Satellite Imagery Construction Detection Using CNN Models
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Solution Overview
Problem
Current methods for tracking new construction activity in the construction industry are inefficient, relying on manual visual surveys which are hindered by weather and personnel availability, and lack precision in detecting initiation and progress of construction, grading, and road development.
Innovation Solution
A method utilizing a machine learned model, specifically a Convolutional Neural Network (CNN), to analyze satellite or aerial images for features indicative of new construction activity, allowing for automated detection and tracking of construction stages, grading, and road development by converting image pixel locations to geographic coordinates and providing confidence scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual visual surveys are used to track construction activity, then personnel can directly observe and record construction milestones, but the method is hindered by weather conditions and personnel availability, reducing reliability and productivity
Solution Approach 1:
The patent replaces manual visual surveys with an automated computer vision system that uses satellite or aerial imagery processed through machine learning models. This substitution eliminates dependence on human personnel and weather constraints, providing continuous, reliable detection of construction milestones while significantly improving data collection efficiency through automated image analysis
Solution Approach 2:
The system enables self-service detection by automatically analyzing satellite or aerial images to identify construction features and milestones without requiring human intervention. The machine learning model independently processes imagery to detect construction activity, calculate metrics, and generate reports, making the detection process autonomous and continuously operational regardless of weather or personnel availability
2Measurement precision
If manual visual surveys are conducted to detect construction features, then detailed observations can be made, but the precision in detecting initiation and progress of construction, grading, and road development is insufficient
Solution Approach 1:
The patent segments the complex task of construction detection into distinct machine learning models specialized for different construction features: one model detects construction initiation, another detects grading progress, and a third detects road development. Each model is trained to recognize specific features with high precision, and their results are integrated to provide comprehensive construction monitoring without requiring a single overly complex system
Solution Approach 2:
The system introduces satellite or aerial imagery as an intermediary medium between the detection system and construction sites. This intermediary provides high-resolution visual data that can be analyzed with precision while eliminating the need for personnel to physically access sites. The imagery serves as a bridge that enables detailed observation without direct human involvement, maintaining detection precision while simplifying the operational system
3Loss of information
If on-site personnel are deployed to track construction milestones, then direct observation is possible, but costs increase and data collection efficiency decreases
Solution Approach 1:
The patent creates a universal system that uses satellite or aerial imagery to simultaneously detect multiple construction milestones including construction initiation, grading progress, and road development. This multi-functional approach captures comprehensive construction information from a single data source, eliminating the need for separate personnel assignments for different milestones while improving data collection efficiency through automated processing of all features in parallel
Data Source
AI summary
Methods, non-transitory computer-readable storage media, and computer or computer systems directed to detecting, analyzing, and tracking new residential or commercial construction activity using satellite or aerial imagery in combination with a machine learned model are described.