as one basis for structuring a broad range of social phenomenology in space-time – Representation and visualization in GI space an issue Quality assertion, quantification an issue – Highly disparate underlying data quality levels; need agreed ontology – NGA to develop technical and ethical best practices for collection?
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From page 60... ...
No stove-piping in 5 core areas – Also applies to cross-cutting areas – These areas blend together (look for and/or promote innovation at the intersection of these areas) Science development needs to be plugged into international science community
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