Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Yahya Ghassoun"'
Autor:
Yahya Ghassoun, Markus Gerke, Yogesh Khedar, Jan Backhaus, Markus Bobbe, Henry Meissner, Prashant Kumar Tiwary, Ralf Heyen
Publikováno v:
Remote Sensing, Vol 13, Iss 3, p 384 (2021)
The regular inspection of the crane tracks of storage cranes at the Container Terminal Altenwerder (CTA), Hamburg requires high accuracy of measurements to determine its position. The allowed tolerances are in the range of 10 mm in the XY plane on a
Externí odkaz:
https://doaj.org/article/e8df0e64aad14def9647bc24737b17bb
Autor:
Yahya Ghassoun, Manfred Buchroithner
Publikováno v:
Iconarp International Journal of Architecture and Planning, Vol 1, Iss 2, Pp 152-163 (2013)
Due to social, economic and political reasons, informal settlements are considered as a challenging problem in the third world countries. These create problems to the society and to the local government. The present study aims to discuss informal set
Externí odkaz:
https://doaj.org/article/70d1df00ba0b45feae29e007f9f7042d
Publikováno v:
Atmospheric Pollution Research. 10:1180-1189
The performance of land use regression models (LUR) depends on the quality and the representation of the explanatory variables and the spatial parameters. Generally, those parameters are implemented in a static manner, i.e. different wind directions
Publikováno v:
Ecotoxicology and Environmental Safety. 174:137-145
Exposure to ambient particulate matter (PM) can increase mortality and morbidity in urban area. In this study, annual and seasonal spatial pattern of PM1, PM2.5 and PM10 pollutants were assessed using land use regression (LUR) models in Sabzevar, Ira
Autor:
Marc-Oliver Löwner, Yahya Ghassoun
Publikováno v:
Atmospheric Environment. 166:362-373
Total particle number concentration (TNC) was studied in a 1 × 2 km area in Berlin, the capital of Germany by three Land Use Regression models (LUR). The estimation of TNC was established and compared using one 2D-LUR and two 3D-LUR models. All mode
Publikováno v:
Ecotoxicology and environmental safety. 174
Exposure to ambient particulate matter (PM) can increase mortality and morbidity in urban area. In this study, annual and seasonal spatial pattern of PM
Publikováno v:
Science of The Total Environment. 536:150-160
The microscale intra-urban variation of ultrafine particle concentrations (UFP, diameter Dp
Autor:
Yahya Ghassoun, Marc-Oliver Löwner
Publikováno v:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol IV-4-W6, Pp 41-48 (2018)
Building models represented in CityGML Level of Detail 0 to 2 were used to calculate urban morphological parameters to test their effectiveness of correlation with measured total number concentration of fine dust in Berlin. Land use regression modell
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13d561d0106cb6f37c336ecf4cf71fd4
https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-4-W6/41/2018/
https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-4-W6/41/2018/
Autor:
Marc-Oliver Löwner, Yahya Ghassoun
Publikováno v:
Advances in 3D Geoinformation ISBN: 9783319256894
In the present study two Land Use Regression Models for the estimation of urban fine dust distribution were established and compared. The first model used 2D parameters derived from an Open Street Map project data (OSM) and the second model used 3D p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::98166e61f35e9a3425c6ad98d6e5427c
https://doi.org/10.1007/978-3-319-25691-7_13
https://doi.org/10.1007/978-3-319-25691-7_13
Publikováno v:
Lecture Notes in Geoinformation and Cartography ISBN: 9783319121802
We present a comparison of a particles distribution model using 3D parameters derived from a CityGML-based 3D city model with an already advanced but 2D-based Land Use Regression model. Particles, especially ultrafine particles have significant influ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8e8cb3c6f9d2822e79cddef7566a724b
https://doi.org/10.1007/978-3-319-12181-9_12
https://doi.org/10.1007/978-3-319-12181-9_12