Call for Papers
ISPRS Journal of Photogrammetry and Remote Sensing
“Data Analytics: Meeting the challenges of Big Geo Data”
Charles Toth (The Ohio State University, USA)
Jan Dirk Wegner (ETH, Zurich, Switzerland)
Cheng Wang (Xiamen University, China)
Onisimo Mutanga (University of KwaZulu-Natal, South Africa)
Planned publication date: Summer 2020
The need for accurate and current geospatial data is rapidly growing, and “Location-aware” datasets and applications are creating a revolution in the way we live. For example, augmented and virtual reality (AR, VR) systems can now analyze an individual’s field of view to provide information such as the identification of items and their properties, recommendations of actions, or many other assistive functions. Visualizations of time-evolving 3D or higher-dimensional representations of location-attributed datasets are enabling new insights in science and engineering. The fundamental datasets underlying these new toolsets are acquired from an ever-growing set of sensors and data collection devices, each providing data with specific sensor-determined resolution, latency, and error properties. Applications include autonomous vehicles, smart cities, human/machine collaborative manufacturing, disaster and environmental management, medical and geriatric assistive functions, defense operations and surveillance, and a plethora of others. The availability and continued development of technologies for acquiring, manipulating, and understanding these “Big Geo Data” are driving new applications and industries that will have major societal impacts over the coming decades and beyond. Machine learning in conjunction with Big Geo Data will redefine the way we view relationships between natural, human, and artificial intelligence processes.
Data analytics applies fundamental scientific principles to the analysis of large, complex data sets, and offers new tools to handle the ever-growing geospatial data that conventional techniques cannot. Machine learning, in particular deep learning, forms a core element of many methods used in computer vision, 3D object reconstruction, scene interpretation, data mining, etc. Convolutional neural networks trained on large image or point cloud datasets with architectures that are custom-tailored for specific applications and environments are becoming ubiquitous. More importantly, these techniques offer a mechanism to directly identify and label patterns by learning adequate features directly from the data, which is essential for information extraction from Big Geo Data. These developments likely will have a strong impact on the theory, development and operational use of photogrammetry and remote sensing.
In this context, this theme issues will review and document recent developments in photogrammetry and remote sensing in the form of state-of-the-art papers in research, development and operational use. You are cordially invited to contribute to this special issue by submitting original research contributions and review articles providing an overview of one of the sub-fields of our discipline.
Papers must be original contributions, not previously published or submitted to other journals. Papers published or submitted for publication in conference proceedings may be considered provided that they are considerably extended and improved. Substantive research and relevant-for-practice papers will be preferred. Papers must follow the instructions for authors at http://www.elsevier.com/journals/isprs-journal-of-photogrammetry-and-remote-sensing/0924-2716/guide-for-authors.
Please submit the full manuscript to http://ees.elsevier.com/photo/default.asp by May 15, 2019.
Prof. Charles Toth
Department of Civil, Environmental and Geodetic Engineering,
The Ohio State University, 2070 Neil Ave., Columbus, OH 43210 USA
Dr. Jan Dirk Wegner
EcoVision Lab, IGP,
Prof. Cheng Wang
School of Information Science and Engineering, Xiamen University, Xiamen, Fujian 361005, China
Prof. Onisimo Mutanga
School of Agricultural, Earth & environmental Sciences, University of KwaZulu-Natal, P. Bag X01, Scottsville, 3209, Pietermaritzburg, South Africa. [email protected]