About the Journal

1. Aims Journal of Geoscience and Spatial Observation (GSO) is a peer-reviewed, open-access journal dedicated to advancing the understanding of Earth systems through the lens of spatial technology. The journal provides a premier platform for researchers, engineers, and practitioners to publish original research that bridges the gap between fundamental geosciences and the innovative utilization of spatial data. GSO aims to foster interdisciplinary dialogue, promoting research that applies advanced measurement, processing, and visualization techniques to solve critical environmental, structural, and societal challenges.

2. Scope The journal welcomes high-quality manuscripts that cover, but are not limited to, the following core areas:

I. Applied Geosciences & Earth Observation

  • Environmental Monitoring: Assessing air quality, atmospheric dynamics, and pollution impacts over time.

  • Disaster Management: Post-disaster damage assessment, humanitarian mapping, and hazard mitigation strategies.

  • Geomorphology & Land Cover: Long-term land-use/land-cover (LULC) changes, urban heat islands, and natural resource management.

  • Climate & Weather Systems: Integration of global earth observation data with localized weather station networks and climate modeling.

II. Spatial Data Measurement & Acquisition

  • Remote Sensing: Multi-spectral, hyper-spectral, and SAR (Synthetic Aperture Radar) satellite observations (e.g., Landsat, Sentinel, MODIS).

  • UAV & Drone Technologies: High-resolution photogrammetry, drone-based LiDAR, and autonomous spatial data collection.

  • Geodesy & Surveying: Advanced positioning systems, geodetic networks, and multi-epoch spatial data acquisition.

  • Infrastructure Monitoring: Applications of spatial technology for structural health monitoring of critical infrastructure (e.g., bridges, dams, transportation networks).

III. Processing, Handling, & Analytics

  • Cloud-Based Geocomputation: Utilization of platforms like Google Earth Engine (GEE) for planetary-scale data analysis.

  • Algorithm Development: Machine learning, deep learning, and artificial intelligence applied to geospatial datasets.

  • Big Data Spatial Analytics: Handling, filtering, and extracting actionable metrics from massive, multi-temporal spatial datasets and LiDAR point clouds.

IV. Spatial Visualization & Communication

  • Geographic Information Systems (GIS): Advanced spatial modeling and spatial decision support systems (SDSS).

  • Dynamic Mapping: 3D/4D modeling, digital twins, and interactive geospatial web applications.

  • Visual Data Storytelling: Innovative methods for communicating complex environmental and structural data to policymakers and the public.

3. Types of Articles Accepted

  • Original Research Articles: Comprehensive studies detailing novel methodologies or significant findings.

  • Review Papers: Critical evaluations of the current state of specific technologies or applications within the scope.

  • Technical Notes / Short Communications: Brief reports on new tools, datasets, processing scripts, or early-stage field assessments.

4. Out of Scope To maintain the journal's focus, GSO will generally not consider manuscripts that deal with pure, theoretical geoscience or social sciences without a clear, substantial spatial observation or geographic information systems (GIS) component. Similarly, purely algorithmic computer science papers must demonstrate a direct, validated application to Earth observation data to be considered.