Satellite Imagery Construction Detection Using CNNs
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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 weather-dependent and resource-intensive, and lack real-time data on construction stages and road/grading development.
Innovation Solution
A machine learning-based method using Convolutional Neural Networks (CNNs) to analyze satellite or aerial images, identifying features characteristic of new construction activity, road development, and grading by demarcating boundaries and providing predictions with confidence scores, thereby automating the tracking process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual visual surveys are used to track construction activity, then data can be collected by people driving through sites, but the process is weather-dependent and resource-intensive
Solution Approach 1:
The patent replaces manual visual surveys with automated computer vision algorithms that process satellite or aerial images. The system uses machine learning models to detect construction features such as foundations, walls, and roofs, eliminating the need for human surveyors to physically visit sites and reducing dependency on weather conditions.
Solution Approach 2:
The system creates digital copies of construction sites through satellite or aerial imagery. These image copies are then analyzed by computer vision algorithms to extract construction data, replacing the need for physical site visits while maintaining accurate representation of the construction activity.
2Loss of information
If manual surveys are conducted to identify construction stages, then data can be collected, but real-time data on construction stages and road/grading development is lacking
Solution Approach 1:
The patent implements continuous monitoring by processing satellite or aerial images at regular intervals to track construction progress. The system continuously updates construction stage classifications and road/grading detection results, providing real-time information without interruption in the monitoring process.
Solution Approach 2:
The system performs preliminary classification of construction features by detecting foundations, walls, and roofs in early construction stages. This preliminary action enables early identification of construction activity and stage classification before complete structures are formed, providing timely information for tracking and analysis.
3Measurement precision
If automated computer vision algorithms are used to process satellite or aerial images, then detection accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent segments the image processing task into distinct components: foundation detection, wall detection, and roof detection. Each component is handled by specialized computer vision algorithms that process specific features independently, improving detection accuracy while optimizing computational resource usage through divided processing.
Solution Approach 2:
The system applies partial processing by focusing computational resources on detecting only the most critical construction features relevant to the current monitoring needs. The algorithm prioritizes detection of key elements such as foundations and structural components, processing only necessary portions of images to reduce overall computational load while maintaining detection accuracy.
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.