FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4382468265> ?p ?o ?g. }

• W4382468265 endingPage "e0286340" @default.
• W4382468265 startingPage "e0286340" @default.
• W4382468265 abstract "Sanborn Fire Insurance maps contain a wealth of building-level information about U.S. cities dating back to the late 19th century. They are a valuable resource for studying changes in urban environments, such as the legacy of urban highway construction and urban renewal in the 20th century. However, it is a challenge to automatically extract the building-level information effectively and efficiently from Sanborn maps because of the large number of map entities and the lack of appropriate computational methods to detect these entities. This paper contributes to a scalable workflow that utilizes machine learning to identify building footprints and associated properties on Sanborn maps. This information can be effectively applied to create 3D visualization of historic urban neighborhoods and inform urban changes. We demonstrate our methods using Sanborn maps for two neighborhoods in Columbus, Ohio, USA that were bisected by highway construction in the 1960s. Quantitative and visual analysis of the results suggest high accuracy of the extracted building-level information, with an F-1 score of 0.9 for building footprints and construction materials, and over 0.7 for building utilizations and numbers of stories. We also illustrate how to visualize pre-highway neighborhoods." @default.
• W4382468265 created "2023-06-29" @default.
• W4382468265 creator A5007408091 @default.
• W4382468265 creator A5009887094 @default.
• W4382468265 creator A5017921952 @default.
• W4382468265 creator A5037680248 @default.
• W4382468265 creator A5041181545 @default.
• W4382468265 creator A5092354967 @default.
• W4382468265 date "2023-06-28" @default.
• W4382468265 modified "2023-09-27" @default.
• W4382468265 title "Creating building-level, three-dimensional digital models of historic urban neighborhoods from Sanborn Fire Insurance maps using machine learning" @default.
• W4382468265 cites W1508744654 @default.
• W4382468265 cites W1754785601 @default.
• W4382468265 cites W1902370489 @default.
• W4382468265 cites W1955935530 @default.
• W4382468265 cites W1977256284 @default.
• W4382468265 cites W1990827006 @default.
• W4382468265 cites W2002533076 @default.
• W4382468265 cites W2005082088 @default.
• W4382468265 cites W2006337704 @default.
• W4382468265 cites W2027311243 @default.
• W4382468265 cites W2031417683 @default.
• W4382468265 cites W2031489346 @default.
• W4382468265 cites W2031565657 @default.
• W4382468265 cites W2040715327 @default.
• W4382468265 cites W2044561307 @default.
• W4382468265 cites W2063907334 @default.
• W4382468265 cites W2064592066 @default.
• W4382468265 cites W2065800647 @default.
• W4382468265 cites W2068076851 @default.
• W4382468265 cites W2082543462 @default.
• W4382468265 cites W2091527089 @default.
• W4382468265 cites W2093393329 @default.
• W4382468265 cites W2103018059 @default.
• W4382468265 cites W2107165817 @default.
• W4382468265 cites W2145023731 @default.
• W4382468265 cites W2154741421 @default.
• W4382468265 cites W2195043538 @default.
• W4382468265 cites W2232154276 @default.
• W4382468265 cites W2321380919 @default.
• W4382468265 cites W2465437403 @default.
• W4382468265 cites W2549822364 @default.
• W4382468265 cites W2569848719 @default.
• W4382468265 cites W2604192718 @default.
• W4382468265 cites W2605982830 @default.
• W4382468265 cites W2784280327 @default.
• W4382468265 cites W2794301933 @default.
• W4382468265 cites W2891211998 @default.
• W4382468265 cites W2944451185 @default.
• W4382468265 cites W2947376266 @default.
• W4382468265 cites W2966101452 @default.
• W4382468265 cites W2967615747 @default.
• W4382468265 cites W2981342983 @default.
• W4382468265 cites W2991566686 @default.
• W4382468265 cites W2995929575 @default.
• W4382468265 cites W2998917644 @default.
• W4382468265 cites W3021667312 @default.
• W4382468265 cites W3047237145 @default.
• W4382468265 cites W3087067468 @default.
• W4382468265 cites W3099577122 @default.
• W4382468265 cites W3124842273 @default.
• W4382468265 cites W3126474228 @default.
• W4382468265 cites W3135384265 @default.
• W4382468265 cites W3183557592 @default.
• W4382468265 cites W3187493418 @default.
• W4382468265 cites W3189927302 @default.
• W4382468265 cites W3196584577 @default.
• W4382468265 cites W3208034772 @default.
• W4382468265 cites W4214743377 @default.
• W4382468265 cites W4220722573 @default.
• W4382468265 cites W4221092554 @default.
• W4382468265 cites W4239510810 @default.
• W4382468265 cites W4240298598 @default.
• W4382468265 cites W4242322188 @default.
• W4382468265 cites W4293776744 @default.
• W4382468265 cites W4294050173 @default.
• W4382468265 cites W4294300421 @default.
• W4382468265 doi "https://doi.org/10.1371/journal.pone.0286340" @default.
• W4382468265 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37379319" @default.
• W4382468265 hasPublicationYear "2023" @default.
• W4382468265 type Work @default.
• W4382468265 citedByCount "0" @default.
• W4382468265 crossrefType "journal-article" @default.
• W4382468265 hasAuthorship W4382468265A5007408091 @default.
• W4382468265 hasAuthorship W4382468265A5009887094 @default.
• W4382468265 hasAuthorship W4382468265A5017921952 @default.
• W4382468265 hasAuthorship W4382468265A5037680248 @default.
• W4382468265 hasAuthorship W4382468265A5041181545 @default.
• W4382468265 hasAuthorship W4382468265A5092354967 @default.
• W4382468265 hasBestOaLocation W43824682651 @default.
• W4382468265 hasConcept C124101348 @default.
• W4382468265 hasConcept C127413603 @default.
• W4382468265 hasConcept C147176958 @default.
• W4382468265 hasConcept C170154142 @default.
• W4382468265 hasConcept C177212765 @default.
• W4382468265 hasConcept C205649164 @default.
• W4382468265 hasConcept C206345919 @default.
• W4382468265 hasConcept C2522767166 @default.

• next