建立數位地球：基於Omniverse平台的東南亞衛星雲圖與雷達圖可視化

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：109

、訪客IP：18.227.134.133

姓名

陳宇揚(Yu-Yang Chen) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

建立數位地球：基於Omniverse平台的東南亞衛星雲圖與雷達圖可視化
(Building a Digital Earth: Visualization of Southeast Asia′s Satellite Cloud and Radar Data on Omniverse)

相關論文

★ 在有干擾的虛擬教室環境下大人小孩的行為表現與腦神經反應的異同	★ 使用映射模型和跨資料集遷移式學習的輕量化居家衰弱症訓練系統
★ 心率生理回饋放鬆訓練對於海洛因使用疾患(HUD)生理資訊之影響分析	★ 基於深度學習模型的3D心理旋轉對認知障礙的診斷與評估
★ 評估注意力偵測之穿戴式腦電電極放置有效性	★ 基於多維度的臺灣天氣類型機器學習臨近預報與分類系統
★ 整合檢索增強生成與大型語言模型應用於精準運動科學平台：架構與實現	★ 透過生理數據分析的VR戰車訓練系統，評估壓力對認知專注力與穩定性的影響及通過多次訓練表現驗證系統有效性
★ 基於機器學習分析ADHD亞型利用VR認知測驗同步的神經生理數據	★ FrAIlti：利用人工智慧和3D攝影技術提升老年照護的自動化衰弱評估系統
★ 自閉症譜系障礙虛擬實境訓練系統的開發與驗證	★ 智慧醫療物聯網平台之裝置管理與應用
★ 智慧醫療物聯網平台之多租戶應用	★ XRCURE：基於實證醫學的AIOT、XR和可穿戴感測器在AWS上的數位療法
★ 重複性經顱磁刺激同步虛擬實境與生理監測用於失語症創新治療與評估	★ 將網路威脅情報與多視角分析和雙聚類結合：一種多維視覺化方法

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

為了應對對精確且實時的環境監測和預測的日益需求，本文介紹了一個基於NVIDIA Omniverse構建的系統，用於創建地球的數位孿生。這個數位孿生系統旨在提供一個全面且動態的地球大氣狀況表示，以便更好地理解和管理環境現象。我們的方法整合了衛星雲圖、降雨圖和雷達數據作為輸入，並使用數值方法來計算雲的物理性質和預測未來的降雨區域。結果展示了在Omniverse中呈現的高保真度可視化效果，突顯了系統在精確建模複雜大氣過程方面的能力。此外，我們利用Web Socket技術將這些結果顯示在網頁平台上，使智能手機和筆記本電腦等低端設備也能夠訪問，確保了更廣泛的可及性和可用性。這個系統不僅增強了我們監測和預測天氣模式的能力，還為災害準備和應對、城市規劃和氣候研究提供了寶貴的工具。

摘要(英)

In response to the increasing need for accurate and real-time environmental monitoring and prediction, this paper presents a system built on NVIDIA Omniverse to create a digital twin of the Earth. The digital twin aims to provide a comprehensive and dynamic representation of the Earth′s atmospheric conditions, facilitating better understanding and management of environmental phenomena. Our approach integrates satellite cloud imagery, rainfall maps, and radar data as inputs. We employ numerical methods to compute the physical properties of clouds and predict future rainfall regions. The results showcase the high-fidelity visualizations rendered within Omniverse, highlighting the system′s capability to model complex atmospheric processes accurately. Additionally, we utilize web sockets to display these results on web platforms, enabling access from low-end devices, such as smartphones and laptops, ensuring broader accessibility and usability. This system not only enhances our ability to monitor and predict weather patterns but also provides a valuable tool for disaster preparedness and response, urban planning, and climate research.

關鍵字(中)

★ 衛星雲圖
★ 孿生地球
★ Omniverse 平台

關鍵字(英)

★ Satellite Imagery
★ Digital twins
★ Omniverse platform

論文目次

摘要 I
Abstract II
致謝 III
Table of Contents IV
List of Figures V
List of Tables VI
I. Introduction 1
II. Related Works 8
III. Method 15
IV. Results 33
V. Discussions 41
VI. Conclusion and Future Works 44
Reference 45

