Carbon stock estimation at scale from aerial and satellite imagery

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Date
30-07-2024
Authors
To, Alex
Pham, Hoang Quoc Viet
Nguyen, Quang H.
Davis, Joseph G.
O’Sullivan, Barry
Pan, Shan L.
Nguyen, Hoang D.
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Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published Version
https://doi.org/10.1109/CAI59869.2024.00064
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Abstract
In 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.
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Keywords
Remote sensing , Tree semantic segmentation , Aerial imagery , Satellite imagery , Domain adaptation
Citation
To, 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/CAI59869.2024.00064
Link to publisher’s version
https://ieeecai.org/2024/
URI
https://hdl.handle.net/10468/15967
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Computer Science - Conference Items
Insight Centre for Data Analytics - Conference Items
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