UKHO Shoreline Navigation Project

The project will look to utilise image and LiDAR data in training a model for location recognition purposes about a coastline. The project will look to answer the questions of whether sensor location can be reliably retrieved given a dataset of observed points and camera locations. The project will also look to answer the question of whether such an observation can be distilled into key informative points, in order to reduce the impact of noisy imaging and to further generalise the outputs for a user. Geometric deep learning methods will also be explored to identify the benefit of local feature extraction in problems which involve pointcloud information.

The approaches used:

The project will first implement a system for retrieving the top likely locations of a sensor, given an observed sample and a model trained on a dataset containing observations of the coastline. The project will first utilise observed pointclouds to train a model to perform feature extraction, embedding the observations into a space that can be queried. A subsequent component of the pipeline will then perform retrieval of the possible positive locations given a query sample. The project will also utilise geometric machine learning approaches to explore the improvement of local feature learning on the pointclouds. The project will then utilise embedding models to generalise the observations and produce representations of the observed data which is more intuitive to humans, reducing the pointclouds to a set of key informative points.



Novel content:

The project novelty comes from the development of models which combine geometric machine learning and information retrieval. Further novelty also comes in the development of models which are able to leverage local and global information to provide users with a simplified representation of their observation and why the model has provided a given prediction.

The Centre will nurture 55 new PhD researchers who will be highly sought after in technology companies and application sectors where data and intelligence based systems are being developed and deployed. We expect that our graduates will be nationally in demand for two reasons: firstly, their training occurs in a vibrant and unique environment exposing them to challenging domains and contexts (that provide stretch, ambition and adventure to their projects and capabilities); and, secondly, because of the particular emphasis the Centre will put on people-first approaches. As one of the Google AI leads, Fei-Fei Li, recently put it, "We also want to make technology that makes humans' lives better, our world safer, our lives more productive and better. All this requires a layer of human-level communication and collaboration" [1]. We also expect substantial and attractive opportunities for the CDT's graduates to establish their careers in the Internet Coast region (Swansea Bay City Deal) and Wales. This demand will dovetail well with the lifetime of the Centre and provide momentum for its continuation after the initial EPSRC investment.

With the skills being honed in the Centre, the UK will gain a important competitive advantage which will be a strong talent based-pull, drawing in industrial investment to the UK as the recognition of and demand for human-centred interactions and collaborations with data and intelligence multiplies. Further, those graduates who wish to develop their careers in the academy will be a distinct and needed complement to the likely increased UK community of researchers in AI and big data, bringing both an ability to lead insights and innovation in core computer science (e.g., in HCI or formal methods) allied to talents to shape and challenge their research agenda through a lens that is human-centred and that involves cross-disciplinarity and co-creation.

The PhD training will be the responsibility of a team which includes research leaders in the application of big data and AI in important UK growth sectors - from health and well being to smart manufacturing - that will help the nation achieve a positive and productive economy. Our graduates will tackle impactful challenges during their training and be ready to contribute to nationally important areas from the moment they begin the next steps of their careers. Impact will be further embedded in the training programme with cohorts involved in projects that directly involve communities and stakeholders within our rich innovation ecology in Swansea and the Bay region who will co-create research and participate in deployments, trials and evaluations.

The Centre will also impact by providing evidence of and methods for integrating human-centred approaches within areas of computational science and engineering that have yet to fully exploit their value: for example, while process modelling and verification might seem much removed from the human interface, we will adapt and apply methods from human-computer interaction, one of our Centre's strengths, to develop research questions, prototyping apparatus and evaluations for such specialisms. These valuable new methodologies, embodied in our graduates, will impact on the processes adopted by a wide range of organisations we engage with and who our graduates join.

Finally, as our work is fully focused on putting the human first in big data and intelligent systems contexts, we expect to make a positive contribution to society's understandings of and involvement with these keystone technologies. We hope to reassure, encourage and empower our fellow citizens, and those globally, that in a world of "smart" technology, the most important ingredient is the human experience in all its smartness, glory, despair, joy and even mundanity.

[1] https://www.technologyreview.com/s/609060/put-humans-at-the-center-of-ai/