參考文獻

[1] J. Pálenik, T. Spengler, and H. Hauser, “Isotrotter: Visually guided empirical modelling of atmospheric convection,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 775–784, 2020.
[2] K. Kashinath, M. S. Pritchard, A. Anandkumar, J. Pathak, M. Mardani, T. Kurth, D. Hall, P. Messmer, S. Posey, S. Subramanian, et al., “Towards digital twins for nvidia’s earth-2 initiative: Pushing the limits of deep auto-regressive fourier neural operator and transformer models for earth system emulation,” in AGU Fall Meeting Abstracts, vol. 2022, pp. GC16C–05, 2022.
[3] J. Vatanen, “Exploring nvidia omniverse ecosystem,” 2024.
[4] S. Salcedo-Sanz, P. Ghamisi, M. Piles, M. Werner, L. Cuadra, A. Moreno-Martínez, E. Izquierdo-Verdiguier, J. Muñoz-Marí,
A. Mosavi, and G. Camps-Valls, “Machine learning information fusion in earth observation: A comprehensive review of methods, applications and data sources,” Information Fusion, vol. 63, pp. 256–272, 2020.
[5] L. Di Girolamo, D. Cox, R. Patterson, S. Levy, K. Borkiewicz, A. Chris- tensen, J. Carpenter, Y. Hong, R. Miller, D. Fu, et al., “Data fusion visualization for nasa camp2ex field campaign,” 2021.
[6] B. Gokaraju, R. A. Nóbrega, D. A. Doss, A. C. Turlapaty, and R. C. Tesiero, “Data fusion of multi-source satellite data sets for cost-effective disaster management studies,” in SoutheastCon 2017, pp. 1–5, IEEE,
2017.
[7] T. Lei, J. Wang, X. Li, W. Wang, C. Shao, and B. Liu, “Flood disaster monitoring and emergency assessment based on multi-source remote sensing observations,” Water, vol. 14, no. 14, p. 2207, 2022.
[8] D. Ververidis, S. Nikolopoulos, and I. Kompatsiaris, “A review of collaborative virtual reality systems for the architecture, engineering, and construction industry,” Architecture, vol. 2, no. 3, pp. 476–496, 2022.
[9] Y. Zhao, Y. Zeng, Q. Long, Y. N. Wu, and S.-C. Zhu, “Sim2plan: Robot motion planning via message passing between simulation and reality,” in Proceedings of the Future Technologies Conference, pp. 29–42, Springer,
2023.
[10] Y. Rao, R. Redmon, K. Dale, S. E. Haupt, A. Hopkinson, A. Bostrom, S. Boukabara, T. Geenen, D. M. Hall, B. D. Smith, et al., “Developing digital twins for earth systems: purpose, requisites, and benefits,” arXiv preprint arXiv:2306.11175, 2023.
[11] G. Page, B. Yorke-Biggs, and S. De-Guido, “Ide digital twin white paper: harnessing the digital twin for real competitive advantage,”
[12] F. Bahrpeyma, A. Sunilkumar, and D. Reichelt, “Application of reinforcement learning to ur10 positioning for prioritized multi-step inspection in nvidia omniverse,” in 2023 IEEE Symposium on Industrial Electronics & Applications (ISIEA), pp. 1–6, IEEE, 2023.
[13] B. Xu, F. Gao, C. Yu, R. Zhang, Y. Wu, and Y. Wang, “Omnidrones: An efficient and flexible platform for reinforcement learning in drone control,” IEEE Robotics and Automation Letters, 2024.
[14] S. Mann, Y. Yuan, F. Lamberti, A. El Saddik, R. Thawonmas, and F. G. Prattico, “extended meta-uni-omni-verse (xv): Introduction, taxonomy, and state-of-the-art,” IEEE Consumer Electronics Magazine, vol. 13,
no. 3, pp. 27–35, 2023.
[15] M. Kern, T. Hewson, F. Sadlo, R. Westermann, and M. Rautenhaus, “Robust detection and visualization of jet-stream core lines in at- mospheric flow,” IEEE transactions on visualization and computer graphics, vol. 24, no. 1, pp. 893–902, 2017.
[16] M. Kern, T. Hewson, A. Schätler, R. Westermann, and M. Rautenhaus, “Interactive 3d visual analysis of atmospheric fronts,” IEEE transactions on visualization and computer graphics, vol. 25, no. 1, pp. 1080–1090,
2018.
[17] Z. Liu, T. Foresman, J. van Genderen, and L. Wang, “Understanding digital earth,” Manual of digital earth, pp. 1–21, 2020.
[18] M. Ehlers, P. Woodgate, A. Annoni, and S. Schade, “Advancing digital earth: beyond the next generation,” International Journal of Digital Earth, vol. 7, no. 1, pp. 3–16, 2014.
