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Bridging the gap between remote sensing and tree modelling with data science

October 2020 - October 2023
MBIE/SDSC funded bilateral programme between New Zealand and Singapore
"Bridging the gap between remote sensing and tree modelling with data science" is a joint New Zealand-Singapore Data Science research programme leveraging Kiwi/Singaporean expertise in data science, remote sensing, and 3D modelling. The project team consists of researchers from Manaaki Whenua - Landcare Research, Scion, University of Canterbury, Victoria University of Wellington, Institute of High Performance Computing and Nanyang Technological University.

Executive Summary

Singapore, the ‘City in a Garden’, embodies the ‘green city’ concept with over 7 million urban trees covering 700 km2. New Zealand, with 24% of its 270,000 km2 land covered in forest, also actively supports and promotes urban re-greening in many of its cities. Sustaining and enhancing biodiversity and healthy living environments are priorities for Singapore and New Zealand that require careful management of trees in urban areas and forests. Reliable information, models, and analysis of trees and their interaction with the surrounding environment are essential to inform management decisions. However, these are currently limited by the quality of available data, tools, and techniques.

Leveraging our joint expertise in data science, remote sensing, and 3D modelling, we propose a proof-of-concept integrated methodology. We will develop novel data-science methods for extracting tree species information from petabytes of multiresolution remote- sensing data to model tree species and their interactions with the environment, and subsequently analyse their socio-economic impacts. This work will form the basis for future research collaborations to enable further modelling, simulation, and analysis. In the long term, our work will empower and inform decision-makers on trees and environmental considerations for the greater benefit of both New Zealand and Singapore.

Work Packages

Project team contacts

  • Dr Jan Schindler (Science Leader, PI) | Manaaki Whenua – Landcare Research, New Zealand)
  • Dr Like Gobeawan (Science Leader, PI) | Institute of High Performance Computing, A*STAR, Singapore
  • Dr Damien Sellier (Co-PI) | Scion, New Zealand
  • Professor Richard Green (Co-PI) | University of Canterbury, New Zealand
  • Associate Professor Lee Bu Sung (Co-PI) | Nanyang Technological University, Singapore
  • Professor Mengjie Zhang (Key Individual) | Victoria University of Wellington, New Zealand

Funding agencies

The New Zealand Ministry of Business, Innovation and Employment funded this research under contract C09X1923. 

This research/project is supported by the National Research Foundation, Singapore under its Industry Alignment Fund – Pre-positioning (IAF-PP) Funding Initiative. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.

Publications

Articles, book chapters & proceedings:

Lim YJ, Yean S, Lee BS, Edwards P. 2022. What Could Ambient Noise around Campus Tell Us? A Study on Campus Crowd Noise. The 13th International Conference on Ambient Systems, Networks and Technologies (ANT), March 22 - 25, 2022, Porto, Portugal. Procedia Computer Science.

Gobeawan L., Wise D.J., Wong S.T., Yee A.T.K., Lim C.W., Su Y. (2021) Tree Species Modelling for Digital Twin Cities. In: Gavrilova M.L., Tan C.K. (eds) Transactions on Computational Science XXXVIII. Lecture Notes in Computer Science, vol 12620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-63170-6_2

Conference talks

Lim YJ, Yean S, Lee BS, Edwards P. 2022. What Could Ambient Noise around Campus Tell Us? A Study on Campus Crowd Noise. The 13th International Conference on Ambient Systems, Networks and Technologies (ANT), March 22 - 25, 2022, Porto, Portugal. Procedia Computer Science.

Jan Schindler, Brent Martin, Alexander Amies, Ben Jolly and David Pairman. 2021. Experiences developing an operational workflow for large-scale instance and semantic segmentation of remote sensing imagery using CNNs. New Zealand Research Software Engineering Conference. Online. 17 September 2021.

Edwards, P., Yean, S., Lee, B. S., Diprose, G., Simcock, R., Schindler, J. & Green, R. 2021. Data science, urban trees and wellbeing. Innovations in Applied Data Symposium, 03 June 2021. Wellington.

Edwards, P., Lee, B. S., Yean, S., Diprose, G., Schindler, J. & Green, R. 2020. Data Science, urban trees and wellbeing. eResearch Australasia. Online. 21 October 2020.

Acknowledgments

We would like to thank our external partners for providing in-kind contributions and support our programme:
Wellington City Council, Wellington Regional Council, Taranaki Whānui-Te Ātiawa and YJP Surveyors Pte. Ltd.

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