Exploring the potential of Multimodal Remote Sensing for Sustainable Development Goals

“2030年可持续发展议程强调了地理空间信息和地球观测（EO）为可持续发展目标和指标提供信息的重要性。EO与高分辨率原位数据相结合，提供了一个机会，以了解和改善地球过程的动态行为，在很大程度上是不可能的。多个数据源使我们能够掌握不同的信息片段，每个部分描述地球表面发生的不同情况。然而，从数据分析的角度来看，将所有这些数据集结合起来是一项具有挑战性的任务。在当前的大数据场景中，我们有大量由复杂的底层物理过程生成的数据，这些数据很难监控。从这些大规模的异构数据集中提取有意义的信息是一个开放的问题。第二大挑战是找出这些数据集可以组合的级别-原始或像素级别，特征级别或决策级别。此外，大多数当前的数据融合技术是基于数据驱动的人工智能（AI）方法，无法提供透明性和解释性的AI算法。换句话说，它没有为我们提供一个明确的物理意义，即结果意味着什么以及为什么模型做出这种预测。的博士该项目旨在了解其中的一些挑战，并开发数据分析方法，以利用可解释的数据融合技术改进地球物理过程的特征描述。博士的主要研究目标有三个。首先，了解通过结合领域知识和最先进的特征选择算法为任何项目选择最相关数据集进行有效分析的方法。第二，研究数据融合技术，将这些最相关的数据集联合收割机结合起来，第三，探索如何量化融合产品的置信度，从而处理与遥感产品相关的不确定性。"

"The 2030 Agenda for Sustainable Development stressed the importance of Geospatial Information and Earth Observations (EO) to inform the SDG Targets and Indicators. EO combined with hi-resolution in-situ data provides an opportunity to understand and improve the dynamic behaviors of the earth's processes in great detail that was never possible before. Multiple data sources enable us to grasp different pieces of information which each partially characterize what is happening on the earth's surface differently. Combining all these datasets, however, is a challenging task from a data analysis point of view. In the current big data scenario, we have massive amounts of data generated by complex underlying physical processes which are difficult to monitor. The extraction of meaningful information from these large-scale heterogeneous datasets is an open problem. The second big challenge is then to find out levels at which these datasets can be combined - raw or pixel level, feature level, or at the decision level. Moreover, most of the current data fusion techniques are based on data-driven Artificial Intelligence (AI) methods which fail to provide transparency and explainability of the AI algorithm. In other words, it does not provide us with a clear physical meaning of what the results mean and why the model makes that prediction. The Ph.D. project aims at understanding some of the challenges and developing data analysis methods to improve the characterization of the earth's physical processes using explainable data fusion techniques. There are three main research objectives of the PhD. First, understanding methods to select the most relevant datasets for any project for effective analysis by incorporating domain knowledge and state-of-the-art feature selection algorithms. Second, to investigate the data fusion techniques to combine these most relevant datasets, and third, to explore ways to quantify the degree of confidence of the fused product thereby dealing with uncertainty associated with remote sensing products. "