LEARNING GOALS and OBJECTIVES Goal 1 Students will explain the foundations of aerial image analysis and interpret imagery as a source of geospatial information. Objectives • Differentiate between observing imagery and analyzing imagery. • Compare vertical and oblique imagery and their applications. • Explain how scale, resolution, and Ground Sample Distance (GSD) affect image interpretation. • Describe how aerial imagery is used to support identification, measurement, and change detection activities. • Explain the relationship between imagery, information, and decision-making. Goal 2 Students will manage, process, and enhance aerial imagery for geospatial analysis. Objectives • Describe the workflow for transferring imagery from drone systems to analysis platforms. • Interpret metadata and EXIF information associated with aerial imagery. • Explain image compression, storage formats, and digital image organization. • Apply analytical image enhancement techniques while preserving data integrity. • Use image processing methods to improve image usability for analysis. Goal 3 Students will create and evaluate geospatial products derived from aerial imagery. Objectives • Explain the purpose and creation of orthomosaics and GeoTIFF products. • Interpret raster and vector geospatial data. • Describe GIS and remote sensing concepts used in aerial image analysis. • Generate and interpret orthomosaics, digital elevation products, and other mapping products. • Apply geospatial workflows to transform imagery into useful information. Goal 4 Students will apply photogrammetry and three-dimensional reconstruction principles to aerial datasets. Objectives • Explain photogrammetric concepts including overlap, keypoints, tie points, and triangulation. • Describe the relationship between imagery and point-cloud generation. • Differentiate between Digital Surface Models (DSM) and Digital Terrain Models (DTM). • Interpret LiDAR and photogrammetric point-cloud datasets. • Evaluate the role of feature detection in three-dimensional reconstruction. Goal 5 Students will extract, classify, and analyze information from aerial imagery. Objectives • Apply feature detection techniques to aerial imagery. • Extract features using manual and automated workflows. • Classify land cover and other image features. • Evaluate machine vision approaches for aerial image analysis. • Interpret machine-generated analytical results critically and responsibly. Goal 6 Students will plan aerial data acquisition missions and evaluate data quality. Objectives • Explain the relationship between flight planning and data quality. • Evaluate overlap, altitude, GSD, and mission geometry requirements. • Assess image acquisition parameters that influence reconstruction quality. • Interpret processing reports and quality metrics including coverage, RMSE, and accuracy indicators. • Differentiate between relative and absolute accuracy. Goal 7 Students will communicate aerial image analysis findings through professional geospatial products and reports. Objectives • Distinguish observations from interpretations and conclusions. • Annotate aerial imagery effectively. • Create professional maps and geospatial deliverables. • Write analytical findings and executive summaries. • Communicate uncertainty, limitations, and recommendations appropriately to technical and non-technical audiences. Goal 8 Students will evaluate ethical, legal, privacy, and professional considerations associated with aerial image analysis. Objectives • Discuss privacy concerns associated with aerial data collection. • Explain ethical responsibilities of image analysts and drone operators. • Differentiate between legal compliance and ethical decision-making. • Evaluate responsible data collection, storage, and reporting practices. • Analyze the societal impacts of aerial imaging, machine vision, and AI-assisted analysis. Goal 9 Students will develop a professional portfolio demonstrating competency in aerial image analysis. Objectives • Select representative project artifacts demonstrating technical competency. • Organize analytical products into a professional portfolio format. • Document project methodologies, results, and lessons learned. • Present geospatial analysis products to professional audiences. • Demonstrate readiness for employment in image analysis, GIS, remote sensing, and UAS-related industries.