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Domain

Stormwater Inspection & Assessment

Stormwater inspection and condition assessment — CCTV sewer inspection with PACP/MACP scoring, catch basin inspection and cleaning programs, outfall inspection, deficiency tracking, and condition rating.

AI-assisted

CCTV Analysis

Certified

PACP/MACP

Scheduled

CB Programs

1–5 Scale

Condition Rating

Purpose-Built for Canadian Municipalities

Ontario Compliant
MFIPPA Ready
AODA Accessible
Bilingual Support
Canadian Hosted
SOC 2 Aligned

Purpose & Scope

What this module owns

Clear ownership boundaries prevent duplication and ensure every capability has exactly one authoritative home.

Owns

6

Delegated to

5

Stormwater asset inventory, pipe/CB/outfall registry

stormwater-infrastructure

Inspection workflow engine, scheduling, forms

inspection-engine

Field data collection, offline mobile, GPS

mobile-field

Spatial analysis, mapping, catchment boundaries

geospatial-engine

AI/ML defect classification models

ai-ml-engine

These capabilities are handled by dedicated modules and consumed via stable API contracts — keeping boundaries clean and ownership unambiguous.

Core Capabilities

What it does

3 capability groups comprising 6 discrete capabilities — each with API surface, business rules, and data ownership.

CCTV sewer inspection with video recording, GPS-referenced observations, and timestamped defect logging per PACP/MACP standards.

Video Integration

CCTV video feeds linked to pipe segments — timestamped observations synchronized to video position.

Defect Coding

NASSCO PACP/MACP defect coding — structural, operational, and construction defects with severity grading.

Observation Log

GPS-referenced observations at measured distance from MH — crack, fracture, deformation, root intrusion, deposit, infiltration.

Condition Scoring

Automated structural and O&M condition scoring from coded observations — 1 (excellent) to 5 (failed).

Machine learning models that analyze CCTV footage in real-time to identify and classify pipe defects with confidence scoring.

Real-time Analysis

AI model processes CCTV footage during inspection — flagging potential defects for operator confirmation.

Defect Classification

Automated classification: cracks, fractures, root intrusion, joint displacement, corrosion, deposits, infiltration.

Confidence Scoring

Each AI detection assigned a confidence score — high-confidence detections auto-coded, low-confidence flagged for review.

Model Improvement

Operator confirmations/corrections feed back into model training — continuous accuracy improvement.

Every module owns a single bounded context, exposes stable APIs, and can be composed into any Civic product — that's the architecture that scales.

Krutik Parikh

Creator of Civic

Data Model

Entity Architecture

4 entities with 5 relationships — the authoritative schema for this bounded context.

Entities

Select an entity to explore its fields and relationships

API Surface

Integration Endpoints

7 RESTful endpoints across 6 resource groups — plus 5 domain events for async integration.

|
GET

/api/v1/stormwater-inspection/cctv

CCTV inspection records with PACP scoring

GET

/api/v1/stormwater-inspection/cctv/{inspectionId}/observations

CCTV observations with AI detection flags

Technical Specifications

Performance, Compliance & Configuration

AI Defect Detection Accuracy

Target≥ 85% true positive rate

CCTV Video Sync

Target< 1 second latency

Mobile Offline Support

TargetFull inspection capability

Condition Rating Consistency

Target± 0.5 inter-rater

System Availability

Target99.9%

FAQ

Frequently Asked Questions

Ready to Integrate

Build on Stormwater Inspection & Assessment

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