Decoding Ethiopian Abodes: Towards Classifying Buildings by Occupancy Type Using Footprint Morphology

Daniel S. Adams; Taylor Hauser; Jessica Moehl

doi:10.1109/ICMLA58977.2023.00037

Decoding Ethiopian Abodes: Towards Classifying Buildings by Occupancy Type Using Footprint Morphology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

Building occupancy classification plays a crucial role in urban planning, disaster management, and population modeling. Traditional methods often require extensive field surveys or detailed datasets, which can be time-consuming, expensive, and may yield incomplete or erroneous data. In this paper, we present a novel approach for classifying buildings as residential or non-residential using only building footprint data. By extracting geometric shape derivatives that characterize building morphology, we developed a high-accuracy classification model employing a combination of unsupervised and supervised learning methods. We utilized open-source data from Open Street Map, aggregating it to create binary labels for buildings based on their respective human use type. Our approach demonstrates the potential for scalability without the need for additional data sources other than building footprints and labels, offering a more efficient solution for building occupancy classification.

Original language	English
Title of host publication	Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023
Editors	M. Arif Wani, Mihai Boicu, Moamar Sayed-Mouchaweh, Pedro Henriques Abreu, Joao Gama
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	210-217
Number of pages	8
ISBN (Electronic)	9798350345346
DOIs	https://doi.org/10.1109/ICMLA58977.2023.00037
State	Published - 2023
Event	22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023 - Jacksonville, United States Duration: Dec 15 2023 → Dec 17 2023

Publication series

Name	Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

Conference

Conference	22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023
Country/Territory	United States
City	Jacksonville
Period	12/15/23 → 12/17/23

Funding

This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-publicaccess- plan). We thank our esteemed colleagues Dr. Philipe Ambrozio, Clinton Stipek, Marie Urban at Oak Ridge National Laboratory, and Dr. Peter Li at Tennessee Technological University for fruitful conversations and manuscript guidance.

Keywords

Anomaly Detection
Building Morphology
Occupancy Type
VGI
XG-Boost

Access to Document

10.1109/ICMLA58977.2023.00037

Cite this

Adams, D. S., Hauser, T., & Moehl, J. (2023). Decoding Ethiopian Abodes: Towards Classifying Buildings by Occupancy Type Using Footprint Morphology. In M. Arif Wani, M. Boicu, M. Sayed-Mouchaweh, P. H. Abreu, & J. Gama (Eds.), Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023 (pp. 210-217). (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA58977.2023.00037

Adams, Daniel S. ; Hauser, Taylor ; Moehl, Jessica. / Decoding Ethiopian Abodes : Towards Classifying Buildings by Occupancy Type Using Footprint Morphology. Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023. editor / M. Arif Wani ; Mihai Boicu ; Moamar Sayed-Mouchaweh ; Pedro Henriques Abreu ; Joao Gama. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 210-217 (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023).

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title = "Decoding Ethiopian Abodes: Towards Classifying Buildings by Occupancy Type Using Footprint Morphology",

abstract = "Building occupancy classification plays a crucial role in urban planning, disaster management, and population modeling. Traditional methods often require extensive field surveys or detailed datasets, which can be time-consuming, expensive, and may yield incomplete or erroneous data. In this paper, we present a novel approach for classifying buildings as residential or non-residential using only building footprint data. By extracting geometric shape derivatives that characterize building morphology, we developed a high-accuracy classification model employing a combination of unsupervised and supervised learning methods. We utilized open-source data from Open Street Map, aggregating it to create binary labels for buildings based on their respective human use type. Our approach demonstrates the potential for scalability without the need for additional data sources other than building footprints and labels, offering a more efficient solution for building occupancy classification.",

keywords = "Anomaly Detection, Building Morphology, Occupancy Type, VGI, XG-Boost",

author = "Adams, \{Daniel S.\} and Taylor Hauser and Jessica Moehl",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023 ; Conference date: 15-12-2023 Through 17-12-2023",

year = "2023",

doi = "10.1109/ICMLA58977.2023.00037",

language = "English",

series = "Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023",

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Adams, DS , Hauser, T & Moehl, J 2023, Decoding Ethiopian Abodes: Towards Classifying Buildings by Occupancy Type Using Footprint Morphology. in M Arif Wani, M Boicu, M Sayed-Mouchaweh, PH Abreu & J Gama (eds), Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023. Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023, Institute of Electrical and Electronics Engineers Inc., pp. 210-217, 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023, Jacksonville, United States, 12/15/23. https://doi.org/10.1109/ICMLA58977.2023.00037

Decoding Ethiopian Abodes: Towards Classifying Buildings by Occupancy Type Using Footprint Morphology. / Adams, Daniel S.; Hauser, Taylor ; Moehl, Jessica.
Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023. ed. / M. Arif Wani; Mihai Boicu; Moamar Sayed-Mouchaweh; Pedro Henriques Abreu; Joao Gama. Institute of Electrical and Electronics Engineers Inc., 2023. p. 210-217 (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Decoding Ethiopian Abodes

T2 - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

AU - Adams, Daniel S.

AU - Hauser, Taylor

AU - Moehl, Jessica

PY - 2023

Y1 - 2023

N2 - Building occupancy classification plays a crucial role in urban planning, disaster management, and population modeling. Traditional methods often require extensive field surveys or detailed datasets, which can be time-consuming, expensive, and may yield incomplete or erroneous data. In this paper, we present a novel approach for classifying buildings as residential or non-residential using only building footprint data. By extracting geometric shape derivatives that characterize building morphology, we developed a high-accuracy classification model employing a combination of unsupervised and supervised learning methods. We utilized open-source data from Open Street Map, aggregating it to create binary labels for buildings based on their respective human use type. Our approach demonstrates the potential for scalability without the need for additional data sources other than building footprints and labels, offering a more efficient solution for building occupancy classification.

AB - Building occupancy classification plays a crucial role in urban planning, disaster management, and population modeling. Traditional methods often require extensive field surveys or detailed datasets, which can be time-consuming, expensive, and may yield incomplete or erroneous data. In this paper, we present a novel approach for classifying buildings as residential or non-residential using only building footprint data. By extracting geometric shape derivatives that characterize building morphology, we developed a high-accuracy classification model employing a combination of unsupervised and supervised learning methods. We utilized open-source data from Open Street Map, aggregating it to create binary labels for buildings based on their respective human use type. Our approach demonstrates the potential for scalability without the need for additional data sources other than building footprints and labels, offering a more efficient solution for building occupancy classification.

KW - Anomaly Detection

KW - Building Morphology

KW - Occupancy Type

KW - VGI

KW - XG-Boost

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M3 - Conference contribution

AN - SCOPUS:85190102850

T3 - Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

SP - 210

EP - 217

BT - Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

A2 - Arif Wani, M.

A2 - Boicu, Mihai

A2 - Sayed-Mouchaweh, Moamar

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A2 - Gama, Joao

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 15 December 2023 through 17 December 2023

ER -

Adams DS , Hauser T , Moehl J. Decoding Ethiopian Abodes: Towards Classifying Buildings by Occupancy Type Using Footprint Morphology. In Arif Wani M, Boicu M, Sayed-Mouchaweh M, Abreu PH, Gama J, editors, Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 210-217. (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023). doi: 10.1109/ICMLA58977.2023.00037

Decoding Ethiopian Abodes: Towards Classifying Buildings by Occupancy Type Using Footprint Morphology

Abstract

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