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Short Abstract

Deep Time’s human-in-the-loop model combines spatial AI and citizen mapping to validate 70,000 habitat polygons across 5000 km² of UK landscape. Citizen–AI fusion achieved 88 % accuracy vs Machine Learning datasets, proving people remain the essential validators in AI-driven environmental science.

Abstract

Deep Time shows how human-in-the-loop AI can transform citizen-generated data into scientifically robust, policy-ready evidence. Developed by DigVentures, the platform integrates spatial AI baselines with collective human interpretation, enabling citizens to refine and validate Earth Observation data across 5 300 km² of UK landscape.

Machine-learning habitat maps from Living England provided the initial training layer; citizens then improved these outputs through Deep Time’s participatory GIS and online learning system. Each citizen-drawn polygon was automatically cross-checked against AI predictions and scored for fidelity, accuracy, completeness, and recency via a live QA dashboard. Results showed 88 percent concordance with ML outputs, and 60 percent of grids surpassed machine-only accuracy in complex habitats such as peatlands and coastal zones.

This human-in-the-loop approach closes known AI gaps—limited training data, weak contextual reasoning, and low trust—by embedding citizens as co-creators of validated datasets. Mission Leaders and partner ecologists oversee tiered review cycles, creating analysis-ready outputs for Natural England, National Landscapes, and Wildlife Trusts. By combining machine efficiency with human judgement and local knowledge, Deep Time builds datasets that are both technically credible and socially legitimate.

The project demonstrates that AI and citizen science are not competing paradigms but complementary systems. Linked through a human-centred validation loop, they jointly enhance data quality, equity, and impact—offering a replicable framework for distributed environmental monitoring aligned with European data-validation standards.

https://digventures.com/projects/deep-time/