Cross-Modal Learning of Housing Quality in Amsterdam

Alex Levering; Diego Marcos; Ilan Havinga; Devis Tuia

doi:10.1145/3486635.3491067

Cross-Modal Learning of Housing Quality in Amsterdam

Alex Levering, Diego Marcos, Ilan Havinga, Devis Tuia

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

Abstract

In our research we test data and models for the recognition of housing quality in the city of Amsterdam from ground-level and aerial imagery. For ground-level images we compare Google StreetView (GSV) to Flickr images. Our results show that GSV predicts the most accurate building quality scores, approximately 30% better than using only aerial images. However, we find that through careful filtering and by using the right pre-trained model, Flickr image features combined with aerial image features are able to halve the performance gap to GSV features from 30% to 15%. Our results indicate that there are viable alternatives to GSV for liveability factor prediction, which is encouraging as GSV images are more difficult to acquire and not always available.

Original language	English
Title of host publication	GEOAI '21
Subtitle of host publication	Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery
Editors	Dalton Lunga, Lexie Yang, Song Gao, Bruno Martins, Yingjie Hu, Xueqing Deng, Shawn Newsam
Publisher	Association for Computing Machinery, Inc
Number of pages	4
ISBN (Electronic)	9781450391207
DOIs	https://doi.org/10.1145/3486635.3491067
Publication status	Published - 2021
Externally published	Yes
Event	4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021 - Beijing, China Duration: 2 Nov 2021 → 2 Nov 2021

Conference

Conference	4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021
Country/Territory	China
City	Beijing
Period	2/11/21 → 2/11/21

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10.1145/3486635.3491067

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Levering, A., Marcos, D., Havinga, I., & Tuia, D. (2021). Cross-Modal Learning of Housing Quality in Amsterdam. In D. Lunga, L. Yang, S. Gao, B. Martins, Y. Hu, X. Deng, & S. Newsam (Eds.), GEOAI '21: Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery Association for Computing Machinery, Inc. https://doi.org/10.1145/3486635.3491067

Levering, Alex ; Marcos, Diego ; Havinga, Ilan et al. / Cross-Modal Learning of Housing Quality in Amsterdam. GEOAI '21: Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery. editor / Dalton Lunga ; Lexie Yang ; Song Gao ; Bruno Martins ; Yingjie Hu ; Xueqing Deng ; Shawn Newsam. Association for Computing Machinery, Inc, 2021.

@inproceedings{c61bbdf8f12844539d6381dfaf38908d,

title = "Cross-Modal Learning of Housing Quality in Amsterdam",

abstract = "In our research we test data and models for the recognition of housing quality in the city of Amsterdam from ground-level and aerial imagery. For ground-level images we compare Google StreetView (GSV) to Flickr images. Our results show that GSV predicts the most accurate building quality scores, approximately 30\% better than using only aerial images. However, we find that through careful filtering and by using the right pre-trained model, Flickr image features combined with aerial image features are able to halve the performance gap to GSV features from 30\% to 15\%. Our results indicate that there are viable alternatives to GSV for liveability factor prediction, which is encouraging as GSV images are more difficult to acquire and not always available.",

author = "Alex Levering and Diego Marcos and Ilan Havinga and Devis Tuia",

year = "2021",

doi = "10.1145/3486635.3491067",

language = "English",

editor = "Dalton Lunga and Lexie Yang and Song Gao and Bruno Martins and Yingjie Hu and Xueqing Deng and Shawn Newsam",

booktitle = "GEOAI '21",

publisher = "Association for Computing Machinery, Inc",

note = "4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021 ; Conference date: 02-11-2021 Through 02-11-2021",

}

Levering, A, Marcos, D, Havinga, I & Tuia, D 2021, Cross-Modal Learning of Housing Quality in Amsterdam. in D Lunga, L Yang, S Gao, B Martins, Y Hu, X Deng & S Newsam (eds), GEOAI '21: Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery. Association for Computing Machinery, Inc, 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021, Beijing, China, 2/11/21. https://doi.org/10.1145/3486635.3491067

Cross-Modal Learning of Housing Quality in Amsterdam. / Levering, Alex; Marcos, Diego; Havinga, Ilan et al.
GEOAI '21: Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery. ed. / Dalton Lunga; Lexie Yang; Song Gao; Bruno Martins; Yingjie Hu; Xueqing Deng; Shawn Newsam. Association for Computing Machinery, Inc, 2021.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Cross-Modal Learning of Housing Quality in Amsterdam

AU - Levering, Alex

AU - Marcos, Diego

AU - Havinga, Ilan

AU - Tuia, Devis

PY - 2021

Y1 - 2021

N2 - In our research we test data and models for the recognition of housing quality in the city of Amsterdam from ground-level and aerial imagery. For ground-level images we compare Google StreetView (GSV) to Flickr images. Our results show that GSV predicts the most accurate building quality scores, approximately 30% better than using only aerial images. However, we find that through careful filtering and by using the right pre-trained model, Flickr image features combined with aerial image features are able to halve the performance gap to GSV features from 30% to 15%. Our results indicate that there are viable alternatives to GSV for liveability factor prediction, which is encouraging as GSV images are more difficult to acquire and not always available.

AB - In our research we test data and models for the recognition of housing quality in the city of Amsterdam from ground-level and aerial imagery. For ground-level images we compare Google StreetView (GSV) to Flickr images. Our results show that GSV predicts the most accurate building quality scores, approximately 30% better than using only aerial images. However, we find that through careful filtering and by using the right pre-trained model, Flickr image features combined with aerial image features are able to halve the performance gap to GSV features from 30% to 15%. Our results indicate that there are viable alternatives to GSV for liveability factor prediction, which is encouraging as GSV images are more difficult to acquire and not always available.

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DO - 10.1145/3486635.3491067

M3 - Conference contribution

BT - GEOAI '21

A2 - Lunga, Dalton

A2 - Yang, Lexie

A2 - Gao, Song

A2 - Martins, Bruno

A2 - Hu, Yingjie

A2 - Deng, Xueqing

A2 - Newsam, Shawn

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Y2 - 2 November 2021 through 2 November 2021

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Levering A, Marcos D, Havinga I, Tuia D. Cross-Modal Learning of Housing Quality in Amsterdam. In Lunga D, Yang L, Gao S, Martins B, Hu Y, Deng X, Newsam S, editors, GEOAI '21: Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery. Association for Computing Machinery, Inc. 2021 Epub 2021 Nov 8. doi: 10.1145/3486635.3491067

Cross-Modal Learning of Housing Quality in Amsterdam

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