Overhead Imagery Object Outlines Using Action Sequence Prediction
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Solution Overview
Problem
Existing automated techniques for generating object outlines from overhead imagery often fail to capture detailed geometry, such as protrusions and shape irregularities, leading to inaccuracies in digital maps that affect their visual quality and functional use.
Innovation Solution
A machine learning model is trained to predict a sequence of user actions, such as clicks, based on a training dataset of annotated overhead images, to accurately generate object outlines, which are then used to correct and stitch together tiles in digital maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated techniques are used to generate object outlines from overhead imagery, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The automated system segments the object outline generation process into multiple components: initial automated boundary detection, identification of uncertain regions, and targeted refinement. This segmentation allows the system to maintain high productivity through automation while improving measurement precision by focusing computational resources on critical areas where automated methods fail.
Solution Approach 2:
The system introduces an intermediary confidence score mechanism that acts as a mediator between automated detection and manual verification. By computing confidence scores for each boundary point and selectively flagging only low-confidence regions for review, the system maintains automation efficiency while ensuring precision where needed.
2Measurement precision
If manual annotation is used to capture detailed object geometry, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
Instead of requiring complete manual annotation of all object boundaries, the system applies partial manual action only to regions where the automated system expresses low confidence. This selective approach maintains measurement precision for critical boundaries while preserving productivity by avoiding unnecessary manual intervention in high-confidence regions.
Solution Approach 2:
The system performs self-service by automatically identifying and flagging its own uncertain predictions. The confidence score mechanism enables the automated system to self-evaluate its performance and selectively request human assistance only when needed, reducing overall dependency on manual annotation while maintaining precision.
3Device complexity
If simple shape approximations are used for objects, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The system dynamically adapts the complexity of shape representation based on the specific object and context. Rather than forcing all objects into simple geometric approximations, the system generates detailed boundary representations where needed while maintaining simplicity where appropriate, allowing measurement precision to match the actual complexity requirements of each object.
Data Source
AI summary
Some implementations relate to using a trained machine (ML) model to identify object boundaries from overhead imagery. The ML model may be trained by obtaining training data including overhead images of a scene and corresponding user input actions and being trained based on the training data. The ML model obtains overhead imagery that depicts a plurality of objects having an unknown object boundary. The trained ML model generates predicted sequences of user input actions associated with object boundaries of the plurality of objects in the overhead imagery. Object boundaries of the plurality of objects are determined based on the predicted sequences of user input actions.