Post-storm GIS Correction


Post-storm GIS Correction

Accelerate and enhance corrective maintenance programs through the automation of asset data capture.


Manual inspections drive both cost and effort within utilities – especially after extreme weather events that may have potentially damaged assets.

The increasing availability of data sources, such as LiDAR, presents a significant opportunity to streamline operations and reduce costs.


Following a storm event, most utilities would utilize a manual site or pole inspection to collect positional data for updating their GIS.

In some instances, utilities may deploy LiDAR scanners and attempt to correct the old data manually with the resulting scan.


Neara’s Spatial tools provide an end-to-end solution for point-cloud data, all in one intelligent platform. Our software is purpose-built for utilities to simplify and streamline all stages of geospatial data processing.

The Neara Platform automatically generates an engineering-grade, physics-enabled digital twin from point cloud, LiDAR, GiS, imagery and asset information. Neara can identify damaged assets such as broken conductors or overloaded poles, verify construction, and provide accurate inventory (including cm precision locations).

The same model can then be used to unlock several additional use cases, such as:

  • Advanced simulation of clearances under different conditions and configurations to ensure compliance
  • Asset failure risk prediction to determine likelihood of structural or foundation failure
  • Predictive risk calculations by integrating with other systems
    (GIS, AMS, OMS etc.) and data sources (e.g. real time bushfire or storm maps)
  • Improved productivity and collaboration by sharing a single cloud-based digital model across the asset inspections, asset replacement, engineering design, vegetation management and risk teams.

With this 3D model, utilities are able to analyze various scenarios on the grid, assess the impact of each scenario, model proposed work programs (vegetation, pole replacement etc.) and determine their effectiveness.

Utilities are empowered with consistent data-driven decision-making that holds up to any level of scrutiny, from end customers through to regulators.

Automatic conflation of GiS with true location of assets scanned by LiDAR.


Four steps were used to analyze the data and produce the report:

  1. Pre-classification and ingestion of various data sources (LiDAR, GIS, and pole standard construction data)
  2. 3D model generation including auto-classification of LiDAR and autocorrecting of data where possible
  3. Network risk analysis, including accurate pole locations, validation of construction standards, and identification of broken conductors
  4. Automated reporting including exporting the results into geo-spatial and tabular results

Automatic detection of discrepancy between GiS and LiDAR data of recorded assets.


Regardless of the quality of the individual data sources, utilizing various data inputs such as LiDAR, GIS and engineering drawings and standards enabled the creation of an accurate engineering-grade model.

Engineering and physics algorithms and machine learning were used to autocorrect and autocomplete the data where possible. With Neara, even a low-quality LiDAR data set (10-20 psm) combined with additional data sources can unlock tremendous cost savings and efficiencies.


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