ml4eo 2024

Identifying deforestation drivers in Cameroon using deep learning and Earth observation data

Amandine Debus (University of Cambridge), Emilie Beauchamp (International Institute for Sustainable Development), Justin Kamga (Forêts et Dévelopmments Rural, FODER), Astrid Verhegghen (European Commission Joint Research Centre & ARHS Developments Italia S.R.L), Christiane Zébazé (Forêts et Dévelopmments Rural, FODER), Emily R. Lines (University of Cambridge)

Towards a framework for uncertainty propagation from satellite data to land cover maps and their downstream application

Anna Pustogvar (National Physical Laboratory, University of Leicester), Bernardo Mota (National Physical Laboratory), Samuel E. Hunt (National Physical Laboratory), Heiko Balzter (University of Leicester, National Centre for Earth Observation)

Living England: an earth-observation derived national scale habitat map

Becky Trippier (Natural England)

PeaSat: using satellite imagery to estimate yield of vining peas

Ben J Hockridge (ADAS)

Estimating Canopy Height in Tropical Forests: Integrating Airborne LiDAR and Multi-Spectral Optical Data with Machine Learning

Brianna J Pickstone (University of Exeter)

From pixel to peat: Mapping England’s Peatland  

Craig C Dornan (NE)

Rates and Drivers of Cliff Erosion in England from over 20 Years of LiDAR Observations Erosion

Cristina Coker (University of Exeter)

Catastrophe modelling of health costs and asset losses due to future tsunamis over Sumatra and Java, Indonesia

Dimitra M Salmanidou (University College London)

Using continual pretraining with a geospatial foundation model

Geoffrey J Dawson (IBM), Chris Dearden (STFC), Andrew Taylor (STFC), Helen Tamura-Wicks (IBM Research), Paolo Fraccaro (IBM UK), Anne Jones (IBM Research)

A remote sensing foundation model for the British Isles

Helen Tamura-Wicks (IBM Research), Andrew Taylor (STFC), Chris Dearden (STFC), Geoffrey J Dawson (IBM), Anne Jones (IBM Research), Paolo Fraccaro (IBM UK)

Application of machine learning to forecast agricultural drought impacts for large scale sub-seasonal drought monitoring in Brazil

Joseph W Gallear (Rothamsted Research), Marcelo Valadares Galdos (Rothamsted Research), Marcelo Zeri (CEMADEN), Andrew Hartley (Met Office)

The Blue Belt Programme: Using Earth Observation Data to Support Global Marine Environmental Protection

Lewis Brady (Marine Management Organisation), Emma Harvey (Marine Management Organisation)

Machine Learning Volcanic Ash Detection

Måns Holmberg (Met Office), Cameron Saint (Met Office)

High Impact Weather in the Mid-Latitudes: A Neural Network Approach to Identifying Dry Intrusion Outflows  

Owain L Harris (University of Exeter)

Potential of Renewable Distributed Energy Resources using Computer Vision and Earth Observation Data

Owen G.W. Saunders (University of Exeter), Cesar Angeles (University of Exeter)

Satellite detection of coal mine methane emissions using machine learning  

Sarah Shannon (Ember), Sabina Assan (Ember)

Emulating melt ponds on sea ice with neural networks

Simon Driscoll (University of Reading)

Complex dynamical insights to air quality interplay in urban spaces: A case of cities co movements and comparison

Syed Shariq Husain (OP Jindal Global University)

A machine learning based climatology of dissolved organic carbon

Thelma Panaïotis (National Oceanography Centre), Jamie Wilson (University of Liverpool), BB Cael (National Oceanography Centre)

Hybrid ANN and Object-Based Image Analysis for Slum Detection and Monitoring in Abidjan and Karachi: An Operational Approach

Tomáš Bartaloš (GISAT), Jan Kolomazník (GISAT)