It is one world. And it's in our care. For the first time in the history of humanity, for the first time in 500 million years, one species has the future in the palm of its hands.

- Sir David Attenborough

Rapid mapping of 3D environments

In April 2017, in response to an industry-posed challenge, Dr Adrian Clark and I developed a system for capturing video of the entire 4π steradians. The problem was testing such a system without getting operators in the field of view and to truly explore a 3-dimensional environment. Dr Clark, an experienced computer vision researcher, and myself, a specialist in human-machine interaction and underwater research, proposed to use computer vision processing to reconstruct a 3D model of the environment rather than just capture video of it. Furthmore, we proposed circumventing the problems of making the prototype hover by building an underwater system to capture structures such as coral reefs and wrecks, using floats within it to achieve neutral buoyancy.

The rig was tested at a marine research site in Dominica in the Caribbean during July and August 2017. The 3D models reconstructed from the images captured from the rig show accuracy and clarity at both small and large scales. Work is currently under way to optimise the procedure and increase the speed of data collection as time spent in the field is the rarest commodity in this type of research; however, the current speed of environment mapping is far greater than any other visual method currently used.

The research is spawning new collaborations with interested researchers including Coral Reef Research Unit in Essex's School of Biological Sciences, where we are now working on several new reef conservation projects.

The University of Essex has been active in underwater robotics for some years, and is keen to extend existing work on robotic fish and stereo vision systems.

ImageCLEFcoral Annotation Challenge

The increasing use of structure-from-motion photogrammetry for modelling large-scale environments from action cameras attached to drones has driven the next-generation of visualisation techniques that can be used in augmented and virtual reality headsets. It has also created a need to have such models labelled, with objects such as people, buildings, vehicles, terrain, etc. all essential for machine learning techniques to automatically identify as areas of interest and to label them appropriately. However, the complexity of the images makes it impossible for human annotators to assess the contents of images on a large scale.

Advances in automatically annotating images for complexity and benthic composition have been promising, and we are interested in automatically identify areas of interest and to label them appropriately for monitoring coral reefs. Coral reefs are in danger of being lost within the next 30 years, and with them the ecosystems they support. This catastrophe will not only see the extinction of many marine species, but also create a humanitarian crisis on a global scale for the billions of humans who rely on reef services. By monitoring the changes and composition of coral reefs we can help prioritise conservation efforts.

The data for this task originates from a growing, large-scale collection of images taken from coral reefs around the world as part of a coral reef monitoring project with the Marine Technology Research Unit at the University of Essex (currently containing over 2TB of image data of benthic reef structure).

Disagreement and Ambiguity in Language Interpretation (DALI)

Natural language expressions are supposed to be unambiguous in context. Yet more and more examples of use of expressions that are ambiguous in context are emerging. In previous work using crowdsourcing we demonstrated that ambiguity in anaphoric reference is ubiquitous, and there is frequent disagreements in annotation. Using the Phrase Detectives Game-With-A-Purpose to collect massive amounts of judgments online, we found that up to 30% of anaphoric expressions in our data are ambiguous. These findings raise a serious challenge for computational linguistics (CL), as assumptions about the existence of a single interpretation in context are built in the dominant methodology, that depends on a reliably annotated gold standard.

The goal of the DALI project is to tackle this fundamental issue of disagreements in interpretation by using computational methods for collecting and analysing such disagreements, some of which already exist but have never before been applied in linguistics on a large scale, some we will develop from scratch. Specifically, we will develop more advanced games-with-a-purpose to collect massive amounts of data about anaphora from people playing a game.

Client insight visualisation with Signal Media

The University of Essex in partnership with Signal Media Ltd are developing insight extraction and visualisation techniques to convert a stream of unstructured textual documents, e.g. news articles, into easily digestible information and present it in novel ways. The collaboration is part of a Knowledge Transfer Partnership (KTP). In January 2019, James Brill was appointed as our new KTP Associate and the team are looking forward to getting stuck into some industry-led research challenges over the coming months.

Groupsourcing: Identifying wildlife on social media

Hundreds of thousands of images are being posted to social websites, showing a unique glimpse of the underwater world. Analysis of a small subset of these images show very high accuracy of image tagging (93% were annotated correctly) which makes them useful as a primary data source for conservation research. By analysing the text associated with the uploaded images it is possible to map where different species live around the world, what they eat, what eats them and whether changes are occurring to their populations.

Social networks can be seen as decentralised and self-organised crowdsourcing systems that are becoming increasingly popular. Tasks are created by the users, so they are motivated to participate, and the natural language of the interface allows them to express their emotions whilst solving the tasks.

We call this approach groupsourcing, completing a task using a group of intrinsically motivated people of varying expertise connected through a social network.