Satellite Road And Grading Detection For Construction Tracking
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Solution Overview
Problem
Existing methods for tracking construction activity, such as road laying and grading, are inefficient and require manual visual surveys, which are hindered by weather and personnel availability, and lack automated, accurate data collection from satellite or aerial imagery.
Innovation Solution
A method using machine learned models, particularly Convolutional Neural Networks (CNNs), to analyze satellite or aerial images for detecting roads and grading activity, converting pixel locations to geographic coordinates, and providing probabilistic predictions, thereby automating the tracking of construction stages and reducing data consumption costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual visual surveys are used to track construction activity, then personnel can directly observe and verify construction stages, but the process is hindered by weather conditions and personnel availability, reducing productivity
Solution Approach 1:
The patent replaces manual visual surveys with an automated machine learning system that processes satellite and aerial imagery. The machine learning model automatically detects construction stages, roads, and grading activities without human intervention, eliminating weather and personnel constraints while maintaining verification accuracy through algorithmic analysis of image data
Solution Approach 2:
The system enables self-service data collection by automatically processing and analyzing construction site imagery. The machine learning model independently identifies construction stages and features without requiring human observers, allowing the system to monitor itself and generate reports autonomously based on input images
2Measurement precision
If manual data collection methods are used, then direct observation of construction sites is possible, but the process is time-consuming and lacks automated tracking capabilities
Solution Approach 1:
The patent substitutes manual observation with automated machine learning processing that analyzes satellite and aerial images to detect construction stages. The system processes multiple images rapidly to identify and track construction progression, significantly reducing data collection time while maintaining or improving measurement precision through algorithmic feature recognition
Solution Approach 2:
The system performs preliminary processing by pre-processing and training machine learning models on construction imagery datasets before actual monitoring. This preliminary training enables the system to quickly and accurately detect construction stages during actual data collection, reducing real-time processing time while maintaining high detection accuracy
3Productivity
If automated machine learning models are used to analyze satellite or aerial images, then data collection is automated and time-efficient, but the system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex machine learning system into distinct functional modules: image processing module, machine learning detection module, and data output module. The machine learning model itself is segmented into feature extraction, classification, and prediction components, making the overall system more manageable and easier to implement while maintaining automated data collection efficiency
Solution Approach 2:
The patent introduces an intermediary layer in the form of a machine learning model that mediates between raw satellite/aerial imagery and construction activity detection. This intermediary component simplifies the system architecture by providing a standardized interface for image input and structured data output, reducing the complexity of direct integration between imaging systems and data processing systems
Data Source
AI summary
Methods, non-transitory computer-readable storage media, and computer or computer systems directed to detecting, analyzing, and tracking roads and grading activity using satellite or aerial imagery in combination with a machine learned model are described.