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Building Change Detection by Using Past Map Information and Optical Aerial Images
Motohiro TAKAGI Kazuya HAYASE Masaki KITAHARA Jun SHIMAMURA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2021/06/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Artificial Intelligence, Data Mining
neural networks, change detection, map information,
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This paper proposes a change detection method for buildings based on convolutional neural networks. The proposed method detects building changes from pairs of optical aerial images and past map information concerning buildings. Using high-resolution image pair and past map information seamlessly, the proposed method can capture the building areas more precisely compared to a conventional method. Our experimental results show that the proposed method outperforms the conventional change detection method that uses optical aerial images to detect building changes.