Anaheim Convention Center
Anaheim, California, United States
26 - 30 April 2020
Conference SI113
Geospatial Informatics X
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Abstract Due:
16 October 2019

Author Notification:
20 December 2019

Manuscript Due Date:
1 April 2020

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Conference Chairs
Program Committee
  • Hadi AliAkbarpour, Univ. of Missouri-Columbia (United States)
  • Alex Aved, Air Force Research Lab. (United States)
  • John A. Berger, Toyon Research Corp. (United States)
  • Arnav Bhavsar, Indian Institute of Technology Mandi (India)
  • Erik Blasch, Air Force Office of Scientific Research (United States)
  • Prasad Calyam, Univ. of Missouri-Columbia (United States)
  • May V. Casterline, NVIDIA (United States)
  • John T. Dolloff, Integrity Applications, Inc. (United States)
  • Flavio Esposito, Saint Louis Univ. (United States)

Program Committee continued...
  • Isabel Figueiredo, Univ. de Coimbra (Portugal)
  • Hirsh Goldberg, Johns Hopkins Univ. Applied Physics Lab., LLC (United States)
  • Jutta E. Hild, Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung (Germany)
  • John M. Irvine, Draper Lab. (United States)
  • Raju Namburu, U.S. Army Research Lab. (United States)
  • Ram M. Narayanan, The Pennsylvania State Univ. (United States)
  • Shibin Parameswaran, Naval Information Warfare Ctr. Pacific (United States)
  • Raghuveer M. Rao, U.S. Army Research Lab. (United States)
  • Andreas Savakis, Rochester Institute of Technology (United States)
  • Jason S. Schwendenmann, National Geospatial-Intelligence Agency (United States)
  • Clark N. Taylor, Air Force Institute of Technology (United States)
  • William R. Thissell, Deftec Corp. (United States)
  • Chris M. Ward, Naval Information Warfare Ctr. Pacific (United States)

Call for
Geospatial Informatics is the science and technology that develops and uses information science and technology to address applications in the geospatial and geosciences. Recent trends in big data, visual analytics and cloud computing, small satellite technologies, wide availability of low-cost drones, innovations in sensors and the exponentially increasing volumes of geo-aware multi-sensor data streams for layered sensing are driving the development of novel methodologies and tools for integrating and exploiting multi-dimensional (temporal, spatial, and spectral) geospatial information. Geospatial information systems (GIS) combined with spatiotemporal data streams from sensor networks, social networks and ancillary information are enabling new insights and pattern discovery in large environmental, defense, and civil datasets that was not previously possible.

GIS is an essential analysis tool to support decision making from time-varying spatial information. Today, defense and civil applications, such as space-based satellite imaging, airborne/unmanned airborne systems (UAS), navigation for autonomous vehicles, terrestrial and maritime-based security systems, are rapidly transforming their focus from volume to value. From a traditional collect-and-view paradigm, that simply “takes pictures” to commercial high value, fully-capable GIS, that incorporate multi-sensor collections, perform advanced processing and analytics in real-time, initiate sensor cross-cueing, and allow multiple user communities to collaborate, rapidly retrieve and disseminate information with improved accuracies.

Exploitation of remote sensing data, and temporal data cubes for change analysis, are essential components of the evolving Geospatial Informatics field. Geospatial Informatics and remote sensing data analytics are critical technologies that enable defense and civil data providers to satisfy emerging demands in monitoring and security, for rapid access to information for situational awareness, for forensic retrospective analysis to track past change, and to develop decision models for anticipating future change.

Visual or geospatial cloud computing is becoming an enabling technology for large area mapping and in disaster response using small aerial and ground mapping systems with computing at the edge that have limited endurance and communication links. Algorithms, processing chains, work flows, data access, network routing and distributed processing need to be adapted and optimized for visual cloud and fog computing applied to streaming data with high data volume and variety but limited bandwidth, computational resources and node availability.

This conference provides a central collaboration point for industry, government, and academic leaders of geospatial informatics, GIS and remote sensing data analytics technologies to share their advancements, learning, and new solutions in algorithms, data integration architectures and standards, and big data science and cloud computing instrumental for achieving predictive analytics.

Topic areas include, but are not limited to:

Geospatial Big Data Science, Algorithms and Data Visualization
  • detection and categorization of image features
  • multisensor data fusion (VIS, IR, LIDAR, RADAR, SAR, etc.)
  • multi- and hyperspectral data analysis
  • 3D urban reconstruction and point cloud processing
  • geospatial sourcing, human geography and behavior
  • activity based/anticipatory intelligence
  • predictive analytics for modeling and decision making
  • autonomous mobile mapping systems
  • geospatial contextual data and social networks
  • geopositioning, pose estimation, error propagation, and uncertainty characterization
  • augmented reality (AR)/virtual reality (VR) systems for geospatial data visualization.
Environmental Sensing, Ecosystem Science and Monitoring
  • drone-based mapping and aerial networking
  • naval and marine applications of machine learning
  • sensor and data management technologies to support sustainable land imaging strategies
  • machine learning methods for land monitoring and change analysis
  • implementation of temporal data cubes for community sharing
  • assessment of user needs to inform future sensor system designs
  • geometric and radiometric calibration and validation methods
  • applications including water quality, agriculture, wildlife, mining, forestry, oil and gas
  • GPU-based real time processing
  • mobile apps, cognitive interfaces and human factors.
Full Motion Video Analytics
  • deep learning for vision
  • cloud-based video analytics, processing and dissemination
  • image, video and target track intelligence
  • motion imagery standards and quality metrics
  • motion imagery tagging, geopositioning
  • large volume streaming data, wide area motion imagery
  • next-generation video, stereo, multiview 3D
  • precision navigation, geolocalization, visual odometry, SLAM
  • automatic target recognition
  • target tracking in dense, urban environments.
Geospatial Informatics Applications
  • autonomous vehicle mapping and navigation
  • visual geospatial cloud computing
  • geospatially aware cyber-physical systems and cybersecurity
  • urban planning, disaster response, search and rescue
  • smart cities and smart health
  • social networks, geospatial databases for data mining
  • infrastructure inspection such as bridges and construction sites
  • food, energy, water sustainable practices and policies
  • crop phenotyping, methane detection, marine ecosystem resilience
  • model-based image and video compression
  • artificial intelligence and deep learning
  • ground-truthing, crowd sourcing tools and challenge datasets
  • automatic building detection and segmentation
  • real estate development, zoning, state and local government mapping
  • cultural heritage studies, archaeology.
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