Journal: IPSI Transactions on Internet Research


Towards Real-time House Detection in Aerial Imagery Using Faster Region-based Convolutional Neural Network

Authors: Ahmed, Khandaker Mamun Ghareh Mohammadi, Farid
Matus, Manuel
Shenavarmasouleh, Farzan Manella Pereira, Luiz

Ioannis, Zisis and Amini, M. Hadi


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Abstract

In the past few years, automatic building detection in aerial images has become an emerging field in computer vision. Detecting the specific types of houses will provide information in urbanization, change detection, and urban monitoring that play increasingly important roles in modern city planning and natural hazard preparedness. In this paper, we demonstrate the effectiveness of detecting various types of houses in aerial imagery using Faster Region-based Convolutional Neural Network (Faster-RCNN). After formulating the dataset and extracting bounding-box information, pre-trained ResNet50 is used to get the feature maps. The fully convolutional Region Proposal Network (RPN) first predicts the bounds and objectness score of objects (in this case house) from the feature maps. Then, the Region of Interest (RoI) pooling layer extracts interested regions to detect objects that are present in the images. To the best of our knowledge, this is the first attempt at detecting houses using Faster R-CNN that has achieved satisfactory results. This experiment opens a new path to conduct and extent the works not only for civil and environmental domain but also other applied science disciplines.


Keywords

RCNN, Neural Network, Deep Learning, Convolution, Mini batch


Published in: IPSI Transaction on Internet Research (Volume: 19, Issue: 2)
Publisher: IPSI, Belgrade

Date of Publication: July 1, 2023

Open Access: CC-BY-NC-ND
DOI: 10.58245/ipsi.tir.2302.06

Pages: 46-54

ISSN: 1820 - 4503



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Ahmed, Khandaker Mamun

Knight Foundation School of Computing and Information Sciences, Florida International University (FIU), Miami, FL, USA; and the Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab), FIU, Miami, FL, USA.
Orcid ID: 0000-0002-4713-188X

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Ghareh Mohammadi, Farid

Department of Radiology, Center for Augmented Intelligence (CAI), Mayo Clinic, Jacksonville, FL, USA.

× Matus, Manuel

Dept. of Civil & Environ. Engineering Florida International University, Miami, FL, USA.
Orcid ID: 0000-0003-0307-6732

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Shenavarmasouleh, Farzan

R&D Department, MediaLab Inc., GA, USA.
Orcid ID: 0000-0001-5485-232X

× Manella Pereira, Luiz

Knight Foundation School of Computing and Information Sciences, Florida International University (FIU), Miami, FL, USA; and the Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab), FIU, Miami, FL, USA.

× Ioannis, Zisis

Dept. of Civil & Environ. Engineering Florida International University, Miami, FL, USA.

× Amini, M. Hadi

Knight Foundation School of Computing and Information Sciences, Florida International University (FIU), Miami, FL, USA; and the Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab), FIU, Miami, FL, USA.
Corresponding Author: e-mail: moamini@fiu.edu; Orcid ID: 0000-0002-2768-3601

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Cite this article

Ahmed, Khandaker Mamun; Ghareh Mohammadi, Farid; Matus, Manuel; Shenavarmasouleh, Farzan; Manella Pereira, Luiz; Ioannis, Zisis; and Amini, M. Hadi "Towards Real-time House Detection in Aerial Imagery Using Faster Region-based Convolutional Neural Network ", IPSI Transactions on Internet Research, vol. 19(2), pp. 46-54, 2023. https://doi.org/10.58245/ipsi.tir.2302.06