Automatic detection of building typology using deep learning methods on street level images-Reference-Cited by-同舟云学术

Automatic detection of building typology using deep learning methods on street level images

Published:2020-06 Issue: Volume:177 Page:106805
ISSN:0360-1323
Container-title:Building and Environment
language:en
Short-container-title:Building and Environment

Author:

Gonzalez Daniela,Rueda-Plata Diego^ORCID,Acevedo Ana B.^ORCID,Duque Juan C.^ORCID,Ramos-Pollán Raúl,Betancourt Alejandro,García Sebastian

Funder

UKRI's Global Challenge Research Fund

EAFIT University

Publisher

Elsevier BV

Subject

Building and Construction,Geography, Planning and Development,Civil and Structural Engineering,Environmental Engineering

Reference60 articles.

1. Hazus–mh Mr5, Multi-Hazard Loss Estimation Methodology–Earthquake Model,2010

2. Inventory development for natural and built environments-remote sensing technologies for inventory development and risk assessment-characteristics of satellite images in bangkok, Thailand;Yamazaki;Tech. Rep.,2000

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