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Remote Sensing for Geospatial Science

A section of Remote Sensing (ISSN 2072-4292).

Section Information

The integration of remote sensing technologies with geospatial science has opened new frontiers in understanding and managing natural resources, urban environments, and ecosystems. This section, "Remote Sensing for Geospatial Science", focuses on innovative research that applies or combines remote sensing with geospatial analytics, modeling, and advanced computational methods. Our goal is to publish original research that enhances knowledge and methodologies in capturing, analyzing, and interpreting dynamic geospatial phenomena across multiple scales.

We invite contributions that showcase novel technologies, new applications, methodological advancements, and integrative approaches in geospatial sensing and modeling. Submissions should prioritize clarity in data presentation, and we encourage high-quality visualizations, including maps, models, and graphical analyses.

This section addresses a broad range of topics in geospatial sensing and modeling, including technological innovations and applied research in remote sensing and geospatial science. Topics include, but are not limited to:

Technologies and Methods:

Mathematics and physics in remote sensing: Fundamental principles underlying remote sensing technologies and imaging systems Geospatial artificial intelligence: machine learning, deep learning, and AI applications in geospatial analytics
Geospatial data mining and analytics: extracting patterns and insights from large-scale geospatial datasets
Positioning, localization, and navigation: geospatial positioning, SLAM (Simultaneous Localization and Mapping), and tracking across diverse platforms (e.g., UAVs, satellites, IoT devices)
GNSS and advanced positioning technologies: integration of GPS, 5G, and high-precision positioning systems
Multi modal remote sensing data fusion: combining data from different sensors (e.g., optical, LiDAR, SAR) for enhanced analysis
Internet of Things (IoT) in geospatial contexts: sensor networks and IoT applications for continuous and real-time spatial data collection
Digital twins and simulation modeling: virtual representations and simulations for urban and environmental systems

Applications:

Urban and regional modeling: smart cities, smart regions, urban planning, and infrastructure management
Cultural heritage and archaeology: preservation, documentation, and analysis of historical sites through geo-spatial technologies
Natural resources and environmental management: applications in forestry, agriculture, water resources, and ecosystem monitoring
Environmental and geographic modeling: modeling natural processes and environmental changes across temporal and spatial scales
Disaster and risk Assessment: monitoring, prediction, and mitigation strategies for natural hazards and climate-related risks
Terrain modeling, analysis and visualization: digital terrain models (DTM), city models, and canopy models
Location-Based Services: navigation, mobile applications, SLAM, and other time-critical geospatial applications
Geosocial science: sensor network, citizen sensors, geospatial-economic and geospatial-demographic interactions