Machine Learning
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
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基于数字孪生的现实条件海平面上升模拟
Translated by Dr. Mengyi Jin
数字孪生技术正越来越多地应用于模拟海平面上升所带来的影响,为城市规划、海岸管理和灾害应对等领域的决策者提供了宝贵的工具。这些虚拟模型整合了包括地理空间影像、人工智能和环境监测系统等不同来源的实时数据,可以详细模拟海平面上升对特定区域产生的影响。
通过准确绘制当前的土地覆盖特征,并不断用新数据更新这些模型,数字孪生使研究人员和政府部门能够在不同的气候变化条件下对未来的情景进行预测。这有助于识别脆弱区域、规划基础防护设施以及优化疏散策略。例如,高分辨率地理空间数据可以显示哪些区域面临洪水风险,而由人工智能驱动的模拟则可以预测海平面上升可能对当地生态系统和城市环境产生的长期影响。
通过将海平面上升纳入数字孪生模拟,城市规划者和环境科学家可以充分了解其对沿海地区的长期影响,从而为气候变化带来的挑战做好更加充分的准备。这项技术对于直观呈现和科学规划适应性应对措施,从而减缓海平面上升可能造成的损害具有重要意义。
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Jumpei Takami
Associate Expert in Remote Sensing United Nations Office for Outer Space Affairs
Proficient in Remote Sensing and Geographic Information Systems with Machine Learning approach: Analysis of disaster risk reduction and management associated with climate change using remote sensing and geographic information system technologies and implementation of disaster-oriented projects; landslide, flooding, drought, and land subsidence, optionally with machine learning approaches; forest inventory for canopy height and above ground biomass, and planning, design, construction, and maintenance of civil engineering construction projects.
Sawaid Abbas
Assistant Professor Smart Sensing for Climate and Development, GIS Centre, University of the Punjab Centre for Geographical Information, University of the Punjab
Sawaid is a spatial data scientist who works at the nexus of earth science, ecology and climate change through leveraging remote sensing, machine learning, and strong domain knowledge. His key work involves forest succession, drought, and rangelands which were accomplished through collaboration with institutions like WWF, ICIMOD, ICRAF, AFCD, and KFBG.