A. Understanding raster data formats and properties
B. Importing and managing raster datasets in ArcGIS Pro
C. Raster visualization techniques: symbology and display options
D. Raster data exploration: querying and examining pixel values
E. Raster data preprocessing: resampling, reprojecting, and mosaicking
F. Raster data extraction: subset and clip operations
G. Raster algebra: performing mathematical operations on raster datasets
H. Terrain analysis with rasters: creating elevation models and slope maps
I. Raster interpolation methods: creating continuous surfaces from point data
J. Raster data integration with vector data in spatial analysis

A. Understanding the concept of raster functions and their applications
B. Exploring the available raster functions in ArcGIS Pro
C. Applying basic arithmetic and mathematical operations to rasters
D. Performing neighborhood operations using raster functions
E. Applying filters and smoothing techniques to rasters
F. Enhancing and adjusting raster display properties using functions
G. Creating raster masks and clipping using functions
H. Raster overlay and combining multiple rasters with functions

A. Data preparation for change detection analysis
B. Importing and managing raster datasets for change detection
C. Exploring different change detection algorithms and techniques
D. Utilizing raster functions for change detection analysis
E. Image differencing: subtracting raster datasets to identify change
F. Thresholding techniques for change detection
G. Change vector analysis: quantifying magnitude and direction of change
H. Object-based change detection: segmenting and classifying change areas
I. Change detection using vegetation indices and remote sensing data
J. Multitemporal change detection: analyzing changes over multiple time periods
K. Accuracy assessment and validation of change detection results
L. Visualizing and interpreting change detection outputs 
M. Change detection for environmental monitoring and land cover mapping
N. Change detection in urban areas and infrastructure monitoring

A. Understanding remote sensing and image classification concepts
B. Data preparation for image classification
C. Preprocessing techniques for image classification (e.g., radiometric calibration, atmospheric correction)
D. Supervised classification: Training sample collection and selection
E. Creating training samples and classifying pixels using the Maximum Likelihood Classifier
F. Object-based image classification: Segmenting and classifying image objects
G. Unsupervised classification: Clustering pixels based on spectral similarity
H. Accuracy assessment of classified images
I. Change detection using image classification techniques
J. Classification of multi-temporal satellite imagery
K. Classification of aerial imagery and drone data
L. Classification of land cover and land use classes
M. Classification of land cover and land use classes

A. Exam and Assessment

A. Certificate