CALL FOR PAPERS

ACM Transactions on Spatial Algorithms and Systems

Special issue on Deep Learning for Spatial Algorithms and Systems


Special Issue Guest Editors:
Moustafa Youssef: Alexandria University, Egypt
John Krumm: Microsoft Research, USA
Muhammad Aamir Cheema: Monash University, Australia


Aim and Scope

The availability of both large-scale datasets as well as the advances in graphical processing units (GPUs) has paved the way to the recent breakthroughs in the deep learning field. This in turn has led to unprecedented accuracy in various applications of machine learning such as image recognition, natural language processing, and machine translation, among others. Spatio-temporal data sets are naturally large due to the wide extent in both space and time. Hence, approaches based on deep learning are well suited to systems designed to process spatio-temporal data.

This special issue on Deep Learning for Spatial Algorithms and Systems will be published in ACM Transactions on Spatial Algorithms and Systems (TSAS). The guest editors target covering various deep-learning algorithms and systems applied to spatial data processing.

Topics of interest include (but are not limited to) applications of deep learning to:

Big Spatial Data
Location Privacy, Data Sharing and Security
Mobile Systems and Vehicular Ad Hoc Networks
Spatio-Temporal Data Analysis
Spatial Data Mining and Knowledge Discovery
Spatial Data Quality and Uncertainty
Spatio-Temporal Sensor Networks
Spatio-Temporal Stream Processing
Spatio-Textual Searching
Location-Based Services
Location Tracking Algorithms
Traffic Telematics
Urban and Environmental Planning
Crowdsourcing Spatial Data
Geographic Information Retrieval
Connected Cars, Intelligent Transportation Systems, Smart Spaces
Mobile Data Analytics
Behavioral/Activity Sensing and Analytics
Location-Based Social Networks
Location and Trajectory Analytics
Innovative Applications Driven by Spatial Data


The journal welcomes original articles on any of the above topics or closely related disciplines in the context of deep learning for spatial algorithms and systems. TSAS will encourage original submissions that have not been published or submitted in any form elsewhere, and submissions which may significantly contribute to opening up new and potentially important areas of research and development. TSAS will publish outstanding papers that are "major value-added extensions" of papers previously published in conferences. These extensions should contribute at least 30% new original work. In this case, authors will need to identify in a separate document the list of extensions over their previously published paper. For more information, please visit https://tsas.acm.org/authors.cfm.

Important Dates

May 1, 2019: Deadline for submissions of full-length papers
Aug 1, 2019: Notification of initial reviews
Sep 1, 2019: Deadline for revisions
Dec 1, 2019: Notification of final reviews
Jan 1, 2020: Submission of final camera-ready manuscripts
Mar 1, 2020: Expected publication


For further information, please contact the guest editors at deep-learning-editors@acm.org.