[19] T. Wright, M. Burton, D. Pyle, and T. Caltabiano, “Visualising volcanic gas plumes with virtual globes,” Computers & Geosciences, vol. 35, no. 9, pp. 1837–1842, 2009.
[20] J. E. Bailey and A. Chen, “The role of virtual globes in geoscience,” tech. rep., Elsevier Science Publishers, 2011.
[21] H. Doleisch, P. Muigg, and H. Hauser, “Interactive visual analysis of hurricane isabel with simvis,” IEEE Visualization Contest, 2004.
[22] K. I. Dale, E. C. Pope, A. R. Hopkinson, T. McCaie, and J. A. Lowe, “Environment-aware digital twins: Incorporating weather and climate information to support risk-based decision-making,” Artificial Intelligence for the Earth Systems, vol. 2, no. 4, p. e230023, 2023.
[23] M. Hummel and K. van Kooten, “Leveraging nvidia omniverse for in situ visualization,” in High Performance Computing: ISC High Performance 2019 International Workshops, Frankfurt, Germany, June 16-20, 2019, Revised Selected Papers 34, pp. 634–642, Springer, 2019.
[24] M. Datcu, D. Faur, E. Mamut, I. Nedelcu, C. Ionescu, and L. Miron, “Digital twin earth for climate change adapation: An ai based federated system,” in IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, pp. 1392–1395, IEEE, 2023.
[25] S. Dehnavi, Y. Maghsoudi, K. Zakšek, M. J. Valadan Zoej, G. Seckmeyer, and V. Skripachev, “Cloud detection based on high resolution stereo pairs of the geostationary meteosat images,” Remote Sensing, vol. 12, no. 3, p. 371, 2020.
[26] A. Okuyama, M. Takahashi, K. Date, K. Hosaka, H. Murata, T. Tabata, and R. Yoshino, “Validation of himawari-8/ahi radiometric calibration based on two years of in-orbit data,” Journal of the Meteorological
Society of Japan. Ser. II, 2018.
[27] G. L. B. N. B. J. C. G. G. R. e. a. Martin, G., “Support for goes-r and himawari-8 in cspp geo.,” 2016.
[28] J. Li, C.-Y. Liu, H.-L. Huang, T. J. Schmit, X. Wu, W. P. Menzel, and J. J. Gurka, “Optimal cloud-clearing for airs radiances using modis,” IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 6,
pp. 1266–1278, 2005.
[29] J. R. Eyre and H. M. Woolf, “Transmittance of atmospheric gases in the microwave region: a fast model,” Applied optics, vol. 27, no. 15, pp. 3244–3249, 1988.
[30] C.-Y. Liu, J. Li, E. Weisz, T. J. Schmit, S. A. Ackerman, and H.-L. Huang, “Synergistic use of airs and modis radiance measurements for atmospheric profiling,” Geophysical Research Letters, vol. 35, no. 21,
2008.
[31] H. Letu, K. Yang, T. Y. Nakajima, H. Ishimoto, T. M. Nagao, J. Riedi, A. J. Baran, R. Ma, T. Wang, H. Shang, et al., “High-resolution retrieval of cloud microphysical properties and surface solar radiation using himawari-8/ahi next-generation geostationary satellite,” Remote Sensing of Environment, vol. 239, p. 111583, 2020.
[32] Y.-J. Noh, J. M. Haynes, S. D. Miller, C. J. Seaman, A. K. Heidinger, J. Weinrich, M. S. Kulie, M. Niznik, and B. J. Daub, “A framework for satellite-based 3d cloud data: An overview of the viirs cloud base height retrieval and user engagement for aviation applications,” Remote Sensing, vol. 14, no. 21, p. 5524, 2022.
[33] W. P. Menzel, R. A. Frey, H. Zhang, D. P. Wylie, C. C. Moeller, R. E. Holz, B. Maddux, B. A. Baum, K. I. Strabala, and L. E. Gumley, “Modis global cloud-top pressure and amount estimation: Algorithm description and results,” Journal of Applied Meteorology and Climatology, vol. 47, no. 4, pp. 1175–1198, 2008.
[34] U. S. N. B. of Standards and F. E. Nicodemus, Geometrical consider- ations and nomenclature for reflectance, vol. 160. US Department of Commerce, National Bureau of Standards Washington, DC, USA, 1977.
[35] C. F. Bohren and D. R. Huffman, Absorption and scattering of light by small particles. John Wiley & Sons, 2008.
[36] H. Wann Jensen, S. R. Marschner, M. Levoy, and P. Hanrahan, “A practical model for subsurface light transport,” in Seminal Graphics Papers: Pushing the Boundaries, Volume 2, pp. 319–326, 2023.

指導教授

葉士青吳曉光(Shih-Ching Yeh Hsiao-kuang Wu)

審核日期

2024-7-30

推文