2022
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Suel E, Sorek-Hamer M, Moise I, von Pohle M, Sahasrabhojanee A, Asanjan A, Arku RE, Alli AS, Barratt B, Clark SN, Middel A, Deardorff E, Lingenfelter V, Oza N, Yadav N, Ezzati M, Brauer M Predicting air pollution spatial variation with street-level imagery Remote Sensing, 14 (14), pp. 3429, 2022. (Big Data) Big Data @article{E2022,
title = {Predicting air pollution spatial variation with street-level imagery},
author = {Suel E and Sorek-Hamer M and Moise I and von Pohle M and Sahasrabhojanee A and Asanjan A and Arku RE and Alli AS and Barratt B and Clark SN and Middel A and Deardorff E and Lingenfelter V and Oza N and Yadav N and Ezzati M and Brauer M},
doi = {https://doi.org/10.3390/rs14143429},
year = {2022},
date = {2022-07-17},
journal = {Remote Sensing},
volume = {14},
number = {14},
pages = {3429},
keywords = {Big Data},
pubstate = {published},
tppubtype = {article}
}
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Sorek-Hamer M, von Pohle M, Asanjan A, Sahasrabhojanee A, Deardorff E, Lingenfelter V, Suel E, Das K, Oza N, Ezzati M, Matthews B, Brauer M A deep learning approach for meter-scale air quality estimation in urban environments using very high-spatial-resolution satellite imagery Atmosphere, 13 (5), pp. 696, 2022. (Big Data) Big Data @article{M2022,
title = {A deep learning approach for meter-scale air quality estimation in urban environments using very high-spatial-resolution satellite imagery},
author = {Sorek-Hamer M and von Pohle M and Asanjan A and Sahasrabhojanee A and Deardorff E and Lingenfelter V and Suel E and Das K and Oza N and Ezzati M and Matthews B and Brauer M},
doi = {https://doi.org/10.3390/atmos13050696},
year = {2022},
date = {2022-04-27},
journal = {Atmosphere},
volume = {13},
number = {5},
pages = {696},
keywords = {Big Data},
pubstate = {published},
tppubtype = {article}
}
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2021
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Suel E, Bhatt S, Brauer M, Flaxman S, Ezzati M Multimodal deep learning from satellite and street-level imagery for measuring income, overcrowding, and environmental deprivation in urban areas Remote Sensing of Environment, 257 , pp. 112339, 2021. (Big Data, London, Poverty and Inequality) Big Data, London, Poverty and Inequality @article{E2021,
title = {Multimodal deep learning from satellite and street-level imagery for measuring income, overcrowding, and environmental deprivation in urban areas},
author = {Suel E and Bhatt S and Brauer M and Flaxman S and Ezzati M},
doi = {10.1016/j.rse.2021.112339},
year = {2021},
date = {2021-05-21},
journal = {Remote Sensing of Environment},
volume = {257},
pages = {112339},
keywords = {Big Data, London, Poverty and Inequality},
pubstate = {published},
tppubtype = {article}
}
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2020
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Suel E, Sorek-Hamer M, Moise I, von Pohle M, Sahasrabhojanee A, Asanjan A, Deardorff E, Lingenfelter V, Oza N, Ezzati M, Brauer M Predicting air pollution spatial variation with street-level imagery Machine Learning in Public Health (MLPH) Workshop, 34th Conference on Neural Information Processing Systems (NeurIPS 2020) 2020. (Big Data) Big Data @proceedings{E2020,
title = {Predicting air pollution spatial variation with street-level imagery},
author = {Suel E and Sorek-Hamer M and Moise I and von Pohle M and Sahasrabhojanee A and Asanjan A and Deardorff E and Lingenfelter V and Oza N and Ezzati M and Brauer M },
url = {https://spiral.imperial.ac.uk/bitstream/10044/1/88352/4/slevel_air_pollution_neurips.pdf},
year = {2020},
date = {2020-12-12},
organization = {Machine Learning in Public Health (MLPH) Workshop, 34th Conference on Neural Information Processing Systems (NeurIPS 2020)},
keywords = {Big Data},
pubstate = {published},
tppubtype = {proceedings}
}
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Li F, Li F, Li S, Long Y Deciphering the recreational use of urban parks: Experiments using multi-source big data for all Chinese cities Science of the Total Environment, 701 (1), pp. 134896, 2020. (Big Data, Housing and neighbourhood, Related publication) Big Data, Housing and neighbourhood, Related publication @article{F2019b,
title = {Deciphering the recreational use of urban parks: Experiments using multi-source big data for all Chinese cities},
author = {Li F and Li F and Li S and Long Y},
doi = {10.1016/j.scitotenv.2019.134896},
year = {2020},
date = {2020-01-20},
journal = {Science of the Total Environment},
volume = {701},
number = {1},
pages = {134896},
keywords = {Big Data, Housing and neighbourhood, Related publication},
pubstate = {published},
tppubtype = {article}
}
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2019
