Lesson 1DSM vs DTM production: methods to create bare-earth DTM in partially vegetated, built areas and river channel modelingDis section explain DSM an DTM concepts an workflows, focusing on bare-earth extraction in vegetated an urban areas. Learners wi apply filtering, editing, an river channel modeling to obtain hydrologically consistent terrain surfaces, mon.
DSM versus DTM definitions and usesGround point filtering in vegetationHandling buildings and urban artifactsManual terrain editing and breaklinesRiver channel and floodplain modelingLesson 2Orthomosaic generation: seamlines, radiometric blending, color balancing, and handling water/reflective surfacesDis section explain orthomosaic generation from DSM or DTM, including seamline design, radiometric balancing, an color correction. Learners wi handle water, shadows, an reflective surfaces to produce visually consistent, accurate mosaics, yuh see.
Orthorectification using DSM or DTMSeamline placement and editingRadiometric balancing and blendingColor correction and tone mappingHandling water, shadows, and glareLesson 3Bundle adjustment: theory overview, role in defining relative geometry, interpreting residuals and report metricsDis section introduce bundle adjustment theory an its role in defining di relative camera geometry. Learners wi understand normal equations, constraints, an how fi interpret residuals, covariance, an software reports to judge solution stability, irie.
Collinearity equations and observation modelWeighted least squares and constraintsRelative orientation and network geometryInterpreting residuals and outliersReading bundle adjustment quality reportsLesson 4Image alignment and keypoint matching: objectives, tie points, and strategies to improve tie qualityDis section detail image alignment an keypoint matching, including feature detection, tie point generation, an quality improvement strategies. Learners wi tune alignment settings an diagnose poor overlap, blur, or repetitive texture issues, seen.
Feature detection and descriptor choiceTie point matching and cleaningAlignment settings and accuracy levelsDealing with low overlap or motion blurManaging water, snow, and textureless areasLesson 5Image import, metadata validation, and pre-processing: EXIF checks, lens profile, radiometric correctionsDis section cover safe image import, metadata validation, an pre-processing. Learners wi verify EXIF integrity, lens profiles, an coordinate tags, then apply radiometric corrections an image culling to ensure robust downstream processing, yuh know.
Organizing image datasets and backupsEXIF and GNSS metadata validationLens profile selection and verificationRadiometric and color correctionsImage quality screening and cullingLesson 6Vector extraction and planimetric mapping: breakline creation, building footprint extraction, road centerlines, and hydrographyDis section cover vector extraction an planimetric mapping from orthomosaics an elevation data. Learners wi digitize breaklines, building footprints, roads, an hydrography, an apply topology rules an accuracy checks fi mapping products, mon.
Digitizing and snapping best practicesBreakline creation from terrain modelsBuilding footprint and roofline mappingRoad centerlines and pavement edgesHydrography and drainage network tracingLesson 7Camera calibration and interior orientation: self-calibration parameters, when to fix vs estimate focal length and distortionDis section cover camera calibration an interior orientation, including self-calibration parameters an when fi fix or estimate dem. Learners wi understand focal length, principal point, an distortion models an dem impact on accuracy, yuh hear.
Interior orientation and camera modelsRadial and tangential distortion termsSelf-calibration requirements and risksFixing versus estimating focal lengthUsing pre-calibrated metric camerasLesson 8Dense point cloud generation: filtering, noise removal, classifying points (ground, vegetation, buildings)Dis section focus on dense point cloud generation from aligned images. Learners wi configure depth map settings, manage noise, classify points into ground an objects, an prepare a clean point cloud fi surface modeling an editing, seen.
Depth map generation parametersPoint density versus processing timeNoise filtering and outlier removalGround and non-ground classificationExport formats and data managementLesson 9Quality checks at each stage: tie point residuals, GCP residuals, RMSE on check points, visual comparison, cross-sections and profile checksDis section explain how fi evaluate quality at each processing stage using quantitative metrics an visual checks. Learners wi interpret residuals, RMSE, an profiles to detect issues early an decide when reruns or edits required, irie.
Tie point residuals and reprojection errorGCP and check point residual analysisRMSE computation and interpretationVisual inspection of orthos and 3D viewCross-sections and elevation profile checksLesson 10Processing strategies for limited budgets: cloud vs local processing, open-source (e.g., OpenDroneMap) vs commercial software trade-offsDis section compare processing strategies under limited budgets, covering hardware choices, cloud versus local workflows, an open-source versus commercial tools. Learners wi balance cost, speed, scalability, an data security fi real projects, mon.
Estimating compute and storage needsCloud processing costs and constraintsLocal workstation and GPU considerationsOpen-source photogrammetry toolchainsCommercial software features and licensing
