Troubleshooting LiDAR import issues
Table of Contents
Using Automated LiDAR Classification?
If you're using Neara's new Automated LiDAR Classification feature, it features a new Importer with more flexible options for importing LiDAR data
Processing failed
When using the Project menu > Import from LiDAR option to upload and import a LiDAR dataset from a .las, .laz, or .txt file, you may encounter this error in the Import Pointcloud Data panel:
Corrupted/invalid source file
A “Processing failed” error can occur if the file being imported is corrupted or doesn't contain valid LiDAR data.
Steps to try:
- If the file is a .txt file, check that it matches the format that Neara expects for a successful import
- If the file is a .las or .laz file, try opening it up in another app that can import those files
- If the file is a .las file, try validating if the data inside it is in the correct format using a third-party tool such as LAStools
If the above doesn't resolve the error, submit a new Customer Support Request using the Intercom messenger available on every screen, including:
- The link to your Design
- Attach the .las/.laz/.txt file
Our team will diagnose the issue and contact you to discuss possible solutions.
File too large
A “Processing failed” error may also occur if the source file size is very large.
Steps to try:
- Strip the file's contents to include only necessary components
- Submit a new Customer Support Request using the Intercom messenger available on every screen, to import the file directly from cloud storage (e.g. AWS, Azure, GCP)
Some points have been hidden
If you are able to upload a LiDAR dataset but the PointCloud preview does not display all expected points, it may prevent you from selecting mappings before proceeding to the next step.
This may occur if the file contains many millions of points.
Steps to try:
- Strip the file's contents to include only necessary components
- Submit a new Customer Support Request using the Intercom messenger available on every screen, to import the file directly from cloud storage (e.g. AWS, Azure, GCP)