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- Common challenges and impact on costs
- Cost variations as projects increase in size
- Workflows in Correlator3D to minimize man-hours
- Accelerating timelines with distributed processing
- Capturing thousands of images
- Validating and preparing raw imagery
- Processing large projects with Correlator3D
- Preparing results for delivery
- Selecting platforms and sensors to best match projects
- Mission planning for efficient data collection
- Setting up projects in Correlator3D and validating input data
- Doubling DSM and point cloud processing speeds with new version 10.1
- Analyzing the different camera types and sizes available
- Understanding the factors affecting camera quality
- Selecting a camera for photogrammetry
- Processing data from any camera with Correlator3D
- Flying a drone over a mining site
- Vetting the resulting imagery and accuracy
- Producing base map products with Correlator3D
- Analyzing geospatial data to monitor the site
- What is the difference between RTK and PPK
- How RTK/PPKs affect aerial triangulation
- When are GCPs needed
- How accuracy is validated
- What is an accuracy standard
- How is a well-accepted standard such as ASPRS defined
- How does new technology affect accuracy standards
- How are mapping deliverables validated against a standard
- Using the redesigned UI including new light/dark modes
- Increasing the densities of point clouds
- Adding oblique imagery to improve results
- Colorizing photogrammetric point clouds automatically
- Filtering out points with lower accuracies
- Challenges in managing large corridor mapping projects
- Running Correlator3D in the cloud
- Advantages of distributed processing
- Time savings with scripting
- Typical drone mapping project costs
- Cost variations as projects become larger
- Workflows in Correlator3D to minimize man-hours
- Using multiple PCs to accelerate processing
- Value of integrated lidar and imaging sensors
- Optimizing workflows with dual systems
- Using Correlator3D to confirm data accuracy
- Creating high-quality point clouds and mosaics
- Best practices for project setup
- Generating high quality DSM/DTMs, point clouds,
and orthomosaics
- Solutions to optimize processes
- Highlight capabilities of C3D Version 9 and a sneak peek at Version 10
- Acquiring imagery where terrain is a factor
- Acquiring imagery where weather is a factor
- Flight planning
- Processing workflows
- Choosing the right sensor
- Collecting optimal data for calibration
- Performing sensor calibration
- Monitoring sensor performance over time
- Types of cameras used in aerial mapping
- Single sensor vs multi-sensors
- Additional components (e.g. IMUs, stabilized mounts)
- Factors affecting accuracy
- Processing workflows
- Differences between RTK & PPK
- Factors impacting accuracy
- Processing options in Correlator3D
- When to use RTK/PPK
- Troubleshooting results
- Choosing the right Phase One imaging sensor
- Understanding the Intelligent Image Quality
(IIQ) format
- Determining when to use IIQ format
- Setting up IIQ projects in Correlator3D
- Differences between cameras and positioning systems
- Coverage area per mission
- Costs associated with acquisition
- Differences in processing workflows
- Planning corridor mapping projects
- Importing and merging different sensor data
- Batch processing via scripting
- Editing output data
- Managing multiple LAS files
- Setting up email notifications to monitor processing
- Processing imagery from the new Red Edge P sensor
- Importing data from the Pléiades Neo satellite
- Creating better surfaces with DEM editing tools and processes
- Developing workflows for infrastructures such as buildings, bridges and elevated roadways
- Inspecting surfaces through visualization
- Using DEM-based options for ortho and mosaic creation
- Setting up a photogrammetric project
- Importing a lidar DEM and intensity image
- Using lidar as supplemental control
- Colorizing point clouds
- Designing simple scripts in the GUI
- Executing scripts with the GUI vs command-line
- Batch processing via scripts
- Editing output data
- Saving scripts for reuse
- Mission planning
- Data acquisition and post-processing
- Generating DSMs, DTMs, and orthomosaics
- Assessing final project accuracy
- Configuring a suitable network
- Discover available PCs for processing
- Dispatch processing on specific PCs
- Monitor processing on the different PCs in real-time
- Dynamically adjust computer power during processing
- Importing LiDAR point clouds
- Registering LiDAR data and images automatically
- Orthorectifying on LiDAR DEMs
- Colorizing LiDAR point clouds
- Understanding input requirements for accurate measurements
- Using the proper calculation methodology
- Scaling and automating over multiples stockpiles
- Assessing accuracies of volume measurements
- Large-scale projects
- Continuous stream of projects
- Multi-mission projects
- Single projects with discontinuous areas
- Collecting optimal imagery
- Assessing quality of results
- Performing aerial triangulation
- Generating accurate DSMs and DTMs
- Creating seamless orthomosaics
- Working in different real-world scenarios
- Mission planning
- Data acquisition and post-processing
- Assessing final project accuracy
- Generating DSM, DTM and orthomosaic
- Mission planning
- Data acquisition and post-processing
- Performing aerial triangulation with PPK
- Generating DSM, DTM and orthomosaic
- Controlling quality of input imagery
- Generating dense DSMs, DTMs and 3D models
- Producing accurate orthomosaics
- Sharing and visualizing resulte on the web
- Collecting optimal drone imagery
- Performing aerial triangulation
- Generating DSMs, DTMs and orthomosaics
- Optimizing processing workflows
- Assessing final project accuracy
- Importing LiDAR point clouds
- Registering LiDAR data and images automatically
- Orthorectifying on LiDAR DEMs
- Colorizing LiDAR point clouds
- Review of Correlator3D processing options
- Processing multispectral data
- Handling single image satellite projects
- Improving mosaic seamlines with DSM
View Recording
- Choosing between DTM-based vs true orthos
- Generating mosaic from individual orthos
- Assessing mosaic accuracy
- Editing mosaic seamlines
- Creating NDVI maps
- Collecting optimal drone imagery
- Performing quality control on raw data
- Cleaning-up input datasets
- Optimizing processing workflows
- Large-scale projects
- Continuous stream of projects
- Multi-mission projects
- Single projects with discontinuous areas
- Using calibrated reflectance panels and sun sensors
- Registering multispectral bands
- Producing multiband orthomosaics
- Creating vegetation index maps
- Using calibrated reflectance panels and sun sensors
- Registering multispectral bands
- Producing multiband orthomosaics
- Creating vegetation index maps
- Generating maps from drone imagery
- Exporting results to the cloud
- Creating shareable web links
- Visualizing geospatial data online
- Preparing imput data and pairing images
- Refining RPCs through aerotriangulation
- Generating high-resolution DSMs and DTMs
- Creating seamless orthomosaics
- Pansharpening of multispectral images
- Collecting optimal imagery
- Assessing quality of results
- Performing aerial triangulation
- Generating accurate DSMs, DTMs
- Creating seamless orthomosaics
- Performing aerial triangulation
- Producing mosaic of orthophotos
- Assessing final project accuracy
- Generating DSM, DTM and orthomosaic
- Understanding requirements for precise measurements
- Using a proper reference elevation
- Automating volumetric calculation
- Assessing accuracies of volume measurements
- Understanding input data requirements
- Selecting bundle adjustment parameters
- Calibrating sensors
- Understanding AT results and reports
- Performing direct georeferencing