Satellite Imagery Detection of Housing Construction Stages
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Solution Overview
Problem
Existing methods for tracking construction activity in housing projects rely on manual visual surveys, which are time-consuming and prone to weather and personnel availability issues, and lack efficient automated systems for detecting construction stages and infrastructure development.
Innovation Solution
Utilizing satellite or aerial imagery and Convolutional Neural Networks (CNN) to automatically detect and classify construction stages, road, and grading activities by training a machine learned model to recognize distinct features in images, converting pixel data to geographic coordinates, and integrating with geographic information systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual visual surveys are used to track construction activity, then data collection can be performed with simple equipment, but the process is time-consuming and prone to weather and personnel availability issues
Solution Approach 1:
The patent replaces manual visual surveys with an automated machine learning system that processes satellite and aerial imagery. The system uses convolutional neural networks to automatically detect construction stages, roads, and grading activities, eliminating the need for human surveyors to physically visit sites and reducing dependence on weather conditions and personnel availability.
Solution Approach 2:
The patent creates digital copies of the physical construction site environment through satellite and aerial imagery. These images serve as representations of the actual construction progress, allowing the system to analyze construction stages without physically being present at the site, thereby improving efficiency and reducing time loss.
2Extent of automation
If manual visual surveys are used to detect construction stages, then the system remains simple, but it lacks automated capabilities for detecting construction stages and infrastructure development
Solution Approach 1:
The patent introduces machine learning models as intermediaries between the raw satellite/aerial imagery and the construction stage detection. These models process the imagery and translate visual features into automated detection results, enabling automated construction stage detection while managing system complexity through specialized algorithms rather than complex hardware systems.
Solution Approach 2:
The patent develops a multi-functional system that can detect multiple construction-related features simultaneously including construction stages, roads, and grading activities using the same imagery and machine learning framework. This universal approach improves automation extent without proportionally increasing system complexity by reusing the same core processing architecture.
3Productivity
If manual data collection methods are used, then the system is easier to operate, but it is less efficient and more costly
Solution Approach 1:
The patent implements a self-service system where the machine learning model automatically processes satellite and aerial imagery to detect construction stages without requiring manual intervention. The system autonomously analyzes images, identifies construction activities, and generates reports, thereby improving productivity while maintaining ease of operation through automated decision-making.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously processes new imagery and updates construction stage detections. This automated feedback loop allows the system to track progress over time efficiently without manual re-surveying, improving productivity while the automated nature maintains operational simplicity through consistent algorithmic processing.
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.