Data Integration

Integrate all your Key Data and Layers

Enernite Platform comes with more than 100 European layers. However, if you have any proprietary/non-public data, we can integrate data from most GIS formats.

Integrate proprietary/non-public data

We can integrate any proprietary/non-public data/layers right to your company’s account as a custom layer. If the data is not coming from a public source, then we will automatically integrate it as a custom layer which only you and other users in your company can view. Access to your imported data is limited to authorized members.

Integrate external sources

External sources like public webmaps and mapservers/feature servers are subject to the terms of the data owner. If the data provider has a warning that data may not be re-displayed or provides access on a per-user basis, then we may not be able to integrate the layer.


KML (Keyhole Markup Language) is the data format used by Google Earth Pro and Google Maps.

Shapefiles / GDBs

Shapefile (.shp) and File Geodatabase (.gdb) are storage formats for spatial data developed by Esri.

Excel / CSVs

Excel sheets and CSVs (provided they have lat/long) are stored tabular data of numbers and text.

Other public webmaps

External sources like public webmaps and mapservers/ feature servers via WMS, WFS or WCS.

Secure data permissions and authentication

Access to your imported data is limited to authorized members in your organization who require it for their job and all data access is logged. Your organization admin will have full control over data access in the organization.

  • Enernite utilizes a variety of data security and vulnerability checks throughout the development lifecycle
  • Security breaches will be communicated within 48 hours, and vulnerabilities are fixed ASAP
  • Data is encrypted in-transit and at rest to provide steadfast protection
  • Security is a company-wide endeavor. All Enernite employees complete an annual security training program

Proprietary Enernite data from satellite imagery

Enernite has developed proprietary machine learning models to extract information from satellite imagery, allowing users to evaluate and track trends over time. With regularly updated satellite imagery and global coverage, Enernite provides valuable recent insights across the globe.

  • Building detection to identify nearby buildings of various sizes from satellite imagery
  • Object detection of utility-scale solar installations using machine learning and satellite imagery
  • Utilizing deep learning networks for land cover classification to give the user insights of land use nearby the site
  • Leverage optical and radar imagery and analytics to map historical flooding and predict water-related geohazards

Ready to see it in action?

There is a race to identify the best project sites for renewables. In this race, you need the best tools available to gain the competitive edge.

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