Excited to share our new preprint: "Better Together: Evaluating the Complementarity of Earth Embedding Models"! Short summary in this thread! arxiv.org/abs/2605.18667

Better Together: Evaluating the Complementarity of Earth Embedding Models

Earth embedding models transform Earth observation data into embeddings uniquely tied to locations on the Earth's surface. These models are typically evaluated in isolation, comparing the downstream t...

arxiv.org

Our perspective on deployment-centric, multimodal AI beyond vision and language is now out in Nature Machine Intelligence! www.nature.com/articles/s42...

Towards deployment-centric multimodal AI beyond vision and language - Nature Machine Intelligence

Multimodal AI combines different types of data to improve decision-making in fields such as healthcare and engineering, but work so far has focused on vision and language models. To make these systems...

nature.com

Can LLMs do our 'digital dishes'? Or in other words, what are tedious, small tasks that researchers often face and could be automated by LLMs? We've worked on one such task: extracting dataset metadata and converting these to a single format to build a dataset knowledge base. [1/5]

Great read from Michael Pocock: Linking remote sensing, citizen science data and AI could transform environmental monitoring | UK Centre for Ecology & Hydrology www.ceh.ac.uk/news-and-med...

Linking remote sensing, citizen science data and AI could transform environmental monitoring

In our new paper, published in Ecological Solutions and Evidence, we provided a perspective on how three areas of science – remote sensing, citizen science, and machine learning (a form of AI) – could...

ceh.ac.uk

Published last week at the CVPR FGVC Workshop; our paper on "Predicting butterfly species presence from satellite imagery using soft contrastive regularisation". PDF (with links to data/code) available here: openaccess.thecvf.com/content/CVPR...

In our latest perspective article, we outline how ML can overcome 4 current obstacles for large-scale, high-resolution monitoring of protected areas. doi.org/10.1002/2688... Hope this stimulates the conversation and provides a pathway of how ML research can be applied for monitoring PAs at scale.

🎯 How can we empower scientific discovery in millions of nature photos? Introducing INQUIRE: A benchmark testing if AI vision-language models can help scientists find biodiversity patterns- from disease symptoms to rare behaviors- hidden in vast image collections. Thread👇🧵

Using computer vision and #MapReader software, we analysed the loss of field boundaries across the #PeakDistrict Since the 1950s, the White Peak has seen a 12% reduction-551 km lost from an original 4,814 km. A stark reminder of landscape change 📊🌿 storymaps.arcgis.com/stories/5c89... #maps #GIS

Mapping the Past Against the Present

Past Landscapes, Present Discoveries: A Data Science Approach to Rediscovering Field Systems from Historic Ordnance Survey Maps

storymaps.arcgis.com