Carbon stock estimation at scale from aerial and satellite imagery

Simple item page
dc.contributor.authorTo, Alexen
dc.contributor.authorPham, Hoang Quoc Vieten
dc.contributor.authorNguyen, Quang H.en
dc.contributor.authorDavis, Joseph G.en
dc.contributor.authorO’Sullivan, Barryen
dc.contributor.authorPan, Shan L.en
dc.contributor.authorNguyen, Hoang D.en
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2024-06-04T08:41:18Z
dc.date.available2024-06-04T08:41:18Z
dc.date.issued30-07-2024en
dc.description.abstractIn the ongoing efforts to mitigate climate change effect, the capability to reliably estimate forest carbon stock on a global scale is vital to support sustainable development. This entails the investigation of tree coverage from diverse forest ecosystems worldwide, necessitating a substantial volume of high-resolution images. This paper integrates a variety of remote sensing data sources, from aerial to satellite imagery, for the training and development of our AI system. Given the heterogeneous nature of these data sources, we develop a standardization method to ensure consistent image size and resolution between source platforms. Our harmonized dataset includes 86,088 training images and 21,768 validation images, each with a high resolution of 1.194 m2 per pixel. We introduce a novel technique for tree semantic segmentation which offers a more effective alternative to traditional individual tree crown delineation for large-scale tree coverage estimation. To assess the adaptability of our AI models, we conducted experiments on a hand-annotated satellite image test set and achieved a High Vegetation IoU score of 45.73%. Building on these findings, we present an interactive web-based Geographic Information System for navigating high vegetation segmented satellite images and estimating carbon stock on a global scale.en
dc.description.sponsorshipScience Foundation Ireland (12/RC/2289-P2)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationTo, A., Pham, H. Q. V., Nguyen, Q. H., Davis, J. G., O’Sullivan, B., Pan, S. L. and Nguyen, H. D. (2024) 'Carbon stock estimation at scale from aerial and satellite imagery', 2024 IEEE Conference on Artificial Intelligence (IEEE CAI 2024), Singapore, 25-27 June, pp. 292-299. https://doi.org/10.1109/CAI59869.2024.00064en
dc.identifier.doihttps://doi.org/10.1109/CAI59869.2024.00064
dc.identifier.endpage299
dc.identifier.startpage292
dc.identifier.urihttps://hdl.handle.net/10468/15967
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.ispartof2024 IEEE Conference on Artificial Intelligence (IEEE CAI 2024), Singapore, 25-27 June.en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Centres for Research Training Programme::Data and ICT Skills for the Future/18/CRT/6223/IE/SFI Centre for Research Training in Artificial Intelligence/en
dc.relation.urihttps://ieeecai.org/2024/en
dc.rights© 2024, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectRemote sensingen
dc.subjectTree semantic segmentationen
dc.subjectAerial imageryen
dc.subjectSatellite imageryen
dc.subjectDomain adaptationen
dc.titleCarbon stock estimation at scale from aerial and satellite imageryen
dc.typeConference itemen
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
2024_IEEE_CAI_CarbonStockEstimation.pdf
Size:
4.85 MB
Format:
Adobe Portable Document Format
Description:
Accepted Version
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Computer Science - Conference Items
Insight Centre for Data Analytics - Conference Items