Earticle

초록

영어

Automatic identification of residential areas from remote sensing images is beneficial to tasks such as urban planning and disaster assessment. Currently, residential area extraction tasks are primarily based on deep learning methods using single-modal data, and the information that a single modality can express is limited. Therefore, this paper proposes an end-to-end multi-modal semantic segmentation model that extracts features of remote sensing images and mobile phone signaling data through a dual-branch encoder, fuses the two features, and concatenates them with the feature map of the decoder stage. Experimental results show that our proposed method outperforms other models and can effectively identify residential areas.

목차

Abstract
I. INTRODUCTION
II. THE PROPOSED MODEL
A. Overall Architecture
B. Encoder-Feature Extracting
C. Decoder-Resolution Restoring
III. EXPERIMENTAL RESULT
A. Dataset
B. Evaluation Metrics
C. Results
IV. CONCLUSION
REFERENCES

키워드

저자

• Lei Yan [ Department of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing, China ] Corresponding author
• Zhiguo Yan [ Department of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004