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

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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.