Co-Teaching Neural Networks for Noisy Building Footprint Detection
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Solution Overview
Problem
Conventional methods for automated detection of building footprints in satellite images face challenges due to user errors in labeling, high costs of trained annotators, and the inability of traditional algorithms to accurately handle noisy or sparse data, leading to inaccurate predictions and poor generalization.
Innovation Solution
The system employs a co-teaching method using simulated noisy data to train neural networks by distorting polygon boundaries in training images, creating noisy labels that mimic real-world noise, and ranking training data for improved accuracy in identifying building footprints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional algorithms are used for automated detection of building footprints, then the process is simple and fast, but the accuracy is poor due to inability to handle noisy or sparse data
Solution Approach 1:
The patent creates synthetic noisy labels by copying and distorting ground truth annotations to simulate real-world noisy data. This allows the model to be trained on artificially generated noisy data without requiring actual noisy field data, thereby improving detection accuracy while avoiding the complexity of building complex noise modeling systems.
Solution Approach 2:
The patent performs preliminary data preparation by pre-processing satellite images and pre-generating noisy labels before model training. This includes image normalization, polygon extraction from ground truth, and distortion application. By preparing data in advance, the system achieves high accuracy without requiring complex real-time processing during detection.
2Measurement precision
If user input is used to label images, then some accuracy can be achieved, but user errors and lack of domain expertise lead to sparse or inaccurate labeling
Solution Approach 1:
The system uses automated algorithms to generate and label training data without human intervention. The noisy label generation process is fully automated, taking ground truth polygons and automatically creating distorted versions as training labels. This eliminates human errors and大幅提高 labeling efficiency while maintaining consistent quality.
Solution Approach 2:
Instead of relying on users to create labels, the system copies ground truth annotations and applies systematic distortions to create noisy labels. This copying approach ensures that the underlying structure remains accurate while introducing controlled noise, achieving both accuracy and efficiency.
3Measurement precision
If trained annotators are hired to label images, then labeling accuracy improves, but the cost in terms of time and money increases significantly
Solution Approach 1:
The patent uses inexpensive synthetic noisy labels generated by algorithms instead of expensive human annotators. These artificially created labels serve the purpose of training the model effectively without incurring the high costs and time investments required for hiring and training professional annotators. The synthetic labels are disposable and can be generated on-demand.
Solution Approach 2:
The system copies ground truth data and transforms it into training labels through automated processes, eliminating the need to pay for human annotation services. This copying and transformation approach provides a cost-effective alternative to hiring trained annotators while maintaining sufficient labeling quality.
4Measurement precision
If simple algorithms are used for automated labeling, then the process is fast and inexpensive, but the predictions are inaccurate and generalization is poor
Solution Approach 1:
The system performs preliminary processing of satellite images and pre-generation of noisy labels before model training. This advance preparation allows the use of more sophisticated models without sacrificing processing speed, as the heavy lifting is done beforehand. The pre-processed data can be efficiently used during actual detection operations.
Solution Approach 2:
The patent applies parameter changes by systematically distorting polygon boundaries using various transformation parameters (scaling, rotation, translation, skewing). This introduces controlled noise that improves model robustness and generalization. The parameter-based approach maintains processing efficiency while significantly improving prediction accuracy through enhanced training data quality.
Data Source
AI summary
The disclosed computer-implemented method may include collecting a set of labels that label polygons within a training set of images as architectural structures. The method may also include creating a set of noisy labels with a predetermined degree of noise by distorting boundaries of a number of the polygons within the training set of images. Additionally, the method may include simultaneously training two neural networks by applying a co-teaching method to learn from the set of noisy labels. The method may also include extracting a preferential list of training data based on the two trained neural networks. Furthermore, the method may include training a machine learning model with the preferential list of training data. Finally, the method may include identifying one or more building footprints in a target image using the trained machine learning model. Various other methods, systems, and computer-readable media are also disclosed.