Construction Stage Detection Using Satellite Imagery And CNNs
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Solution Overview
Problem
Existing methods for tracking construction activity in housing projects rely on manual visual surveys, which are weather-dependent and resource-intensive, lacking efficiency and scalability.
Innovation Solution
Utilizing satellite or aerial imagery combined with machine learning models, specifically Convolutional Neural Networks (CNNs), to automatically detect and classify stages of construction, presence of roads, and grading activities by analyzing image features and converting pixel coordinates to geographic coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual visual surveys are used to track construction activity, then data can be collected through direct observation, but the method is weather-dependent and resource-intensive
Solution Approach 1:
The patent replaces manual mechanical surveying with an automated optical system using satellite/aerial imagery and machine learning algorithms. The system processes images to detect construction features automatically, eliminating the need for physical field surveys and reducing resource consumption while maintaining data reliability
Solution Approach 2:
The system creates a digital copy of the construction site through satellite or aerial imagery, allowing analysis and tracking without physical presence. This virtual replication enables continuous monitoring regardless of weather conditions or resource availability
2Reliability
If manual visual surveys are used to track construction activity, then direct observation is possible, but the method lacks efficiency and scalability
Solution Approach 1:
Manual surveying is replaced with automated image processing systems that can analyze multiple construction sites simultaneously. The machine learning model processes satellite and aerial images to detect construction features, enabling scalable tracking across numerous projects without proportionally increasing human resources
Solution Approach 2:
The system is designed to handle multiple construction sites and various construction stages through a single unified platform. The machine learning model can detect different construction features (foundations, structures, roads) across diverse geographic locations, providing universal tracking capability
3Productivity
If satellite or aerial imagery with machine learning is used, then scalability and efficiency improve, but system complexity increases
Solution Approach 1:
The system segments the complex task of construction tracking into distinct components: image acquisition from satellite/aerial sources, preprocessing of imagery, feature detection using machine learning models, and result interpretation. This modular segmentation manages system complexity by breaking down the overall process into manageable, independent modules
4Loss of information
If manual visual surveys are used, then local context can be observed, but the method is weather-dependent
Solution Approach 1:
The system creates virtual copies of construction sites through satellite and aerial imagery that can be analyzed without physical presence. These digital replicas provide consistent views unaffected by weather conditions, eliminating the harmful effect of weather dependency while maintaining visibility of construction details
Solution Approach 2:
The system performs preliminary image capture and processing to establish a baseline view of the construction site before weather conditions deteriorate. By having pre-acquired imagery and automated processing capabilities, the system ensures continuous monitoring capability regardless of subsequent weather changes
Data Source
AI summary
Methods, non-transitory computer-readable storage media, and computer or computer systems directed to detecting, analyzing, and tracking stages of housing construction using satellite or aerial imagery in combination with a machine learned model are described.