Dam Inspection Report Document Processing
Description
The AI use case allows our staff to identify the biggest issues and trends across 2,100+ dams.
Detailed example
The model outputs text and checkbox responses, including dam metadata, inspection issue responses (yes and no checkboxes), and further remarks on the issue regarding what has been or needs to be done on the dam.
AI / analytics pattern
Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
The benefit and positive outcome from the AI is a wholistic understanding of dam issues across 2,100+ dams. This allows us to better analyze the data and build a risk-prioritization system to rank sites and projects to work on, reducing labor hours required to complete the task manually, and reducing the dependence on specific staff to maintain knowledge of each dam's inspection response.
Audit / financial statement impact
The AI is used for information extraction that informs dam portfolio decisions. However, the principal basis for decisions is made by humans for the repairs, and is vetted through aerial imagery analysis, the Oklahoma Conservation Commission, and associated dam sponsors.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
The data is made up of dam inspection forms collected by dam owners (ex. conservation districts, cities) or Natural Resources Conservation Service (NRCS) staff. The data was validated through manual checks and is composed of 30+ dam inspection reports with different handwriting. The data contains 120+ variables that are read from each two-page inspection report. The data types are date, text, table (text variable types in the table), and checkbox binary outputs (checked: yes or no).