Distributed Machine Learning for Automatic Annotation and Analyses of Vast Distributed Image Archives

这个博士项目是为了解决利用分布式机器学习技术（如联邦学习）有效分析大量高分辨率图像内容的挑战。这些图像是由新兴的传感器生成的，并存储在跨越地球的位置。如果可以将所有图像集中到一个中心位置，则可以使用集中式机器学习来自动注释图像，从而生成多种类型（汽车，树木，建筑物，桥梁等）的地理时间定位对象列表。然后可以以这样的方式处理该列表，例如，可以识别改变和/或定位查询对象的类似对象。由于图像单独很大并且通常数量也很大，因此通信带宽考虑意味着将所有图像发送到一个集中位置是不实际的。关于本地数据集的隐私可能会使传输到中央位置在法律上不可行。所需要的是一种机制，通过这种机制，可以在（真实的）分布式异步设置中模拟（假设的）集中式过程。这将需要开发基础设施以促进分布式存储和查询（例如，分布式空间索引、学习使能的智能查询等）以及分布式算法的开发（例如，该项目旨在创建非平凡的数据科学和计算解决方案，提供分布式机器学习机制，从而可以在（真实的）分布式环境中模拟（假设的）集中式流程。这将需要开发基础设施以促进分布式存储和查询（例如，分布式空间索引、学习使能的智能查询等）以及分布式算法的开发（例如，Map-Reduce、联合学习等），其可以在这样的分布式、异步设置中操作。

This PhD project is to tackle the challenge of efficiently analysing the content of vast volumes of high-resolution imagery with distributed machine learning techniques such as federated learning. The images are generated with emerging sensors and stored in locations that span the Earth. Were it possible to bring all the imagery to one central location, it would be possible to use centralised machine learning to auto-annotate the imagery and thereby generate a list of geo-temporally localised objects of each of many types (cars, trees, buildings, bridges etc). This list could then be processed in such a way that, for example, changes could be identified and/or similar objects to a query object localised. Since the images are individually large and typically also very large in number, communications bandwidth considerations mean that it is not practical to send all the imagery to one centralised location. The privacy regarding local datasets may render the transmission to a central location legally infeasible. What is needed is a mechanism whereby the (hypothetical) centralised processes can be emulated in a (real) distributed, asynchronous setting. This will require development of both the infrastructure to facilitate distributed storage and querying (via e.g., distributed spatial indexing, learning-enabled intelligent querying, etc) as well as development of distributed algorithms (via e.g., map-reduce, federated learning, etc) that can operate in such a distributed, asynchronous setting.The project aims to create non-trivial data science and computing solutions that offer a distributed machine learning mechanism whereby the (hypothetical) centralised processes can be emulated in a (real) distributed setting. This will require development of both the infrastructure to facilitate distributed storage and querying (via e.g., distributed spatial indexing, learning-enabled intelligent querying, etc) as well as development of distributed algorithms (via e.g., map-reduce, federated learning, etc) that can operate in such a distributed, asynchronous setting.