Human-induced environmental issues (e.g., air, water, and soil pollution) have greatly pressured the Earth's sustainable development. Although scientists and environment management agencies have made great efforts to provide regular environment reports to the public, timely or progressive pollution alerts, and health advice remain unsolved. The main obstacle is the lack of fine-grained observations of environmental pollutants and synchronized personal health data. Recent advances in sensor resolution, data acquisition time and the availability of big data make it possible to overcome the challenges of sparse and granular data.
Remote sensing allows for the measurement, integration, and presentation of multi-scale spatio-temporal information. It has played a key role in sustainable development. It covers a variety of sub-topics in land resource surveying, environment change monitoring, water quality assessment, land resource surveying, environment change monitoring, water quality assessment, and near real-time disaster prevention and mitigation. Remote sensing data has proven powerful for monitoring environmental changes but only considers environmental aspects. Social sensing big data could be incredibly helpful to fill the gap between environmental assessment and its social implications.
To advance the understanding of socio-environmental impact at multiple scales and inform policy, scientists and researchers recommend integrating broadly available social media data and environmental remote sensing data to investigate the social and environmental impacts. The convergence of remote sensing and social sensing big data will play an increasing role in exploring sustainable development. The convergence involves many computational and geo-spatial modeling techniques, such as artificial intelligence, deep learning, big-data mining, geo-simulation, spatial analysis, and newly developed statistical models.
This Research Topic seeks sustainable solutions to build new environment monitoring systems and approaches through remote sensing and social sensing big-data convergence. We invite contributions to share their remote sensing applications in environmental monitoring from the perspective of big data convergence. Any study that explores how remote sensing can be used in a cross-cutting, interdisciplinary manner to support decision-making aimed at addressing environmental challenges is encouraged. The topics can be but are not limited to:
• Environmental assessment based on remote sensing and social sensing big data
• Development of satellite and social media derived indices for water or soil diagnosis and their socio-environmental assessment
• Evaluation of environment quality by integrating remote sensing and social sensing big data
• Data fusion based solutions to study sustainable environment
• Deep learning algorithms to retrieve pollution information from satellite and UAV observations
Human-induced environmental issues (e.g., air, water, and soil pollution) have greatly pressured the Earth's sustainable development. Although scientists and environment management agencies have made great efforts to provide regular environment reports to the public, timely or progressive pollution alerts, and health advice remain unsolved. The main obstacle is the lack of fine-grained observations of environmental pollutants and synchronized personal health data. Recent advances in sensor resolution, data acquisition time and the availability of big data make it possible to overcome the challenges of sparse and granular data.
Remote sensing allows for the measurement, integration, and presentation of multi-scale spatio-temporal information. It has played a key role in sustainable development. It covers a variety of sub-topics in land resource surveying, environment change monitoring, water quality assessment, land resource surveying, environment change monitoring, water quality assessment, and near real-time disaster prevention and mitigation. Remote sensing data has proven powerful for monitoring environmental changes but only considers environmental aspects. Social sensing big data could be incredibly helpful to fill the gap between environmental assessment and its social implications.
To advance the understanding of socio-environmental impact at multiple scales and inform policy, scientists and researchers recommend integrating broadly available social media data and environmental remote sensing data to investigate the social and environmental impacts. The convergence of remote sensing and social sensing big data will play an increasing role in exploring sustainable development. The convergence involves many computational and geo-spatial modeling techniques, such as artificial intelligence, deep learning, big-data mining, geo-simulation, spatial analysis, and newly developed statistical models.
This Research Topic seeks sustainable solutions to build new environment monitoring systems and approaches through remote sensing and social sensing big-data convergence. We invite contributions to share their remote sensing applications in environmental monitoring from the perspective of big data convergence. Any study that explores how remote sensing can be used in a cross-cutting, interdisciplinary manner to support decision-making aimed at addressing environmental challenges is encouraged. The topics can be but are not limited to:
• Environmental assessment based on remote sensing and social sensing big data
• Development of satellite and social media derived indices for water or soil diagnosis and their socio-environmental assessment
• Evaluation of environment quality by integrating remote sensing and social sensing big data
• Data fusion based solutions to study sustainable environment
• Deep learning algorithms to retrieve pollution information from satellite and UAV observations
