Environmental sustainability refers to the conservation and management of natural resources to match the needs of present without negotiating the ability of coming generations to meet theirs. It aims to balance ecological, social, and economic goals with assurance of equitable access to the resources. Over the last few decades, sustainability is concerned with the man environmental interface, the complex boundary where bio-physical and socio-cultural systems interact. It accumulated tremendous knowledge about the surrounding earth surface features and natural phenomena. It is crucial to pursuit the advancements and innovations in the dynamic field of earth and environmental monitoring. It is therefore important to analyze and harness the advanced technologies in achieving environmental sustainability at different observational scales. Geospatial big data and artificial intelligence (AI) provide new and great opportunities to solve the problems associated with environmental sustainability with advanced analytics. It can support real-time spatial analysis and visualization of data in the form of maps.
Recent progresses in remote sensing technologies have led us to the time of spatially explicit big Earth data and Artificial Intelligence. The launch of various satellites on board sensors opened a new era in earth observation techniques for environmental studies. Various spaceborne and airborne multispectral sensors, UAV based sensors, and even IoT and social media data also contribute considerably to geospatial big data collection. The last few decades have witnessed an explosion in the amount, collection, and complexity of spatial environmental data. Complex data from multiple sources can be integrated to provide a more comprehensive scenario and to address real-world environmental and sustainability problems. Moreover, the cloud-based computing platforms equipped with AI are now available for solving any big and complex computational problems. Apart from this widespread employment of machines and deep are gaining a lot of interest to handle massive availability of geospatial big data. Advanced modelling tools coupled with Geospatial information meet the requirement for accurate and timely study in environmental monitoring, risk assessment, and planned decision formulation for sustainable development. It supports research across different fields, providing new views on the interconnection of air, water, soil, food, and energy for a resilient society and a sustainable future.
Big Earth data coupled with remote sensing and geospatial technologies have become increasingly valuable for environmental studies. State-of-the-art Geospatial data analytics have the potential to develop and use analytical and rigorous computer-based data-science methods to evaluate and manage the Earth’s natural resources to accomplish a sustainable society. In this topical collection, we welcome contributions focusing on geospatial data analytics in Earth observation, environmental monitoring, and management. It aims to foster the comprehensive understanding of timely environmental phenomena and guide us toward sustainable development practices. The submission covers, but is not limited to, the themes given below:
• Artificial Intelligence and machine learning;
• Big Geospatial data applications;
• Cloud computing platforms for Geoenvironmental visualization;
• Earth observation and environmental monitoring;
• Environmental sustainability;
• Geospatial data modelling and fusion;
• Multiscale data integration;
• Natural hazards and disasters;
• Remote sensing and GIS for natural resources.
Environmental sustainability refers to the conservation and management of natural resources to match the needs of present without negotiating the ability of coming generations to meet theirs. It aims to balance ecological, social, and economic goals with assurance of equitable access to the resources. Over the last few decades, sustainability is concerned with the man environmental interface, the complex boundary where bio-physical and socio-cultural systems interact. It accumulated tremendous knowledge about the surrounding earth surface features and natural phenomena. It is crucial to pursuit the advancements and innovations in the dynamic field of earth and environmental monitoring. It is therefore important to analyze and harness the advanced technologies in achieving environmental sustainability at different observational scales. Geospatial big data and artificial intelligence (AI) provide new and great opportunities to solve the problems associated with environmental sustainability with advanced analytics. It can support real-time spatial analysis and visualization of data in the form of maps.
Recent progresses in remote sensing technologies have led us to the time of spatially explicit big Earth data and Artificial Intelligence. The launch of various satellites on board sensors opened a new era in earth observation techniques for environmental studies. Various spaceborne and airborne multispectral sensors, UAV based sensors, and even IoT and social media data also contribute considerably to geospatial big data collection. The last few decades have witnessed an explosion in the amount, collection, and complexity of spatial environmental data. Complex data from multiple sources can be integrated to provide a more comprehensive scenario and to address real-world environmental and sustainability problems. Moreover, the cloud-based computing platforms equipped with AI are now available for solving any big and complex computational problems. Apart from this widespread employment of machines and deep are gaining a lot of interest to handle massive availability of geospatial big data. Advanced modelling tools coupled with Geospatial information meet the requirement for accurate and timely study in environmental monitoring, risk assessment, and planned decision formulation for sustainable development. It supports research across different fields, providing new views on the interconnection of air, water, soil, food, and energy for a resilient society and a sustainable future.
Big Earth data coupled with remote sensing and geospatial technologies have become increasingly valuable for environmental studies. State-of-the-art Geospatial data analytics have the potential to develop and use analytical and rigorous computer-based data-science methods to evaluate and manage the Earth’s natural resources to accomplish a sustainable society. In this topical collection, we welcome contributions focusing on geospatial data analytics in Earth observation, environmental monitoring, and management. It aims to foster the comprehensive understanding of timely environmental phenomena and guide us toward sustainable development practices. The submission covers, but is not limited to, the themes given below:
• Artificial Intelligence and machine learning;
• Big Geospatial data applications;
• Cloud computing platforms for Geoenvironmental visualization;
• Earth observation and environmental monitoring;
• Environmental sustainability;
• Geospatial data modelling and fusion;
• Multiscale data integration;
• Natural hazards and disasters;
• Remote sensing and GIS for natural resources.