1. Defining GIS and remote sensing.

2. History and development of GIS and remote sensing.

3. Components of a GIS.

4. Spatial data types and structures.

1. Data sources and formats (vector and raster).

2. GPS and field data collection.

3. Digitizing and scanning.

4. Remote sensing data acquisition (satellite, aerial, and UAV imagery).

1. Spatial data models (vector and raster).

2. Spatial data organization and storage.

3. Spatial data transformation and conversion.

4. Spatial data quality and metadata.

1. Spatial queries and selections.

2. Overlay analysis (union, intersection, difference).

3. Buffer analysis and proximity analysis.

4. Raster analysis (e.g., map algebra, surface analysis).

1. Aerial photography and satellite imagery interpretation.

2. Multispectral and hyperspectral remote sensing.

3. Image processing techniques (e.g., enhancement, classification).

4. Applications of remote sensing data in GIS.

1. Introduction to GIS software (e.g., ArcGIS, QGIS, Google Earth)

2. Spatial data visualization and cartography.

3. GIS applications in various domains (e.g., urban planning, environmental management, public health).

4. Hands-on exercises and case studies.