Deep Learning Approaches for Urban Sensing Data Analytics

Dear Colleagues,

Deep Learning (DL) has attracted burgeoning research interest in the past few years, due to its strength in automatic learning of hierarchical features from big data. At the same time, different types of remote sensing, such as satellite and airborne imagery and video systems, as well as ground-level mobile mapping systems (e.g., mobile laser scanning systems) have been widely used in urban environment monitoring and analytics at various scales. In addition, existing sensing infrastructures (e.g., CCTV) can be harnessed to extract new information (e.g., pedestrian/vehicle moving patterns) with the help of DL. Although DL is rapidly gaining popularity in remote sensing (Zhang et al., 2016), we are facing numerous challenges in applying it to urban sensing data, such as noisy training datasets, incompatible spatial scales, dense mixture of image objects, short update intervals, onerous hyper parameter tuning, and limited prior knowledge. All these challenges are requiring us to develop special DL approaches for urban sensing data analytics.

This Special Issue aims to provide new DL methods that could transform big urban sensing data into knowledge with limited intervention. Due to the high variety of urban sensing systems, how to develop common architectures of deep neural networks will become the major concern of this Special Issue. Topics of interest mainly include but are not limited to:

• New deep neural network models for urban scene classification;
• 3D deep learning for urban scene understanding;
• New recurrent neural network algorithms for urban change detection;
• Advanced training and testing of deep learning methods;
• Real-time urban sensing data analytics using deep learning algorithms;
• Generative adversarial network for remote sensing data fusion;
• Innovative reinforcement learning algorithms for transportation management.

Dr. Jin Xing
Dr. Wen Xiao
Prof. Gui-Song Xia
Prof.  Liangpei Zhang
Guest Editors

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