Feature extraction and machine learning techniques for identifying historic urban environmental hazards: New methods to locate lost fossil fuel infrastructure in US cities.

U.S. cities contain unknown numbers of undocumented "manufactured gas" sites, legacies of an industry that dominated energy production during the late-19th and early-20th centuries. While many of these unidentified sites likely contain significant levels of highly toxic and biologically pe...

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Autores principales:	Jonathan Tollefson, Scott Frickel, Maria I Restrepo
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7180d7a31a5d4c158fce13485563bf8f
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id	oai:doaj.org-article:7180d7a31a5d4c158fce13485563bf8f
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spelling	oai:doaj.org-article:7180d7a31a5d4c158fce13485563bf8f2021-12-02T20:18:43ZFeature extraction and machine learning techniques for identifying historic urban environmental hazards: New methods to locate lost fossil fuel infrastructure in US cities.1932-620310.1371/journal.pone.0255507https://doaj.org/article/7180d7a31a5d4c158fce13485563bf8f2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255507https://doaj.org/toc/1932-6203U.S. cities contain unknown numbers of undocumented "manufactured gas" sites, legacies of an industry that dominated energy production during the late-19th and early-20th centuries. While many of these unidentified sites likely contain significant levels of highly toxic and biologically persistent contamination, locating them remains a significant challenge. We propose a new method to identify manufactured gas production, storage, and distribution infrastructure in bulk by applying feature extraction and machine learning techniques to digitized historic Sanborn fire insurance maps. Our approach, which relies on a two-part neural network to classify candidate map regions, increases the rate of site identification 20-fold compared to unaided visual coding.Jonathan TollefsonScott FrickelMaria I RestrepoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255507 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jonathan Tollefson Scott Frickel Maria I Restrepo Feature extraction and machine learning techniques for identifying historic urban environmental hazards: New methods to locate lost fossil fuel infrastructure in US cities.
description	U.S. cities contain unknown numbers of undocumented "manufactured gas" sites, legacies of an industry that dominated energy production during the late-19th and early-20th centuries. While many of these unidentified sites likely contain significant levels of highly toxic and biologically persistent contamination, locating them remains a significant challenge. We propose a new method to identify manufactured gas production, storage, and distribution infrastructure in bulk by applying feature extraction and machine learning techniques to digitized historic Sanborn fire insurance maps. Our approach, which relies on a two-part neural network to classify candidate map regions, increases the rate of site identification 20-fold compared to unaided visual coding.
format	article
author	Jonathan Tollefson Scott Frickel Maria I Restrepo
author_facet	Jonathan Tollefson Scott Frickel Maria I Restrepo
author_sort	Jonathan Tollefson
title	Feature extraction and machine learning techniques for identifying historic urban environmental hazards: New methods to locate lost fossil fuel infrastructure in US cities.
title_short	Feature extraction and machine learning techniques for identifying historic urban environmental hazards: New methods to locate lost fossil fuel infrastructure in US cities.
title_full	Feature extraction and machine learning techniques for identifying historic urban environmental hazards: New methods to locate lost fossil fuel infrastructure in US cities.
title_fullStr	Feature extraction and machine learning techniques for identifying historic urban environmental hazards: New methods to locate lost fossil fuel infrastructure in US cities.
title_full_unstemmed	Feature extraction and machine learning techniques for identifying historic urban environmental hazards: New methods to locate lost fossil fuel infrastructure in US cities.
title_sort	feature extraction and machine learning techniques for identifying historic urban environmental hazards: new methods to locate lost fossil fuel infrastructure in us cities.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/7180d7a31a5d4c158fce13485563bf8f
work_keys_str_mv	AT jonathantollefson featureextractionandmachinelearningtechniquesforidentifyinghistoricurbanenvironmentalhazardsnewmethodstolocatelostfossilfuelinfrastructureinuscities AT scottfrickel featureextractionandmachinelearningtechniquesforidentifyinghistoricurbanenvironmentalhazardsnewmethodstolocatelostfossilfuelinfrastructureinuscities AT mariairestrepo featureextractionandmachinelearningtechniquesforidentifyinghistoricurbanenvironmentalhazardsnewmethodstolocatelostfossilfuelinfrastructureinuscities
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Feature extraction and machine learning techniques for identifying historic urban environmental hazards: New methods to locate lost fossil fuel infrastructure in US cities.

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