Roof Vector Projection for Scalable 3D Building Height Extraction
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Solution Overview
Problem
Existing methods struggle to efficiently extract three-dimensional structure information from overhead imagery, particularly for buildings with complex roof geometries, as conventional approaches lack scalability and accuracy in determining height from various camera perspectives and lighting conditions.
Innovation Solution
A scalable approach using two-dimensional vector maps combined with additional imagery from alternate viewpoints for vector data projection and feature matching, leveraging machine learning techniques for self-supervised training to determine the height of structures, even those with complex roof geometries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional photogrammetry and stereoscopic techniques are used to extract three-dimensional structure information from overhead imagery, then measurement precision may be maintained, but productivity is reduced due to lack of scalability
Solution Approach 1:
The patent introduces an intermediary machine learning model that acts as a mediator between overhead imagery and three-dimensional structure extraction. The model is trained to predict height and geometric features directly from two-dimensional images, eliminating the need for complex multi-view geometry computations while achieving scalable processing of large datasets.
Solution Approach 2:
The patent replaces traditional mechanical photogrammetry systems that require multiple cameras and complex coordinate transformations with a machine learning-based system. The ML model substitutes the mechanical/optical measurement process with a data-driven approach that processes images through neural network layers to directly output three-dimensional structure information.
2Measurement precision
If conventional methods are used for structures with complex roof geometries, then measurement precision deteriorates, but device complexity remains high
Solution Approach 1:
The patent changes the parameters processed by the system from raw pixel coordinates requiring complex geometric transformations to high-level semantic features extracted by the machine learning model. The model transforms images into feature representations that capture roof geometry characteristics, enabling accurate height determination for complex structures through parameter transformation rather than complex computational geometry.
Solution Approach 2:
The patent uses two-dimensional vector maps as simplified copies or representations of the actual three-dimensional structures. These vector maps serve as intermediate representations that capture essential geometric information in a simplified format, making it easier to process and analyze complex roof geometries without dealing with full three-dimensional complexity.
3Measurement precision
If additional imagery from alternate viewpoints is collected to improve three-dimensional extraction, then measurement precision improves, but loss of time increases due to data collection requirements
Solution Approach 1:
The patent extracts only the essential height information directly from single-overhead imagery using the machine learning model, eliminating the need to collect and process additional imagery from multiple viewpoints. The model is trained to extract the critical three-dimensional parameter (height) directly from the available two-dimensional overhead view, removing unnecessary data collection steps.
Solution Approach 2:
The patent performs preliminary training of the machine learning model on datasets with known three-dimensional structures before deployment. This preliminary action embeds the knowledge of how to infer height from overhead imagery into the model during training, so that during actual operation, height can be determined directly from single images without requiring additional viewpoint data collection.
Data Source
AI summary
Methods and systems for determining the height of structures based on imagery of the structures and associated two-dimensional vector data are provided. An example method involves projecting two-dimensional vector data outlining a roof of a structure into images of the structure captured from different perspectives and feature matching the vector data across the imagery to determine a best-matching three-dimensional position for the roof situated at the height of the structure.