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Spatial Modules

Automate point cloud classification at scale and save time.

Neara’s Spatial Modules help utilities, geospatial and aerial inspection teams deliver value on their client LiDAR projects, shorten project delivery timeframes, and improve profit margins.

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A unique and competitive advantage.

Fragmented workflows often create inaccuracy and slowness in LiDAR classification, vectorization, and analysis, requiring intensive manual quality reviews that drive up operating costs and project delays.

Neara’s Spatial Modules* are purpose-built to simplify and streamline all stages of geospatial data processing. Underpinned by ML & AI, they comprise an integrated end-to-end solution that hosts, manages, processes, classifies, vectorizes, & analyzes LiDAR data on a massive scale.


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.



Hosting » Filtering » Classification » Vectorization » QA/QC » Analysis

  • View, edit and share large scale LiDAR data in Neara, and eliminate siloed systems
  • Using LIDAR, auto-generate digital twins to explore business uses such as scenario modeling, spatial analysis, terrain modeling, and more
  • Automatically process, classify and QA large-scale LiDAR projects in days vs weeks with Neara’s proprietary machine learning algorithms
  • Scales on the cloud, enabling clients the ability to process and deliver large-scale data projects rapidly and efficiently
  • Team members can collaborate remotely, geographically, cross-functionally, and with external service providers via this cloud-based solution
  • Industry agnostic, serving multiple industries and application use cases such as grid hardening, flood & storm analysis, and more

30x

Processing efficiencyAutomatically process, classify and QA captured LiDAR at 30x efficiency and up to 99% accuracy.

10x

Faster risk identificationTime-to-analysis savings made possible through a robust analytics platform using intelligent and predictive analytics.

24h

Time to productionLiDAR teams can review and productionalize datasets within 24 hours of data ingestion.

10x

Cost savingsUp to 10x cost savings through automation of LiDAR classification, processing and analysis using AI/ML.


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