AI-Automated Structure Digitization

AI-Automated Structure Digitization enables faster, more efficient corridor‑wide mapping.

Enertech’s AI-Automated Structure Digitization service is built around advanced deep learning and artificial intelligence to identify and digitize structures accurately and consistently within an operator’s assessment corridor. This technology‑first approach enables Enertech to produce a complete, polygon‑based structure geodatabase that reflects real‑world conditions while significantly reducing the time, effort, and cost associated with traditional manual structure mapping.

By combining proprietary AI with disciplined GIS review, Enertech delivers structure geometry operators can trust as the foundation for attribution, occupancy analysis, and integrity decision‑making.

Deep Learning at the Core

At the center of AI Automated Structure Digitization is Enertech’s proprietary deep learning system, purpose‑built for large‑scale structure identification and digitization. The system scans the defined assessment corridor and automatically detects structures, generating polygon geometry that aligns with true structure footprints.

The tool has been trained on millions of structures across diverse environments, enabling it to rapidly identify many structures with a high degree of accuracy. As additional imagery and structures are analyzed over time, the model continues to improve — increasing consistency, performance, and scalability across programs.

By automating the most labor‑intensive portion of structure digitization, deep learning allows Enertech to dramatically accelerate corridor‑wide structure identification while reducing reliance on manual, structure‑by‑structure digitization.

Designed to Work with Existing Geodatabases

AI Automated Structure Digitization data integrates cleanly with an operator’s existing GIS environment and structure geodatabases. Programs commonly include:

  • Reviewing and validating an existing polygon‑based structure geodatabase
  • Converting point‑based structure geodatabases into accurate polygons
  • Identifying and digitizing new structures
  • Flagging obsolete structures that no longer exist in the real world

Leveraging deep learning allows these improvements to be completed more efficiently and cost‑effectively than traditional manual review, particularly across large or rapidly changing corridors.

Accuracy Through Expert Validation

While deep learning provides speed and consistency, expert GIS review ensures confidence.

Following AI‑driven structure identification, Enertech analysts perform systematic quality control to:

  • Remove false or misidentified structures
  • Correct geometry inaccuracies
  • Identify structures missed by automation
  • Confirm obsolete structures for removal

This combination of AI‑accelerated mapping and focused human validation ensures operators receive a structure geodatabase that is both comprehensive and defensible — without the time and cost burden of purely manual workflows.

Imagery as an Enabler

High‑resolution imagery is used as a reference layer supporting the deep learning and digitization process. Enertech works with:

  • Operator‑provided imagery, or
  • Satellite or aerial imagery acquired by Enertech

Imagery supports accurate structure detection but does not drive the workflow or define the program approach.

Built for Integrity Confidence

Automated Structure Mapping produces an authoritative, polygon‑based structure geodatabase that supports:

  • Structure attribution intelligence
  • Occupancy and identified site analysis
  • HCA, MCA, and Class Location determinations
  • Repeatable integrity workflows over time

By reducing mapping cycle time and associated costs while maintaining strict quality standards, Enertech enables operators to refresh and maintain structure data efficiently as conditions change.

The Result

  • Comprehensive corridor coverage.
  • Faster delivery with lower effort.
  • Accurate and complete structure geodatabases.

Enertech’s Automated Structure Mapping combines the efficiency of deep learning with the assurance of expert GIS validation—delivering scalable structure intelligence operators can rely on and provides data driven insight while preserving the integrity of their existing classification frameworks.