Remote_Sensing_Pollutant_Plants_China.pdf (529.32 kB)
Detection and characterisation of pollutant assets with AI and EO to prioritise green investments: the geoasset framework
conference contribution
posted on 2023-06-10, 04:24 authored by Cristian Rossi, Nataliya Tkachenko, Maral Bayaraa, Georgios Voulgaris, Peter Foster, Steven Reece, Kimberly Scott, Christophe Christiaen, Matthew McCarten, Georgios VoulgarisDetailed and complete data on physical assets are required in order to adequately assess environment-related risk and impact exposure and the diffusion of these risks and impacts through the financial system. Investors need to know where the physical assets (e.g., power plant, factory, farm) are located of companies in their portfolios, and what their polluting characteristics are. This is essential to manage these environment-related risks and to channel investments to more sustainable alternatives. At present, data on physical assets is typically incomplete, inaccurate, or not released in a timely manner. As a result, key stakeholders including asset owners, asset managers, regulators and policymakers are frequently forced to make crucial decisions with incomplete information. Accurate and comprehensive global asset-level databases are a prerequisite for meaningful innovation in green and digital finance. They provide the link between the financial system and the “real economy” and allows the wealth of EO datasets and insights that we have available to be made actionable for sustainable finance decision making. We created a framework to derive a global database of pollutant plants, such as cement, iron, and steel, which represent about 15% of the global CO2 emissions. Our solution makes use of state-of-the-art deep learning architectures coupled with Earth observation data.
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Publication status
- Published
File Version
- Accepted version
Journal
IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) proceedingsISSN
2153-7003Publisher
IEEEExternal DOI
Page range
1-4Event name
International Geoscience and Remote Sensing Symposium (IGARSS)Event location
Kuala Lumpur, MalaysiaEvent type
conferenceEvent date
17th - 22nd July 2022ISBN
9781665427920Department affiliated with
- Informatics Publications
Notes
© 20XX 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.Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2022-08-03First Open Access (FOA) Date
2022-10-05First Compliant Deposit (FCD) Date
2022-08-03Usage metrics
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