Monday 24 June

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Atrium

Poster titles and authors are available below

Atrium

Andy Cunliffe (University of Exeter), Tom Nicholson (University of Exeter)

Henderson Lecture Theatre

Why is EO so important to global challenges?

Donna Lindsay (Ordnance Survey)

Henderson Lecture Theatre

Icebergs, Genealogy and Jigsaw Puzzles

Ben Evans (British Atlantic Survey)

Parallel session 1A - Henderson Lecture Theatre

11:00. Mapping human impacts on rangelands with Earth observation and quantile regression forests. Guy Lomax (University of Exeter)

11:15. Multi-spatial temporal remote sensing and machine learning for mapping land use on peatlands. Wahaj WH Habib (Trinity College Dublin); John Connolly (Trinity College Dublin)

11:30. The beat of the savanna: Towards non-invasive vibrational monitoring of sub-Saharan wildlife. Tarje Nissen-Meyer (University of Exeter); Rene Steinmann (GFZ Potsdam); Beth Mortimer (University of Oxford)

11:45. Bridging the Data Gap: combining Synthetic and Satellite Data for Agricultural Land Monitoring. Enes Hisam (GMV NSL); Rossana Gini (GMV NSL)*; David Miraut Andrés  (GMV); Raúl Rodríguez Juárez (GMV); Marta Toro Bermejo (GMV); David de  la Fuente Blanco (GMV); Jesús Gimeno (Universidad de Valencia); Marcos  Fernández Marín (Universidad de Valencia)

12:00. Can we use satellite imagery and artificial intelligence to automate habitat monitoring in key biodiversity areas? Andy Wallace (ATOS)

Parallel session 1B - Conference Rooms 1 & 2

11.00. Exploring Sentinel-1-based Change Detection Approaches for Updating the Antarctic Coastline. Aliaksandra S. Skachkova (British Antarctic Survey)

11.15. Predicting Zostera marina seagrass carbon stock using machine learning and earth observation to support carbon accounting. Nicola Wilson (University of Exeter)

11.30. Prediction of diagnostic pigments in the ocean by machine-learning. Angus Laurenson (Plymouth Marine Laboratory)

11.45. Evaluation of accuracy and uncertainty for satellite-derived coastal  bathymetry prediction using Machine Learning and Hybrid Spatial models. Paul Harris (Rothamsted Research); Conor Cahalane (Maynooth University); Xavier Monteys (GSI); Gema Casal (Maynooth University)

12.00. Reconstructing the spatiotemporal evolution of the global interior ocean’s anthropogenic carbon sink using deep learning. Tobias F Ehmen (University of Exeter)*; Neill Mackay (University of Exeter); Andrew Watson (University of Exeter)

Atrium

Henderson Lecture Theatre

Challenges and Solutions for using Machine Learning with Earth Observation Data

David Moffat (Plymouth Marine Laboratory)

Henderson Lecture Theatre

The AI4Peat partnership: using AI to monitor peatland health

Martha Tabor (Defra), Sam Richardson (Natural England), Nick Tomline (Natural England), Phil Shea (Defra)

Parallel session 2A, Henderson Lecture Theatre

14:30. Pivoting Perspectives: Adapting Medical Artificial Intelligence Techniques for Environmental Sample Analysis. Claire Barnes (Swansea University)*; Eloise Smith (Swansea University)

14:45. Using AI with satellite-borne cloud radar data. Cyril J Morcrette (Met Office)

15:00. Object Detection on Satellite Imaging for Sustainable Water Harvesting Placements in Maasai Region, Tanzania. Roshan Taneja ; Yuvraj Taneja

15:15. Improving the Proactive and Reactive Maintenance of Water Pipe Networks using InSAR and Machine Learning. Andrew Watson (SatSense); Sarah Douglas (SatSense); Tom Ingleby (SatSense)

Parallel session 2B, Conference Rooms 1 & 2

14:30. Detection of landslides from space using open-source data. Kathryn Leeming (BGS)

14:45. DisasterScope: A Dataset and RTMDet-based Methodology for Object Detection in Disaster-Related Remote Sensing Images. Zhipeng Liu (University of Exeter) 

15:00. Machine Learning and historical impact data for predicting Tropical Cyclone impacts. Elizabeth Galloway (University of Exeter)

15:15. Rapid Identification of Flood-Affected Areas Using a Sequential Ensemble Algorithm. Rui Guo (Beijing Normal University) 

Atrium

Poster titles and authors are available below

Henderson Lecture Theatre

Andy Cunliffe

Henderson Lecture Theatre

Examining aboveground biomass and forest change in the Region of Cantabria, Spain using multiple satellite sensors and machine learning tools in ArcGIS Pro.

