Planetary Defense (PD) has become a critical effort of protecting our home planet by discovering potentially hazardous objects (PHOs), simulating the potential impact, and mitigating the threats. Due to the lack of structured architecture and framework, pertinent information about detecting and mitigating near earth object (NEO) threats are still dispersed throughout numerous organizations. Scattered and unorganized information can have a significant impact at the time of crisis, resulting in inefficient processes, and decisions made on incomplete data. This PD Mitigation Gateway (pd.cloud.gmu.edu) is developed and embedded within a framework to integrate the dispersed, diverse information residing at different organizations across the world. The gateway offers a home to pertinent PD-related contents and knowledge produced by the NEO mitigation team and the community through (1) a state-of-the-art smart-search discovery engine based on PD knowledge base; (2) a document archiving and understanding mechanism for managing and utilizing the results produced by the PD science community; (3) an evolving PD knowledge base accumulated from existing literature, using natural language processing and machine learning; and (4) a 4D visualization tool that allows the viewers to analyze near-Earth approaches in a three-dimensional environment using dynamic, adjustable PHO parameters to mimic point-of-impact asteroid deflections via space vehicles and particle system simulations. Along with the benefit of accessing dispersed data from a single port, this framework is built to advance discovery, collaboration, innovation, and education across the PD field-of-study, and ultimately decision support.
[1]
Tricia Talbert.
Planetary Defense Coordination Office
,
2015
.
[2]
A. Doria.
Home
,
2016,
The Jerrie Mock Story.
[3]
Han Qin,et al.
An architecture for mitigating near earth object's impact to the earth
,
2017,
2017 IEEE Aerospace Conference.
[4]
Thomas S. Huang,et al.
A Smart Web-Based Geospatial Data Discovery System with Oceanographic Data as an Example
,
2018,
ISPRS Int. J. Geo Inf..
[5]
Thomas S. Huang,et al.
Towards intelligent geospatial data discovery: a machine learning framework for search ranking
,
2018,
Int. J. Digit. Earth.
[6]
Thomas S. Huang,et al.
A comprehensive methodology for discovering semantic relationships among geospatial vocabularies using oceanographic data discovery as an example
,
2017,
Int. J. Geogr. Inf. Sci..
[7]
Rajeev Motwani,et al.
The PageRank Citation Ranking : Bringing Order to the Web
,
1999,
WWW 1999.