Building extraction, which is a term for automatically defining the footprint of a structure, is a common application that has benefited greatly from adopting LIDAR data as its primary data source. By using search routines in LP360 or Terrasolid, points that are on a building roof can be classified and then extracted to generate the footprint of the structure. Additional attributes such as the building height can be captured along with the footprint for later use in processing. This data can then be used for applications ranging from tax planning to building inventories.
Project management is not a single step in your workflow, but rather the tool to make sure all those different steps work together to create your desired products. Accurate project planning and optimizing your workflow have always been challenging on mapping projects. Not working toward continuous improvement in workflow best practices, improving efficiency and implementing reliable project tracking metrics can be costly. GeoCue helps you address these challenges by automatically implementing and monitoring your customized workflow while capturing real production metrics that help in both day-to-day production management and in planning future projects.
A successful project starts with a successful LIDAR data acquisition campaign. You either need to invest in best-in-class hardware and hire experienced, well-trained field crews or you need to hire a reputable service provider. Flight planning software along with preliminary processing of the raw field data is done in proprietary software tools provided by the hardware vendors.
Accuracy assessment is a critical – perhaps the most critical – step in your workflow. Your LIDAR data needs to meet the horizontal and vertical accuracy requirements specified for your deliverable products. Point spacing or point density is another key metric that must be verified. Generating 1’ contours from LIDAR data only accurate enough for 2’ contours or trying to generate a digital elevation model (DEM) from data that is too sparse to support the desired grid size will result in poor or rejected products. Accuracy assessment can be done in LP360 or TerraScan by comparing known check points to your LIDAR surfaces.
All LIDAR data needs some amount of geometric correction to position the point cloud accurately to a known spatial reference system. There are systematic and random errors in the sensor during data collection that must be corrected or minimized on every data set. Most of this correction is applied during the pre-processing of the point cloud using vendor software, however it is important to be able to correct any residual errors that remain (or to improve data sets from vendors who have not done rigorous geometric correction themselves). Geometric correction is one of the tasks in your workflow that you hope not to need to do very often, but when you do need to do it, you need to do it efficiently and accurately. Terrasolid’s TerraMatch is the key tool for calculating and applying geometric corrections to LIDAR data sets.
LIDAR data sets are noisy. They can include erroneous points in the air, below the ground or in other unusual or undesirable locations. To rigorously process a point cloud, some amount of initial data cleaning to remove noise and clean-up the points is required.This is typically done using automated noise filters to search for low points, high points, isolated points or other types of outliers.
The use of automatic classification routines is critical to an efficient workflow. LIDAR projects are often too large to be cost-effectively processed manually by hand. A range of robust algorithms have been developed over the years to automatically perform such tasks as ground classification, height segmentation, building identification, power line extraction and other similar feature identification tasks. Implementing these macros or point cloud tasks is the only way to cost-effectively process LIDAR data point clouds.
Automatic classification of the LIDAR point cloud is efficient, but almost never 100% complete or 100% accurate. Misclassifications and errors do occur and will need to be manually corrected. Some features do not lend themselves well to automatic identification and will have to be classified manually. Having a robust set of manual editing tools to help you review and re-classify points is important.
LIDAR data sets contain three main types of errors: misclassified points, mispositioned points and missing points. Even if you are receiving fully processed and reviewed point clouds, it is always important to do an independent QA/QC review of your data, especially if you have unique requirements or plan to use the data in a custom application. LP360 and TerraScan contain tools for reviewing, editing and correcting data in a QC workflow. Manual editing tools for immediate correction combined with tools for making edit calls –identifying areas that need fixing for others –are necessary for an effective QC environment.