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 paper › 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	Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021
Editors	Dalton Lunga, Lexie Yang, Song Gao, Bruno Martins, Yingjie Hu, Xueqing Deng, Shawn Newsam
Publisher	Association for Computing Machinery (ACM)
Pages	1-4
ISBN (Electronic)	9781450391207
DOIs	https://doi.org/10.1145/3486635.3491067
Publication status	Published - 2 Nov 2021
Event	4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021 - Beijing, China Duration: 2 Nov 2021 → 2 Nov 2021

Publication series

Name	Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021

Conference/symposium

Conference/symposium	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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

deep learning
housing
liveability
remote sensing
urban

Access to Document

10.1145/3486635.3491067

Cite this

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.), Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021 (pp. 1-4). (Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021). Association for Computing Machinery (ACM). https://doi.org/10.1145/3486635.3491067

Levering, Alex ; Marcos, Diego ; Havinga, Ilan et al. / Cross-Modal Learning of Housing Quality in Amsterdam. Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021. editor / Dalton Lunga ; Lexie Yang ; Song Gao ; Bruno Martins ; Yingjie Hu ; Xueqing Deng ; Shawn Newsam. Association for Computing Machinery (ACM), 2021. pp. 1-4 (Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021).

@inproceedings{cbb38a2865e542e28d34e5f47177c51b,

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. ",

keywords = "deep learning, housing, liveability, remote sensing, urban",

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

year = "2021",

month = nov,

day = "2",

doi = "10.1145/3486635.3491067",

language = "English",

series = "Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021",

publisher = "Association for Computing Machinery (ACM)",

pages = "1--4",

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

booktitle = "Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021",

address = "United States",

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

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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), Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021. Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021, Association for Computing Machinery (ACM), pp. 1-4, 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.
Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021. ed. / Dalton Lunga; Lexie Yang; Song Gao; Bruno Martins; Yingjie Hu; Xueqing Deng; Shawn Newsam. Association for Computing Machinery (ACM), 2021. p. 1-4 (Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › 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 - 2021/11/2

Y1 - 2021/11/2

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.

KW - deep learning

KW - housing

KW - liveability

KW - remote sensing

KW - urban

U2 - 10.1145/3486635.3491067

DO - 10.1145/3486635.3491067

M3 - Conference paper

AN - SCOPUS:85119498139

T3 - Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021

SP - 1

EP - 4

BT - Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021

A2 - Lunga, Dalton

A2 - Yang, Lexie

A2 - Gao, Song

A2 - Martins, Bruno

A2 - Hu, Yingjie

A2 - Deng, Xueqing

A2 - Newsam, Shawn

PB - Association for Computing Machinery (ACM)

T2 - 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021

Y2 - 2 November 2021 through 2 November 2021

ER -

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, Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021. Association for Computing Machinery (ACM). 2021. p. 1-4. (Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021). doi: 10.1145/3486635.3491067

Cross-Modal Learning of Housing Quality in Amsterdam

Abstract

Publication series

Conference/symposium

UN SDGs

Keywords

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