Artificial Intelligence and Machine Learning based mapping, derived from remotely sensed data, presents a significant opportunity within the environmental field. These technologies can replace manual digitising of features in imagery, and map larger areas to a finer granularity than is possible using traditional techniques such as Geospatial Information System (GIS) based analysis. As with any new technology however, there are reservations to its use 'on the ground'. This scepticism is to some degree warranted, due to the lack of standardised methods for comparing AI algorithms to ground results in a methodical manner. To address this challenge, a consortium, led by EOLAS Insight and partner organisation Scotland's Rural College (SRUC), will define a framework and candidate standard for direct comparison of AI / ML generated mapping algorithms. The consortium will define application-dependant standard classes for algorithms, and perform accurate ground-based mapping of baseline sites. Organisations with an active interest in the field can determine key performance metrics of their algorithms by analysing these baseline sites and comparing their results to pre-ground truthed data. This framework will be developed using nature markets as an initial use case due to the high requirement for trust in the outputs, emerging best practice, and its position as a high growth market suitable for SME involvement. The consortium, including Omanos Analytics, Agrimetrics, Highlands Rewilding and AECOM, and a working group of interested governmental departments, nature-based agencies and global investment managers, will overcome a key challenge for geospatial AI algorithm developers: user trust in the technology. This will be achieved through provision of educational resources around the use of AI generated mapping, and an established framework for measurement and comparison of algorithm quality. This will be openly published to allow developers to verify their results against pre- ground truthed data. This consortium will, through working groups, ensure a peer reviewed outcome which meets the needs of the market more widely. Through engagement and standard definition the group will mature the use of emerging AI technologies within the nature markets use case, serving as an example for wider adoption of these techniques within the environmental and geospatial sectors. By providing open and transparent methodologies for assessing the quality of AI / ML derived data products this project will increase overall trust in AI as a mechanism for mapping features in combination with remotely sensed data, benefitting the wider community.

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Awaiting Public Project Summary

Awaiting Public Project Summary

The CAPRI project will design & deliver a complete, market ready, mobility service deployable in urban scenarios using trusted secure PODs and systems supported with a 'complete package' of viable business cases, legal, regulatory, insurance recommendations to enable quick and easy deployments. A series of trial deployments demonstrate increasingly complex POD-based mobility services. Whilst addresing all CCAVs priority areas, including cyber security of vehicle and data validated real-time controld systems, our focus is on innovative business models based around POD mobility services.

Awaiting Public Project Summary

Awaiting Public Summary

Awaiting Public Summary

A simple advancement of BMS will be better integration of façade control with BMS. This requires facades that are more readily controllable and greater use of automated facades. This project will overcome common barriers to the use of automated facades and give guidance on operating strategies for façade shading, ventilation and other aspects of functionality. Barriers to be broken down by this project include issues of who designs, who constructs, who is the integrator of the different elements and questions about how automated facades perform in practice.