Improvement of automatic building region extraction based on deep neural network segmentation

ABSTRACT

This work seeks to improve the accuracy of building region extraction, in which each pixel in a scenery image is determined to be part of a building or part of the background. Specifically, UNet++ and MANet, which are state-of-the-art deep neural networks (DNNs) for segmentation, were applied to building extraction. Our experiment using 105 scenery images in the Zurich Buildings Database (ZuBuD) showed that these networks significantly improved the F-measure by at least 1.67% as compared with conventional building extraction. To address the shortcomings of segmentation networks, we also developed a method based on refinement of the building region extracted by a segmentation network. The proposed method demonstrated its effectiveness by significantly increasing the F-measure by at least 1.15%. Overall, the F-measure was improved by 3.58% as compared with conventional building extraction.

KEYWORDS:

• Building region extraction
• deep neural network
• segmentation
• GrabCut
• convex hull

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Noboru Hayasaka

Noboru Hayasaka He received B.S., M.S., and Ph.D. degrees from Hokkaido University in 2002, 2004, and 2007, respectively. At present, he is an associate professor at Osaka Electro-Communication University. His current research interests include signal processing and speech recognition.

Yuki Shirazawa

Yuki Shirazawa He received a B.S. degree from Osaka Electro-Communication University in 2022 and is now an M.S. student there. His current research interest is image processing.

Mizuki Kanai

Mizuki Kanai He has been a B.S. student at Osaka Electro-Communication University since 2019. His current research interest is image processing.

Takuya Futagami

Takuya Futagami He received B.S., M.S., and Ph.D. degrees from Osaka University in 2013, 2015, and 2021, respectively. At present, he is a lecturer at Aichi Gakuin University. His current research interests include image processing and image recognition.