Gender and authorship patterns in urban land science

Autor: Tzu-Hsin Karen Chen, Karen C. Seto

Publikováno v: Journal of Land Use Science, Vol 17, Iss 1, Pp 245-261 (2022)

ABSTRACTWhat are patterns of gender and authorship in urban land science? Our bibliometric analysis shows that the proportion of women shrinks among highly productive, impactful, and senior authors, akin to a pyramid shape. First, women are only one

Externí odkaz: https://doaj.org/article/c5c2f9180cbb45afbdc05794c40a27ec

Higher depression risks in medium- than in high-density urban form across Denmark

Autor: Tzu-Hsin Karen Chen, Henriette Thisted Horsdal, Karl Samuelsson, Ane Marie Closter, Megan Davies, Stephan Barthel, Carsten Bøcker Pedersen, Alexander V. Prishchepov, Clive E. Sabel

Publikováno v: Chen, T-H K, Horsdal, H T, Samuelsson, K, Closter, A M, Davies, M, Barthel, S, Pedersen, C B, Prishchepov, A V & Sabel, C E 2023, ' Higher depression risks in medium-than in high-density urban form across Denmark ', Science Advances . https://doi.org/10.1126/sciadv.adf3760
Chen, T-H K, Horsdal, H T, Samuelsson, K, Closter, A M, Davies, M, Barthel, S, Pedersen, C B, Prishchepov, A V & Sabel, C E 2023, ' Higher depression risks in medium-than in high-density urban form across Denmark ', Science Advances, vol. 9, no. 21, eadf3760 . https://doi.org/10.1126/sciadv.adf3760

Urban areas are associated with higher depression risks than rural areas. However, less is known about how different types of urban environments relate to depression risk. Here, we use satellite imagery and machine learning to quantify three-dimensio

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1c7d03a2c1943e016baf815c81da7177
https://doi.org/10.1126/sciadv.adf3760

Detecting subpixel human settlements in mountains using deep learning: A case of the Hindu Kush Himalaya 1990–2020

Autor: Tzu-Hsin Karen Chen, Bhartendu Pandey, Karen C. Seto

Publikováno v: Remote Sensing of Environment. 294:113625

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c1eda22b0ab09fdfe3782889f6a4cae4
https://doi.org/10.1016/j.rse.2023.113625

Creating cloud-free satellite imagery from image time series with deep learning

Autor: Alexander V. Prishchepov, Tzu-Hsin Karen Chen, Fabian Gieseke, Stefan Oehmcke

Publikováno v: Oehmcke, S, Chen, T H K, Prishchepov, A V & Gieseke, F 2020, Creating cloud-free satellite imagery from image time series with deep learning . in V Chandola, R R Vatsavai & A Shashidharan (eds), Proceedings of the 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BIGSPATIAL 2020 ., 3429345, Association for Computing Machinery, Proceedings of the 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BIGSPATIAL 2020, 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BIGSPATIAL 2020, Seattle, Virtual, United States, 03/11/2020 . https://doi.org/10.1145/3423336.3429345
BigSpatial@SIGSPATIAL
Oehmcke, S, Chen, T H K, Prishchepov, A V & Gieseke, F 2020, Creating cloud-free satellite imagery from image time series with deep learning . in V Chandola, R R Vatsavai & A Shashidharan (eds), Proceedings of the 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BIGSPATIAL 2020 ., 3, Association for Computing Machinery, 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data-BIGSPATIAL 2020, San Diego / Virtuel, United States, 03/11/2020 . https://doi.org/10.1145/3423336.3429345

Optical satellite images are important for environmental monitoring. Unfortunately, such images are often affected by distortions, such as clouds, shadows, or missing data. This work proposes a deep learning approach for cleaning and imputing satelli

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::10bb3b60b91f4950f54b772fc82ef133
https://pure.au.dk/portal/da/publications/creating-cloudfree-satellite-imagery-from-image-time-series-with-deep-learning(d8b4f813-7410-4fb1-8d80-55b00102c591).html

Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: A semantic segmentation solution

Autor: Clive E. Sabel, Chunping Qiu, Michael Schmitt, Tzu-Hsin Karen Chen, Alexander V. Prishchepov, Xiao Xiang Zhu

Publikováno v: Chen, T-H K, Qiu, C, Schmitt, M, Zhu, X, Sabel, C E & Prishchepov, A 2020, ' Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: A semantic segmentation solution ', Remote Sensing of Environment, vol. 251, 112096 . https://doi.org/10.1016/j.rse.2020.112096
Chen, T H K, Qiu, C, Schmitt, M, Zhu, X X, Sabel, C E & Prishchepov, A V 2020, ' Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018 : A semantic segmentation solution ', Remote Sensing of Environment, vol. 251, 112096 . https://doi.org/10.1016/j.rse.2020.112096

Landsat imagery is an unparalleled freely available data source that allows reconstructing horizontal and vertical urban form. This paper addresses the challenge of using Landsat data, particularly its 30m spatial resolution, for monitoring three-dim

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dcff25d6af4834b3caa1f16ba820c35c
https://pure.au.dk/portal/da/publications/mapping-horizontal-and-vertical-urban-densification-in-denmark-with-landsat-timeseries-from-1985-to-2018-a-semantic-segmentation-solution(a89efd0d-13b0-4b0d-a38a-fac9d844bcbb).html