Multi-label Building Functions Classification from Ground Pictures using Convolutional Neural Networks

Autor(es)

Srivastava, Shivangi ; Vargas-Munoz, John E. ; Swinkels, David ; Tuia, Devis ; Hu, Y ; Gao, S ; Newsam, S ; Lunga, D

• Tipo de documento: Artigo Científico
• Fonte: PROCEEDINGS OF THE 2ND ACM SIGSPATIAL INTERNATIONAL WORKSHOP ON AI FOR GEOGRAPHIC KNOWLEDGE DISCOVERY (GEOAI 2018); v. N/A, p. 4-pg., 2018-01-01.

Resumo

We approach the problem of multi building function classification for buildings from the city of Amsterdam using a collection of Google Street View (GSV) pictures acquired at multiple zoom levels (field of views, FoV) and the corresponding governmental census data per building. Since buildings can have multiple usages, we cast the problem as multi-label classification task. To do so, we trained a CNN model end-to-end with the task of predicting multiple co-occurring building function classes per building. We fuse the individual features of three FoVs by using volumetric stacking. Our proposed model outperforms baseline CNN models that use either single or multiple FoVs. (AU)

• Processo FAPESP: 17/10086-0 - Detecção e delineamento de prédios rurais utilizando imagens de sensoriamento remoto
• Beneficiário: John Edgar Vargas Muñoz
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Doutorado