CFP: Big Data PE & RS Special Issue

Geospatial Applications of Big Data Analytics

The volume of data created by an ever increasing number of remote sensing platforms and the capability of modern platforms to collect data at ever increasing spatial, spectral and radiometric resolutions currently exceeds petabytes of data per year and is only expected to increase. The variety of geospatial data available for adding valuable information to traditional remote sensing imagery, including social media and sensors as part of the so-called internet of things (IoT) promises to add information value if it can be effectively integrated. The velocity of all this information is critical, as the information value of all this information degrades if it can not be processed in a timely manner not only for military, intelligence, and other traditional consumers of geospatial data but also for the new breed of geospatial consumers in areas such as business analysis and logistics. Recent developments in information technology commonly referred to as 'Big Data' along with the related fields of data science and analytics will need to be brought to bear in order to process, analyze and realize the value of the overwhelming amount of geospatial data the remote sensing community is capable of generating.

This special issue of Photogrammetric Engineering and Remote Sensing (PE&RS) will focus on the application of advances in Big Data analytic techniques to geospatial applications of advances in Big Data analytic techniques to geospatial applications in the commercial, government and academic remote sensing communities. Papers covering topics including, but not limited to, the following are invited for consideration:

1) Technological advances in hardware, storage, data management, networking and computing models such as visualization and cloud computing for geospatial applications.

2) Creative uses of Big Data innovations such as MapReduce, Hadoop, Big Table and NoSQL in geospatial processing.

3) Usage of human-created, machine-created, structured and unstructured data in geospatial analytics including the integration of geospatial information for non-imaging sensors and the Internet of things (IoT) with more traditional forms of geospatial information.

4) Discovery of patterns in large volumes of geospatial data through analytic techniques such as data mining and predictive analytics in applications such as human geography.

5) Development of new processing algorithms to handle large volumes of data, for instance through applications of functional programming languages such as Lisp, R, and Clojure to geospatial applications.

6) Creation of new visualization products that increase the understanding of large and diverse forms of information.

Authors must prepare manuscripts according to the PE & RS Instructions to Authors, published in each issue of PE & RS and also avaliable on the ASPRS website at www.asprs.org/pers/AuthorInstructions . All submission will be peer-reviewed in accordance with PE & RS policy. Because of page limits, not all submission recommended for acceptance by the review panel may be included in the Special Issue. Under this circumstance, the guest editor will select the most relevant papers for inclusion in the special issue. Papers are revieed favorably, but will not fit with the Special Issue, can be revised and submitted for review as a new paper in the PE & RS Editor -in-Chief for possible publication in a future regular issue of PE & RS.

DEADLINE FOR SUBMISSION OF MANUSCRIPTS: 05/01/15

TENTATIVE PUBLICATION DATE: 04/01/16

Guest Editors: Harold Cline, Tomra Sorting Solutions, Mining ; David Alvarez, Woolpert

PLEASE E-MAIL YOUR MANUSCRIPT DIRECTLY TO:

Harold Cline

email [email protected]

phone: (720) 361-9033

Important Dates

Manuscript Due: 05/01/15

Decisions to Authors 09/01/15

Final Papers Due: 10/01/15

Publication 04/01/16

Attached is also the formal pdf version of this.