I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
A Deep Learning Approach for Meter-Scale Air Quality Estima..:
Sorek-Hamer, Meytar
;
Von Pohle, Michael
;
Sahasrabhojanee, Adwait
...
Atmosphere. 13 (2022) 5 - p. 696 , 2022
Link:
https://doi.org/10.3390/atmos13050696
RT Journal T1
A Deep Learning Approach for Meter-Scale Air Quality Estimation in Urban Environments Using Very High-Spatial-Resolution Satellite Imagery
UL https://suche.suub.uni-bremen.de/peid=cr-10.3390_atmos13050696&Exemplar=1&LAN=DE A1 Sorek-Hamer, Meytar A1 Von Pohle, Michael A1 Sahasrabhojanee, Adwait A1 Akbari Asanjan, Ata A1 Deardorff, Emily A1 Suel, Esra A1 Lingenfelter, Violet A1 Das, Kamalika A1 Oza, Nikunj C. A1 Ezzati, Majid A1 Brauer, Michael PB MDPI AG YR 2022 SN 2073-4433 JF Atmosphere VO 13 IS 5 SP 696 LK http://dx.doi.org/https://doi.org/10.3390/atmos13050696 DO https://doi.org/10.3390/atmos13050696 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)