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Automatic Building Extraction from Google Earth Images under Complex Backgrounds Based on Deep Instance Segmentation

Qi Wen1, Kaiyu Jiang2, Wei Wang3

  • 1National Disaster Reduction Center of China, Beijing 100124, China. whistlewen@aliyun.com.

Sensors (Basel, Switzerland)
|January 18, 2019
PubMed
Summary

This study introduces an improved Mask Region Convolutional Neural Network (Mask R-CNN) for extracting buildings from remote sensing images. The method accurately detects rotated bounding boxes and segments buildings, aiding in natural disaster assessment.

Keywords:
Mask R-CNNbuilding extractiondeep learninginstance segmentationreceptive field blockrotation bounding box

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Area of Science:

  • Computer Vision
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Building damage is a major component of post-natural disaster assessments.
  • Extracting building information from optical remote sensing images is crucial for disaster reduction and evaluation.
  • Traditional methods for building extraction often require significant human-computer interaction or manual interpretation.

Purpose of the Study:

  • To propose an improved Mask Region Convolutional Neural Network (Mask R-CNN) for automated building extraction from remote sensing imagery.
  • To enhance the detection of rotated bounding boxes and segmentation of buildings in complex backgrounds.
  • To improve the accuracy and efficiency of building extraction for natural disaster assessment.

Main Methods:

  • An improved Mask R-CNN model was developed, incorporating modifications for rotated bounding box detection.
  • A new term, principal directions (θ), was added to predict minimum enclosing rectangles for buildings.
  • A novel layer integrating atrous convolution and inception blocks was designed and inserted into the segmentation branch for enhanced feature learning.

Main Results:

  • The proposed method demonstrated effectiveness in detecting rotated bounding boxes of buildings.
  • Simultaneous segmentation of buildings from complex backgrounds was achieved with promising accuracy.
  • Experiments on a large, diverse Google Earth remote sensing dataset validated the method's performance.

Conclusions:

  • The improved Mask R-CNN offers a robust solution for automated building extraction from optical remote sensing data.
  • The method shows significant potential for applications in natural disaster assessment and urban planning.
  • Further research can explore its application on different types of remote sensing data and disaster scenarios.