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.
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The New Zealand Ministry of Business, Innovation and Employment funded this research under contract C09X1923.
Articles, book chapters & proceedings:
Zhao, H., Morgenroth, J., Pearse, G., & Schindler, J. 2023. A Systematic Review of Individual Tree Crown Detection and Delineation with Convolutional Neural Networks (CNN). Current Forestry Reports, 1-22. https://doi.org/10.1007/s40725-023-00184-3
Spiekermann, R. I., van Zadelhoff, F., Schindler, J., Smith, H. G., Phillips, C., & Schwarz, M. 2023. Contrasting Physical and Statistical Landslide Susceptibility Models at the Scale of Individual Trees: Implications for Land Management. Geomorphology, 108870. Preprint available at SSRN 4347971. https://doi.org/10.1016/j.geomorph.2023.108870
Fu, W., Xue, B., Zhang, M., Schindler, J. 2023. Evolving U-Nets Using Genetic Programming for Tree Crown Segmentation. In: Yan, W.Q., Nguyen, M., Stommel, M. (eds) Image and Vision Computing. IVCNZ 2022. Lecture Notes in Computer Science, vol 13836. Springer, Cham. https://doi.org/10.1007/978-3-031-25825-1_14
B. Xu, Y. Bi, B. Xue, J. Schindler, B. Martin and M. Zhang, 2022. "Automatically Designing U-Nets Using A Genetic Algorithm for Tree Image Segmentation," IEEE Symposium Series on Computational Intelligence (SSCI), Singapore, Singapore, pp. 626-633, doi: 10.1109/SSCI51031.2022.10022182. https://ieeexplore.ieee.org/abstract/document/10022182
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. https://doi.org/10.1016/j.procs.2022.03.052
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.