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 data 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 in the commercial, government and academic remote sensing
communities. Papers covering topics including, but not limited to, the following are
invited for consideration:
•
Technologic advances in hardware, storage, data management, networking
and computing models such as virtualization and cloud computing for
geospatial applications.
•
Creative uses of Big Data innovations such as MapReduce, Hadoop, Big
Table and NoSQL in geospatial processing.
•
Usage of human-created, machine-created, structured and unstructured
data in geospatial analytics including the integration of geospatial
information from non-imaging sensors and the Internet of Things (IoT)
with more traditional forms of geospatial information.
•
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.
•
Development of new processing algorithms to handle large volumes of data,
for instance through application of functional programming languages such
as Lisp, R, and Clojure to geospatial applications.
•
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 available on the ASPRS
Website at
. All submissions will be
peer-reviewed in accordance with
PE&RS
policy. Because of page limits, not all
submissions recommended for acceptance by the review panel may be included
in the Special Issue. Under this circumstance, the guest editors will select the
most relevant papers for inclusion in the special issue. Papers that are reviewed
favorably, but will not fit within the Special Issue, can be revised and submitted
for review as a new paper to the
PE&RS
Editor-in-Chief for possible publication in
a future regular issue of
PE&RS
.
Geospatial Appl ications of Big Data
Analytics
G
uest
E
ditors
:
Harold J Cline, Tomra Sorting
Solutions, Mining
David Alvarez, Woolpert
P
lease
e
-
mail
your
manuscript
directly
to
:
Harold Cline
Email:
Phone: (720) 361-9033
I
mportant
D
ates
:
Manuscripts Due: 05/01/15
Decision to Authors: 09/01/15
Final Papers Due: 10/01/15
Publication: 04/01/16
D
eadline
for
S
ubmission
of
M
anuscripts
: 05/01/15
T
entative
P
ublication
D
ate
:
04/01/16
C
all
for
P
apers
