eprintid: 10198606
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/19/86/06
datestamp: 2024-10-18 12:41:07
lastmod: 2024-10-18 12:41:07
status_changed: 2024-10-18 12:41:07
type: proceedings_section
metadata_visibility: show
sword_depositor: 699
creators_name: Rakotoharisoa, A
creators_name: Cenci, S
creators_name: Arcucci, R
title: Evaluating the Impact of Atmospheric CO2 Emissions via Super Resolution of Remote Sensing Data
ispublished: pub
divisions: UCL
divisions: B04
divisions: C04
divisions: F34
keywords: Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Interdisciplinary Applications, Computer Science, Theory & Methods, Computer Science, Super Resolution, Remote Sensing, GHG Monitoring
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Understanding how emissions from point sources affect the atmospheric concentrations of Greenhouse Gases (GHGs) locally and on a wider scale is crucial to quantify their impact on climate change. To this end, different ways of performing global monitoring of GHGs concentration using remote sensing data have been explored. The main difficulty remains to find the right balance between high resolution monitoring, which is often incomplete, and global monitoring, but at a coarser resolution. This study proposes the application of Super Resolution (SR), a Deep Learning (DL) technique commonly employed in Computer Vision, to increase the resolution of atmospheric CO2 L3 satellite data. The resulting maps are achieving an approximate resolution of 1 km * 1 km and are then compared with a benchmark of existing methods, before being used for emissions monitoring.
date: 2024-06-28
date_type: published
publisher: Springer, Cham
official_url: http://dx.doi.org/10.1007/978-3-031-63775-9_28
full_text_type: other
language: eng
verified: verified_manual
elements_id: 2309071
doi: 10.1007/978-3-031-63775-9_28
isbn_13: 978-3-031-63774-2
lyricists_name: Cenci, Simone
lyricists_id: SCENC80
actors_name: Cenci, Simone
actors_id: SCENC80
actors_role: owner
full_text_status: restricted
pres_type: paper
series: Lecture Notes in Computer Science, vol 14836
publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
volume: 14836
pagerange: 383-390
event_title: ICCS 2024
event_location: SPAIN, Univ Malaga, Malaga
event_dates: 2 Jul 2024 - 4 Jul 2024
issn: 0302-9743
book_title: Computational Science – ICCS 2024
editors_name: Franco, L
editors_name: DeMulatier, C
editors_name: Paszynski, M
editors_name: Krzhizhanovskaya, VV
editors_name: Dongarra, JJ
editors_name: Sloot, PMA
citation:        Rakotoharisoa, A;    Cenci, S;    Arcucci, R;      (2024)    Evaluating the Impact of Atmospheric CO2 Emissions via Super Resolution of Remote Sensing Data.                     In: Franco, L and DeMulatier, C and Paszynski, M and Krzhizhanovskaya, VV and Dongarra, JJ and Sloot, PMA, (eds.) Computational Science – ICCS 2024.  (pp. pp. 383-390).  Springer, Cham      
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10198606/1/Rakotoharisoa_etal_2024.pdf