Big Data and Smart City

Abstract:
Smart cities cannot be separated from spatiotemporal location big data. This paper summarizes the needs of spatiotemporal location big data for public epidemic events at home and abroad, investigates the specific services that current spatiotemporal location big data can provide in public epidemic events, and discusses the current stage of spatiotemporal location big data services that cannot meet the requirements of public epidemic events. This paper also focuses on how to improve the spatiotemporal location big data services in the next stage to better serve the public epidemic prevention and control, and gives advice on spatio-temporal location service construction at ordinary times and data sharing mechanism in "wartime". This paper aims to take scientific measures to improve the spatiotemporal big data services, strives to establish a technical support system for public epidemic prevention and control at ordinary times, and introduces a "wartime" spatiotemporal location big data sharing mechanism for epidemic prevention and control.
Abstract:
The rapid development of deep learning provides an important technical means for intelligent analysis of remote sensing big data. Firstly, this paper mainly introduces the deep learning modes in remote sensing data recognition and application, and proposes a deep reinforcement learning, multi-task learning and sub-pixel-pixel-super-pixel feature learning models for object features recognition from LiDAR point clouds, optical remote sensing images and hyperspectral images. The model parameters are basically obtained by learning, and thus the workload of the parameter adjustments is small. The spatial and contextual information, texture and spectral characteristics between ground objects are fully taken into account, so the presented models have good generalization abilities. Then, it describes the progress in terms of the joint deep learning and multi-source remote sensing data in accurate poverty alleviation assessment, wetland change and spatial analysis in Qinghai-Tibet Plateau in the past 20 years, and corn yield estimation. In order to better promote the transformation from remote sensing data to knowledge, it is necessary give full play to the advantages of deep learning in remote sensing big data processing, and develop new data processing algorithms and technologies.
Abstract:
Point-of-interest (POI) recommendation has emerged as a focal point in the research of location-based social network (LBSN) in recent years. It can help users find their favorite venue and bring considerable benefits to businesses. Nowadays, deep learning is gradually applied to the task of recommendation system because it can capture the nonlinear relationship between users and items more effectively. This paper thus focuses on recent research on POI recommendation combined with deep learning. Firstly, we introduce the difference between POI recommendation and other traditional recommendation tasks and illustrate various influencing factors that can improve the performance of the model. Then, the methods of applying deep learning to POI recommendation are divided into four categories, including POI embedding, deep collaborative filtering, feature extraction from side information, and sequence recommendation using recurrent neural network (RNN). We also investigate the development of user models performance and advantages combined with deep learning in these different aspects of applications. Finally, we summarize and look forward to the development of POI recommendation research combined with deep learning.
BeiDou Navigation Satellite System (BDS)
Big Data and Smart City
Review Paper
Cherish the Memory of Dr. NING Jinsheng
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