Big Earth Data
Scope & Guideline
Fostering Interdisciplinary Collaboration for Global Environmental Solutions
Introduction
Aims and Scopes
- Earth Observation and Remote Sensing:
The journal publishes research that leverages satellite and aerial remote sensing technologies to monitor and analyze environmental phenomena, including land use change, climate impacts, and disaster management. - Data Science and Machine Learning:
A significant portion of the research involves the application of advanced data science techniques, including machine learning and deep learning, to process, analyze, and interpret large datasets from various Earth observation sources. - Sustainable Development Goals (SDGs):
The journal emphasizes research that aligns with the United Nations Sustainable Development Goals, promoting studies that utilize big Earth data to address issues such as climate change, resource management, and disaster risk reduction. - Interdisciplinary Approaches:
Research published in 'Big Earth Data' often integrates multiple disciplines, combining insights from geography, environmental science, computer science, and social sciences to provide comprehensive solutions to complex problems. - Big Data Management and Infrastructure:
The journal addresses challenges in managing and processing big Earth data, focusing on the development of frameworks, platforms, and tools that enhance data accessibility and usability for researchers and policymakers.
Trending and Emerging
- Artificial Intelligence and Machine Learning Applications:
There is a growing trend in utilizing AI and machine learning for processing and analyzing Earth observation data, enhancing the accuracy of predictions and insights drawn from large datasets. - Integration of Multi-Source Datasets:
Research increasingly focuses on combining datasets from various sources, including satellite imagery, ground-based observations, and social media data, to create comprehensive models of environmental change. - Climate Change Adaptation and Mitigation Strategies:
Emerging studies are concentrating on practical applications of big Earth data to develop strategies for climate change adaptation and mitigation, particularly in vulnerable regions and communities. - Dynamic Environmental Monitoring:
There is a rising interest in real-time and dynamic monitoring of environmental phenomena, enabled by advancements in data processing technologies and the increasing availability of real-time data streams. - Geospatial Data Visualization and Interaction:
Research is trending towards enhanced data visualization techniques, including interactive platforms and tools that facilitate user engagement and more effective communication of complex data insights.
Declining or Waning
- Traditional Remote Sensing Techniques:
Research focused solely on conventional remote sensing methods without integration of advanced data analytics or machine learning techniques is becoming less common, as the field shifts towards more innovative approaches. - Localized Case Studies:
There is a noticeable decrease in publications centered on localized case studies that do not employ big data methodologies or broader regional analyses, which are now overshadowed by studies with global or widespread applicability. - Static Data Reporting:
Papers that primarily report static datasets without dynamic analysis or predictive modeling are appearing less frequently, as the trend moves towards more interactive and predictive approaches to data. - Single-Domain Focus:
Research that restricts itself to a single domain, such as purely atmospheric studies or solely land use change, is diminishing as interdisciplinary studies that combine multiple domains gain traction. - Generalized Climate Models without Big Data Integration:
There is a decline in the publication of generalized climate models that do not leverage big data techniques, reflecting a preference for studies that utilize extensive datasets for more nuanced insights.
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