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Titel:

Estimating Building Age with 3D GIS

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
Biljecki, Filip; Sindram, Maximilian
Seitenangaben Beitrag:
17-24
Abstract:
Building datasets (e.g. footprints in OpenStreetMap and 3D city models) are becoming increasingly available worldwide. However, the thematic (attribute) aspect is not always given attention, as many of such datasets are lacking in completeness of attributes. A prominent attribute of buildings is the year of construction, which is useful for some applications, but its availability may be scarce. This paper explores the potential of estimating the year of construction (or age) of buildings from ot...     »
Stichworte:
3D city models; building age; year of construction; CityGML; machine learning; random forest regression; GISPro_SSD; GISTop_CityModeling; GISTop_SpatialModelingAndAlgorithms; LOCenter; LOCTop_Urban_Information_Modeling_Virtual_3D_City_Model
Herausgeber:
Kalantari, Mohsen; Rajabifard, Abbas
Kongress- / Buchtitel:
Proceedings of the 12th International 3D GeoInfo Conference 2017
Band / Teilband / Volume:
IV-4/W5
Ausrichter der Konferenz:
University of Melbourne
Verlag / Institution:
ISPRS
Publikationsdatum:
26.10.2017
Jahr:
2017
Nachgewiesen in:
Scopus; Web of Science
Serientitel:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.5194/isprs-annals-IV-4-W5-17-2017
WWW:
https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-4-W5/17/2017/
Hinweise:
This paper received the Best Paper Award.
TUM Einrichtung:
Lehrstuhl für Geoinformatik
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