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 multilabel 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.
|Title of host publication||Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery|
|Place of Publication||New York|
|Publication status||Published - 6 Nov 2018|
|Event||2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery - Seattle, United States|
Duration: 6 Nov 2018 → 6 Nov 2018
|Conference||2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery|
|Period||6/11/18 → 6/11/18|