Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data: A Review and New Developments

Abstract

Despite the wide availability of remote sensing big data from numerous different Earth Observation (EO) instruments, the limitations in the spatial and temporal resolution of such EO sensors (as well as atmospheric opacity and other kinds of interferers) have led to many situations in which using only remote sensing data cannot fully meet the requirements of applications in which a (near) real-time response is needed. Examples of these applications include floods, earthquakes, and other kinds of natural disasters, such as typhoons. To address this issue, social media data have gradually been adopted to fill possible gaps in the analysis when remote sensing data are lacking or incomplete. In this case, the fusion of heterogeneous big data streams from multiple data sources introduces significant demands from a computational viewpoint. In order to meet these challenges,

Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review

“…Pansharpening (Ranchin et al, 2003) Introducing the methods belonging to ARSIS, along with giving a simple comparison (Vivone et al, 2014) Giving a thorough descripitions and assessments of the methods belonging to CS and MRA families (Meng et al, 2019) Introducing the methods belonging to CS, MAR, and VO from the idea of meta-analysis (Vivone et al, 2020) Giving a systematic introduction and evaluation of the methods in the category of CS, MAR, VO, and ML HS pansharpening (Loncan et al, 2015) Conducting a comprehensive analysis and evaluation in the methods from CS, MAR, hybrid, bayesian, and MF HS-MS fusion (Yokoya et al, 2017) Extensive experiments are presented to assess the methods from CS, MRA, unmixing, and bayesian (Dian et al, 2021b) Studying the performance of methods from CS, MAR, MF, TR, and DL Spatiotemporal (Chen et al, 2015) Discussing and evaluating four models from transformation/reconstruction/learning-based methods (Zhu et al, 2018) Reviewing the characteristics of five categories and their applications (Belgiu and Stein, 2019) Introducing the methods in three categories, as well as the challenges and opportunities (Li et al, 2020a) Analyzing the performance of representative methods with their provided benchmark dataset Heterogeneous fusion HS-LiDAR (Man et al, 2014) Summarizing the research on HS-LiDAR fusion for forest biomass estimation (Kuras et al, 2021) Giving an overview of HS-LiDAR fusion in the application of land cover classification SAR-optical (Kulkarni and Rege, 2020) Evaluating the performance of methods in CS and MRA in pixel-level RS-GBD (Li et al, 2021a) Providing a review on RS-social media fusion and their distributed strategies (Yin et al, 2021a) Reviewing the fusion of RS-GBD in the application of urban land use mapping from feature-level and decision-level perspectives Others (Wald, 1999) Setting up some definitions regrading data fusion (Gómez-Chova et al, 2015) Providing a review in seven data fusion applications for RS (Lahat et al, 2015) Summarizing the challenges in multimodal data fusion across various disciplines (Dalla Mura et al, 2015) Giving a comprehensive discussion on data fusion problems in RS by analyzing the Data Fusion Contests (Ghassemian, 2016) Introducing the RS fusion methods in pixel/feature/decision-level and different evaluation criteria (Schmitt and Zhu, 2016) Modeling the data fusion process, along with introducing some typical fusion scenarios in RS…”

Improving the Estimation of PM2.5 Concentration in the North China Area by Introducing an Attention Mechanism into Random Forest

“…Therefore, it is an important way to extract the spatio-temporal correlation via other different methods, as well as to construct a hybrid model for PM 2.5 estimation. In addition, in contrast to some other deep learning methods in the study of intelligent processing of remote sensing information [40,41], the full exploitation of multi-source data as well as spatio-temporal information plays a key role in the efficiency of the model [42,43].…”

Heterogeneous sensing for target tracking: architecture, techniques, applications and challenges

“…Heterogeneous sensing acquires all types of physical world data relating to the target and performs computational analysis to enable fast and accurate effective tracking of it [18]. Traditional single-target tracking methods are limited to batch node activation and periodic data collection via timers.…”