Your Focused Solutions
Browse the recommended Neara modules to meet your needs
processing
Process and classify noisy LiDAR
AutoProcessing
Neara AutoProcessing ingests unclassified and noisy LiDAR and returns a fully classified and denoised dataset.
- Runs on the cloud which allows for multiple datasets to be automatically processed in parallel efficiently
- Classify data sets the size of Texas within days
- The ML model is trained on thousands of datasets and across a variety of features for rapid classification suitable for a variety of industries
- Auto classification improves over time with ML accuracy and speed
vectorization
Auto generate a 3D vector model
Vectorization
Neara automatically generates a 3D vector model from a classified point cloud dataset, and intelligently reclassifies LiDAR based on heuristics and relationship to the model.
- Cleanly extract pole and cable vectors, even where LiDAR coverage is poor
- Use the vectors to also perform automatic LiDAR classification QA, such as reclassifying all false positive conductor and pole points outside of the vectors
- Leverage intelligent and customizable algorithms to automate the QA/QC of LiDAR to solve for dataset-specific challenges
- Perform QA/QC automatically at-scale, including providing LiDAR coverage/quality metrics to inform future data capture requirements
insights
LiDAR point cloud viewer & spatial analytics at-scale
Insights
Conduct LiDAR spatial analytics at-scale to visualize and explore clearance to ground/conductors, vegetation encroachment, etc.
- Perform complex clearance analysis simply with robust report builders and cloud-enabled dataset-wide analytics
- Compare LiDAR changes to analyze changes over time and predict vegetation growth
- Automatically detect asset risks (pole-lean, cable-sag) and non-compliant building structures in right of ways
- Identify and correct missing assets such as found poles.
- Perform automated GIS asset correction using classified LiDAR and update back to source system
*Neara digital twin build not required for Spatial Modules.
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