Leading the Next Generation of Data-Driven Discoveries

从现代数据洪流中提取准确的信息而不牺牲发现意外的能力是许多领域的核心数据挑战。一个跨学科的方法提供了重大的机会，以推进各自的艺术状态。这个项目的重点是汇集两个领域的最新技术：天体物理学和人道主义地球观测。在天体物理学中，从即将到来的大型天空调查中获得的大量数据将带来重大的挑战和机遇。对这些数据的准确分类（例如，一个星系是否包含一个被掩埋的超大质量黑洞，或者星系中的一个新光点是否是超新星，以及什么样的超新星）将使我们对宇宙如何演化和将继续演化，以及银河系等星系如何在其中形成和成长的理解取得重大进展。自然灾害发生后，救援人员需要知道哪里的道路被堵塞，哪里的建筑物被破坏，幸存者在哪里避难。当卫星俯视地球而不是仰望天空时，它们捕捉到的数据与天体物理图像有一些相似的品质，但有许多互补的差异。然而，驱动需求基本上是相同的。例如，探测风暴或地震前后的重要变化类似于识别在遥远星系中新观测到的爆炸恒星的类型。在这个例子中，天体物理学和人道主义分析可以相互借鉴：天体物理学有先进的方法来调整其算法，以补偿观测条件（如大气湍流）的差异，而人道主义“地球观测”算法已经测试了有前途的方法，可以仅基于一个前后数据点来识别关键变化。共生的潜力还有很多其他的例子，这个项目将利用这一点，在异花授粉的环境中同时进行这两项工作。一般来说，这个研究项目将开发新的工具，以便在图像和其他数据点迅速到达并且质量和来源各异的新数据制度中进行有效、准确的数据分类和标记。这两个领域都使用机器学习算法，并将机器分类与专家和高质量的人群数据标签相结合。该项目将测试每个领域开发的新技术，使用特定工具的专业知识和综合领域知识来获得新的发现。通过从每一种制度中提取见解并推进其最有效的工具，这项工作将使我们能够了解不断变化的天空和不断变化的地球，从而为贫困人口提供真正的利益（例如，增强恢复能力，缩短恢复时间，挽救生命），最大限度地发挥近处和远处的影响。这个项目的具体科学主题将涵盖全球范围内一些最紧迫的人道主义需求和一些关于宇宙的最基本的开放问题。

Extracting accurate information from the modern data flood without sacrificing the ability to discover the unexpected is a core data challenge in many fields. A cross-disciplinary approach presents major opportunities to advance the state of the art in each. This project focuses on bringing together the state of the art in two fields: astrophysics, and humanitarian Earth observation. In astrophysics, the proliferation of data that will be available from upcoming large sky surveys presents significant challenges and opportunities. Accurate classification of this data (for example, whether a galaxy contains a buried, feeding supermassive black hole, or whether a new point of light in a galaxy is a supernova, and what kind) will enable major advancement in our understanding of how the Universe has evolved and will continue to evolve, and how galaxies such as the Milky Way form and grow within it. Following a natural disaster, responders need to know where roads are blocked, where buildings are damaged, and where survivors are sheltering. When satellites look down at the Earth instead of up at the heavens, they capture data that has some similar qualities but many complementary differences to astrophysical images. The driving need, however, is fundamentally the same. For instance, detecting the important changes before and after a storm or earthquake is similar to identifying the type of exploding star newly observed in a distant galaxy. In this example, astrophysical and humanitarian analysis have lessons to teach each other: Astrophysics has advanced methods of adjusting its algorithms to compensate for differences in observing conditions such as atmospheric turbulence, while humanitarian "Earth Observation" algorithms have tested promising methods of identifying critical changes based on only one before-and-after data point. There are many other examples of the potential for symbiosis, and this project will capitalise on this by working on both simultaneously in a cross-pollinating environment.Generally speaking, this research project will develop new tools for efficient, accurate data classification and labelling in these new data regimes where images and other data points arrive rapidly and are of varying quality and origin. Both fields make use of machine learning algorithms and combine machine classification with expert and high-quality crowd data labels. The project will test new techniques developed in each field on the other, using tool-specific expertise and combined domain knowledge to make new discoveries. By extracting insights from each regime and advancing their most effective tools, this work will enable us to understand the changing skies and the changing Earth in a way that provides real benefit (e.g. increased resilience, decreased recovery time, saving of lives) to distressed populations, maximising impact both near and far.The specific scientific topics this project will address cover some of the most pressing humanitarian needs across the globe and some of the most fundamental open questions about the Universe.