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Zhang ZX, Long Y Application of Wearable Cameras in Studying Individual Behaviors in Built Environments Landscape Architecture Frontiers, 7 (2), pp. 22-37, 2019. (Big Data, Housing and neighbourhood, Related publication) Big Data, Housing and neighbourhood, Related publication @article{ZX2019,
title = {Application of Wearable Cameras in Studying Individual Behaviors in Built Environments},
author = {Zhang ZX and Long Y},
doi = {10.15302/J-LAF-20190203},
year = {2019},
date = {2019-05-14},
journal = {Landscape Architecture Frontiers},
volume = {7},
number = {2},
pages = {22-37},
keywords = {Big Data, Housing and neighbourhood, Related publication},
pubstate = {published},
tppubtype = {article}
}
|
Suel E, Polak JW, Bennett JE, Ezzati M Measuring social, environmental and health inequalities using deep learning and street imagery Scientific Reports, 9 , pp. 6229, 2019. (Big Data) Big Data @article{E2019,
title = {Measuring social, environmental and health inequalities using deep learning and street imagery},
author = {Suel E and Polak JW and Bennett JE and Ezzati M},
url = {https://www.nature.com/articles/s41598-019-42036-w},
doi = {10.1038/s41598-019-42036-w},
year = {2019},
date = {2019-04-18},
journal = {Scientific Reports},
volume = {9},
pages = {6229},
keywords = {Big Data},
pubstate = {published},
tppubtype = {article}
}
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Hong KY, Tsin PK, van den Bosch M, Brauer M, Henderson SB Urban greenness extracted from pedestrian video and its relationship with surrounding air temperatures Urban Forestry & Urban Greening, 38 (1), pp. 280-285, 2019. (Big Data, Related publication) Big Data, Related publication @article{KY2019,
title = {Urban greenness extracted from pedestrian video and its relationship with surrounding air temperatures},
author = {Hong KY and Tsin PK and van den Bosch M and Brauer M and Henderson SB},
doi = {10.1016/j.ufug.2019.01.008},
year = {2019},
date = {2019-02-01},
journal = {Urban Forestry & Urban Greening},
volume = {38},
number = {1},
pages = {280-285},
keywords = {Big Data, Related publication},
pubstate = {published},
tppubtype = {article}
}
|
Weichenthal S, Hatzopoulou M, Brauer M A picture tells a thousand…exposures: Opportunities and challenges of deep learning image analyses in exposure science and environmental epidemiology Environment International, 122 (1), pp. 3-10, 2019. (Big Data, Measurement and Monitoring) Big Data, Measurement and Monitoring @article{S2019,
title = {A picture tells a thousand…exposures: Opportunities and challenges of deep learning image analyses in exposure science and environmental epidemiology},
author = {Weichenthal S and Hatzopoulou M and Brauer M},
doi = {10.1016/j.envint.2018.11.042},
year = {2019},
date = {2019-01-01},
journal = {Environment International},
volume = {122},
number = {1},
pages = {3-10},
keywords = {Big Data, Measurement and Monitoring},
pubstate = {published},
tppubtype = {article}
}
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2018
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Middel A, Lukasczyk J, Zakrzewski S, Arnold M, Maciejewski R Urban form and composition of street canyons: A human-centric big data and deep learning approach Landscape and Urban Planning, 183 , pp. 122-132, 2018. (Big Data, Related publication) Big Data, Related publication @article{A2018,
title = {Urban form and composition of street canyons: A human-centric big data and deep learning approach},
author = {Middel A and Lukasczyk J and Zakrzewski S and Arnold M and Maciejewski R},
doi = {10.1016/j.landurbplan.2018.12.001},
year = {2018},
date = {2018-12-14},
journal = {Landscape and Urban Planning},
volume = {183},
pages = {122-132},
keywords = {Big Data, Related publication},
pubstate = {published},
tppubtype = {article}
}
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Suel E, Boulleau M, Ezzati M, Flaxman S Combining street imagery and spatial information for measuring socioeconomic status NIPS 2018 Workshop Spatiotemporal 2018. (Big Data) Big Data @proceedings{E2018b,
title = {Combining street imagery and spatial information for measuring socioeconomic status},
author = {Suel E and Boulleau M and Ezzati M and Flaxman S},
url = {https://openreview.net/forum?id=HJl2OqjCY7},
year = {2018},
date = {2018-09-30},
organization = {NIPS 2018 Workshop Spatiotemporal},
keywords = {Big Data},
pubstate = {published},
tppubtype = {proceedings}
}
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Middel A, Lukasczyk J, Maciejewski R, Demuzere M, Rothe M Sky View Factor footprints for urban climate modeling Urban Climate, 25 , pp. 120-134, 2018. (Big Data, Related publication) Big Data, Related publication @article{A2018b,
title = {Sky View Factor footprints for urban climate modeling},
author = {Middel A and Lukasczyk J and Maciejewski R and Demuzere M and Rothe M},
doi = {10.1016/j.uclim.2018.05.004},
year = {2018},
date = {2018-09-01},
journal = {Urban Climate},
volume = {25},
pages = {120-134},
keywords = {Big Data, Related publication},
pubstate = {published},
tppubtype = {article}
}
|
