1. Spatial data infrastructures and geodatabases.

2. Managing big spatial data (e.g., cloud computing, distributed processing).

3. Real-time spatial data and streaming data integration.

4. Spatial data quality and uncertainty.

1. Spatial modelling and simulation (e.g., agent-based modelling, cellular automata).

2. Machine learning and artificial intelligence in spatial analysis.

3. Spatial optimization and decision-making.

4. Spatial data mining and knowledge discovery.

1. Advanced remote sensing data sources and characteristics.

2. Preprocessing and analysis of hyperspectral, LiDAR, and high-resolution imagery.

3. Remote sensing-based change detection and time series analysis.

4. Fusion of remote sensing and GIS data for improved modelling and decision-making.

1. Developing custom GIS applications using APIs and software development kits.

2. Geospatial web services and web-based GIS.

3. Automating GIS workflows and tasks using scripting and workflow engines.

4. Integrating GIS with other information systems (e.g., enterprise systems, IoT, BIM).

1. Advanced cartographic techniques and interactive map design.

2. Dashboards and decision support systems.

3. Spatial data storytelling and web-based GIS applications.

4. Spatial data science and communication of spatial insights.

1. Exploring emerging trends and technologies in GIS and remote sensing.

2. Case studies and best practices in applied GIS and remote sensing.

3. Independent research project on a selected topic.

4. Presentation and critical discussion of research findings.