Special Issue "Instrumenting Smart City Applications with Big Sensing and Earth Observatory Data: Tools, Methods and Techniques"
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (30 April 2018)
With the remarkable advances in high-resolution Earth Observation (EO), we are witnessing an explosive growth in the volume, and also velocity, of Remote Sensing (RS) data. Generally, the volume of archived RS (Remote Sensing) is currently represented in the petabytes scale and this amount is growing everyday by terabytes. If predictions holds true, this could soon move from the petabyte to the exabyte scale given that the Square Kilometer Array (SKA) radio telescopes will transmit 400,000 petabytes (about 400 exabytes) per month, or a massive 155.7 terabytes per second. Furthermore, the European Space Agency (ESA) will launch several satellites in the next few years, which will collect data about the environment, such as air temperatures and soil conditions, and stream that data back, in real time, for analyses. In addition, instrumentation of sophisticated sensing devices (e.g., high resolution cameras, radar Altimeter, radiometers, photometers, etc.) in satellites has further led to the exponential increase in the velocity, variety, and volume of remotely sensed data. Therefore, RS data are referred to as the "Big Remote Sensing Data" or "Big Earth Observation Data''.
RS data play an important role in many application domains; in particular, the smart city domain, e.g., disaster monitoring, climate prediction, and remote surveillance. Effective integration of human, physical, and digital systems holds the promise of improving quality of life and making our cities smart and sustainable. For example, Open Geospatial Consortium (OGC) white paper provides the foundations for a spatial information framework that establishes the basics in order to integrate Geographic Information System (GIS) features, imagery, sensor observations, and social media. Remotely-sensed information, combined with location specific data collected locally or via connected Internet of Things (IoT) devices, presents tremendous opportunities for smart city applications. High-resolution RS data are used by insurance and financial companies to track consumer spending and assist with consumer claims. This, coupled with IoT data generated locally or via connected devices, can exponentially compound the ways to spatially process, analyze, and draw insights from data.
The I/O intensive "Big Remote Sensing Data", compounded by the velocity and variety of data from connected devices (e.g., Internet of things devices, such as smart watches, cars, etc.), pose several new technical challenges for traditional High Performance Computing (HPC) platforms, such as clusters and supercomputers, which have been widely used in the past for data processing, analysis, and knowledge discovery. At the outset, our current systems lack the capacity to store and manage this massive amount of RS data effectively. Cloud computing provides scientists with a revolutionary paradigm of utilizing elastic computing infrastructure and applications. By virtue of virtualization, computing resources and various algorithms could be accommodated and delivered as ubiquitous services on-demand according to the application requirements. Cloud paradigm has also been widely adopted in large-scale RS applications, such as the Matsu project for cloud-based flood assessment. It has also been used to analyze data captured via IoT devices in smart city applications using Big Data processing framework such as apache spark, Hadoop, etc. However, current datacenter clouds and big data processing frameworks are not optimized for deploying data-intensive RS applications due to lack of techniques that can support: (i) data and computation parallelism at finer granularity; (ii) efficient indexing for multi-dimensional RS data; (iii) holistic resource allocation that adapts to the uncertainties of cloud datacentre resources (failure, over-utilization, unavailability) and RS data flow (volume and velocity); (iv) RS data analytics across multiple datacenters, (v) fusion and integration of RS data with IoT data generated within smart city environments, and (vi) ) provide support for scalable and real-time processing of big RS and Internet scale IoT data. Moreover, current technique does not provide the means to fuse RS data with data generated locally or via connected IoT devices or social media. Optimizing RS data combined with data from IoT devices in smart cities will lead to development of future sustainable smart cities.
To address these issues in instrumenting smart city applications, this Special Issues solicits high quality articles in the following areas, but not limited to:
Scalable storage algorithms for highly distributed RS data
Programing abstraction for porting RS data analysis workflows to big data computing programming models (e.g., MapReduce, Stream Processing, NoSQL)
Smart city application specific ontology models for fusing RS data with other Internet of things data sources
Indexing techniques for petabyte efficient NoSQL query-based RS data processing
Quality of service optimized RS data analytic provisioning techniques exploiting cloud datacentre resources
Benchmarking kernels for optimizing RS data analytic tasks over cloud resources
Innovative smart city application use cases augmented by RS data and/or IoT data
Submitted articles must not have been previously published or currently submitted for journal publication elsewhere. Research articles, review articles, as well as technical notes (https://www.mdpi.com/journal/remotesensing/instructions), are invited. You can access them at the MDPI Remote Sensing Open Access Journal, https://www.mdpi.com/journal/remotesensing. Please submit your paper at: https://susy.mdpi.com/
Prof. Rajiv Ranjan
Dr. Prem Prakash Jayaraman
Prof. Dimitrios Georgeakopoulos
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.