research-article

Building Extraction Using Mask Scoring R-CNN Network

Authors:

Fenglin GuoAuthors Info & Claims

CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering

Article No.: 113, Pages 1 - 5

https://doi.org/10.1145/3331453.3361644

Published: 22 October 2019 Publication History

Abstract

Extracting buildings from high resolution remotely sensed images is very practical, which can be applied to urban modeling and so on. The development of computer vision has become better, and the accuracy of recognition of convolutional neural networks has exceeded the accuracy of recognition of human eyes. In this paper, we used a deep convolutional neural network in remote sensing to achieve building extraction. The method in this paper is not based on semantic segmentation, but instance segmentation, which considered each building as an independent individual to achieve building extraction. The results showed that the proposed method is able to extract buildings with high accuracy.

References

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K.M. HE, G. GEORGIA, D. Piotr, et al (2018). Mask R-CNN. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://arxiv.org/abs/1703.06870

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E. MAGGIORI, Y. TARABALKA, G. CHARPIAT, et al (2017). Can Semantic Labeling Methods Generalize to Any City? The Inria Aerial Image Labeling Benchmark. IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, Texas, USA.

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Cited By

Li XYang YSun CFan Y(2024)Investigation, Evaluation, and Dynamic Monitoring of Traditional Chinese Village Buildings Based on Unmanned Aerial Vehicle Images and Deep Learning MethodsSustainability10.3390/su1620895416:20(8954)Online publication date: 16-Oct-2024
https://doi.org/10.3390/su16208954
Gella GWendt LLang STiede DHofer BGao YBraun A(2022)Mapping of Dwellings in IDP/Refugee Settlements from Very High-Resolution Satellite Imagery Using a Mask Region-Based Convolutional Neural NetworkRemote Sensing10.3390/rs1403068914:3(689)Online publication date: 1-Feb-2022
https://doi.org/10.3390/rs14030689
Wang YLi STeng FLin YWang MCai H(2022)Improved Mask R-CNN for Rural Building Roof Type Recognition from UAV High-Resolution Images: A Case Study in Hunan Province, ChinaRemote Sensing10.3390/rs1402026514:2(265)Online publication date: 7-Jan-2022
https://doi.org/10.3390/rs14020265
Show More Cited By

Index Terms

Building Extraction Using Mask Scoring R-CNN Network
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Image processing

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cover image ACM Other conferences

CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering

October 2019

942 pages

ISBN:9781450362948

DOI:10.1145/3331453

Conference Chair:
Ali Emrouznejad,
Program Chair:
Zeshui Xu

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 October 2019

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CSAE 2019

CSAE 2019: The 3rd International Conference on Computer Science and Application Engineering

October 22 - 24, 2019

Sanya, China

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Overall Acceptance Rate 368 of 770 submissions, 48%

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Cited By

Li XYang YSun CFan Y(2024)Investigation, Evaluation, and Dynamic Monitoring of Traditional Chinese Village Buildings Based on Unmanned Aerial Vehicle Images and Deep Learning MethodsSustainability10.3390/su1620895416:20(8954)Online publication date: 16-Oct-2024
https://doi.org/10.3390/su16208954
Gella GWendt LLang STiede DHofer BGao YBraun A(2022)Mapping of Dwellings in IDP/Refugee Settlements from Very High-Resolution Satellite Imagery Using a Mask Region-Based Convolutional Neural NetworkRemote Sensing10.3390/rs1403068914:3(689)Online publication date: 1-Feb-2022
https://doi.org/10.3390/rs14030689
Wang YLi STeng FLin YWang MCai H(2022)Improved Mask R-CNN for Rural Building Roof Type Recognition from UAV High-Resolution Images: A Case Study in Hunan Province, ChinaRemote Sensing10.3390/rs1402026514:2(265)Online publication date: 7-Jan-2022
https://doi.org/10.3390/rs14020265
Xia LZhang XZhang JYang HChen T(2021)Building Extraction from Very-High-Resolution Remote Sensing Images Using Semi-Supervised Semantic Edge DetectionRemote Sensing10.3390/rs1311218713:11(2187)Online publication date: 3-Jun-2021
https://doi.org/10.3390/rs13112187
Kaplan OKaplan G(2021)Response Spectra-Based Post-Earthquake Rapid Structural Damage Estimation Approach Aided with Remote Sensing Data: 2020 Samos EarthquakeBuildings10.3390/buildings1201001412:1(14)Online publication date: 26-Dec-2021
https://doi.org/10.3390/buildings12010014
Ji CTang H(2020)Number of Building Stories Estimation from Monocular Satellite Image Using a Modified Mask R-CNNRemote Sensing10.3390/rs1222383312:22(3833)Online publication date: 22-Nov-2020
https://doi.org/10.3390/rs12223833
Ocer NKaplan GErdem FKucuk Matci DAvdan U(2020)Tree extraction from multi-scale UAV images using Mask R-CNN with FPNRemote Sensing Letters10.1080/2150704X.2020.178449111:9(847-856)Online publication date: 27-Jun-2020
https://doi.org/10.1080/2150704X.2020.1784491

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