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Updated: Jul 16, 2025
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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
Published on: May 7, 2019
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Object-oriented building extraction based on visual attention mechanism
Xiaole Shen1, Chen Yu1, Lin Lin1
1College of Big Data and Internet, Shenzhen Technology University, Shenzhen, Guangdong, China.
Peerj. Computer Science
|September 14, 2023
Summary
This study introduces an advanced method for automatic building extraction from high-resolution remote sensing images (RSI). The novel approach combines feature extraction with prior knowledge, achieving high accuracy for urban mapping and analysis.
Area of Science:
- Geoinformatics
- Urban Remote Sensing
- Computer Vision
Background:
- Buildings are crucial indicators of urban development and essential for various urban studies.
- Automatic building extraction from high-resolution remote sensing images (RSI) is vital for urban planning, sprawl analysis, and damage assessment.
Purpose of the Study:
- To develop an effective building extraction method for high-resolution RSI.
- To combine bottom-up low-level feature extraction with top-down prior knowledge guidance.
Main Methods:
- A feature space transform method considering building characteristics in RSI.
- An object-oriented approach for high-resolution RSI shadow extraction.
- Integration of low-level features with prior knowledge for enhanced building detection.
Main Results:
- Achieved user and producer accuracy above 95% for building extraction.
- Attained overall accuracy exceeding 97% with a quantity error below 1%.
- Demonstrated superior performance compared to traditional methods across all evaluated indicators.
Conclusions:
- The proposed method effectively extracts buildings from high-resolution RSI.
- The integration of feature extraction and prior knowledge significantly improves accuracy.
- The method proves effective for various urban remote sensing applications.
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