Computer Vision Geospatial Property Feature Detection
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Solution Overview
Problem
Existing computer vision systems for geospatial property feature detection and extraction from digital images, such as aerial and satellite images, are time-consuming, require significant manual input, and can only detect specific types of objects, limiting their applicability and efficiency.
Innovation Solution
A computer vision system that automatically detects and extracts both geometric and non-geometric property features from digital images, using a pipeline of image processing steps including imagery selection, pre-processing, pixel-wise labeling, label post-processing, and geometry extraction, capable of handling various image sources and projecting features into world coordinates, enabling the detection of features like tree canopy, pools, roof materials, and structural elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processing methods are used to detect property features from digital images, then detection accuracy can be maintained, but processing time increases significantly and productivity decreases
Solution Approach 1:
The patent replaces manual mechanical processing with an automated computer vision system that uses machine learning models and algorithms to detect and extract property features from digital images. The system automatically identifies geometric and non-geometric features without human intervention, thereby maintaining detection accuracy while significantly improving processing speed and productivity.
Solution Approach 2:
The system enables self-service automation where the computer vision algorithm independently processes images, detects features, and generates results without requiring manual input or intervention. The automated pipeline handles image preprocessing, feature detection, and post-processing tasks autonomously, eliminating the trade-off between accuracy and speed that plagues manual methods.
2Measurement precision
If specialized software systems are implemented to process specific image types, then detection capability for certain features is improved, but system adaptability and versatility decrease
Solution Approach 1:
The patent implements a universal computer vision system capable of processing multiple types of digital images (aerial, satellite, ground-based, UAV, mobile device imagery) and detecting various property features (geometric and non-geometric). The system uses a flexible pipeline architecture with configurable processing steps that can be adapted to different image sources and feature types, thereby maintaining high detection capability while achieving broad versatility.
Solution Approach 2:
The system employs dynamic and adaptable processing pipelines that can be configured based on the specific image type and desired feature detection goals. The flexible architecture allows the system to adjust its processing steps, parameters, and algorithms to optimize performance for different applications while maintaining a core unified framework.
3Adaptability or versatility
If comprehensive feature detection is implemented to detect all types of objects, then system versatility improves, but device complexity and difficulty of operation increase
Solution Approach 1:
The patent segments the comprehensive feature detection task into distinct processing stages: image preprocessing, geometric feature detection, non-geometric feature detection, and post-processing. Each stage handles specific aspects of feature extraction independently, allowing the system to achieve versatile comprehensive detection while managing complexity through modular organization of processing steps.
Solution Approach 2:
The system performs preliminary actions by implementing a structured processing pipeline that prepares images through preprocessing steps before detection, and performs post-processing after detection. This organized sequence of preliminary and follow-up actions simplifies the overall system operation while enabling comprehensive feature detection across multiple categories.
Data Source
AI summary
Systems and methods for property feature detection and extraction using digital images. The image sources could include aerial imagery, satellite imagery, ground-based imagery, imagery taken from unmanned aerial vehicles (UAVs), mobile device imagery, etc. The detected geometric property features could include tree canopy, pools and other bodies of water, concrete flatwork, landscaping classifications (gravel, grass, concrete, asphalt, etc.), trampolines, property structural features (structures, buildings, pergolas, gazebos, terraces, retaining walls, and fences), and sports courts. The system can automatically extract these features from images and can then project them into world coordinates relative to a known surface in world coordinates (e.g., from a digital terrain model).