Rami Alouta (ESRI)

Parallel session 3A, Henderson Lecture Theatre

09:50. Exploring the potential of freely available remote sensing data and  machine learning to monitor above ground tree biomass in the Miombo  woodlands of Zambia. Kennedy Kanja (Lancaster University)

10:05. Using an ensemble of high-dimensionality convolutional neural networks  to map continent scale forest cover and changes over time. James Brown (Geospatial Intelligence Ltd); Rob Coorey (Geospatial  Intelligence Pty Ltd); Jeremy Repetto (Geospatial Intelligence Pty Ltd);  Sam Lewis (Geospatial Intelligence Pty Ltd)

10:20. Changes in forest coverage in Mexico and their relationship with soil moisture and organic carbon content. Luis Felipe Castelblanco Rivera (Universidad Nacional Autónoma de  México)*; Mario Antonio Guevara Sanatamaría (Universidad Nacional  Autónoma de México)

10:35. Coffee Break, Atrium

11:00. Mapping the Spatio-Temporal Distribution of Burned Areas in the Amazon from 2001 to 2020: An Ensemble Modelling Approach. Oscar Rodriguez de Rivera Ortega (University of Exeter); Paula Moraga  (KAUST); Jonatan Gonzalez (Universidad Miguel Hernandez); Mohamed Abid  (KAUST)

11:15. Tree genera classifications using Sentinel-2 time-series extracted from PlotToSat. Milto Miltiadou (University of Cambridge); Stuart Grieve (Queen Mary  University of London); Paloma Ruiz-Benito (Universidad de Alcalá); Julen  Astigarraga (Universidad de Alcalá); Verónica Cruz-Alonso (Universidad  de Alcalá); Emily Lines (Department of Geography, University of  Cambridge)

11:30. Building trust in Machine Learning for commercial forest carbon applications. Hugh Graham (Permian Global, University of Exeter), Andy Cunliffe (University of Exeter), Chris Philipson (Permian Global)

Parallel session 3B, Conference Rooms 1 & 2

09:50. Insight4EO: Onboard Intelligence for Advanced Decision-Making and EO Data Processing. Michael Wilby (Deimos Space UK); Juan Ignacio Bravo Pérez-Villar  (Deimos Engineering and Systems); Álvaro Morón Elorza (Deimos  Engineering and Systems); Enrique Sepúlveda Jorcano (Deimos Engineering  and Systems); Robert Hinz (Deimos Engineering and Systems); Rohaan Ahmed  (Deimos Space UK)

10:05. Application of RandLANet to the semantic segmentation of low-density photogrammetric 3D point clouds. Lucy Main (Ordnance Survey)

10:20.GlobeNet: Geo-Foundation model development. Steve Coupland (Ordnance Survey)

10:35. Coffee Break, Atrium

11:00. The Earth Observation Data Life Cycle and its Environmental Impacts. Markus Mueller (University of Exeter)

11:15.  The Need for Expert-Guided Deep Learning to Digitally Monitor Pollen in the Environment. Ann Power (University of Exeter); Claire Barnes (Swansea University); Eloise Smith (Swansea Univerity)

11:30. Land cover classification in the UK using geospatial foundation models. Remy Vandaele (University of Exeter)*; Edward Pope (Met Office); Hywel T P Williams (University of Exeter) [slides]

Atrium

Henderson Lecture Theatre

Remote-sensing Foundation Models for Generalist Geospatial Artificial Intelligence

Anne Jones (IBM)

Parallel session 4A, Henderson Lecture Theatre

13:35. Training accuracy in building footprints detection for OpenStreetMap. Anna Zanchetta (The Alan Turing Institute); Kshitij Sharma  (Humanitarian OSM Team); Emran ALCHIKH ALNAJAR (Humanitarian OSM Team)

13:50. Estimating vehicle flows in global cities for environmental applications  using high-resolution satellite imagery and deep learning. Annalisa Sheehan (St George's, University London)

14:05. Is a Satellite Image Worth a Thousand Data Points? Comparing Machine  Learning Approaches for Predicting Social and Environmental Inequalities  Across England. Antje Barbara Metzler (Alan Turing Institute)*; Dani Arribas-Bel (Alan  Turing Institute); Martin Fleischmann (Charles University)

14:20. Optimising biodiversity through machine learning. Kamaran Fathulla (University of Aberdeen); Annett Frick (LUP – Luftbild  Umwelt Planung GmbH); Nastasja Scholz (LUP – Luftbild Umwelt Planung  GmbH)

Parallel session 4B, Conference Rooms 1 & 2

13:35. Leveraging Deep-Learning Approaches with Spatial Context for Enhanced  Surface Solar Irradiance Estimation from Third-Generation Geostationary  Satellite Imagery. Vadim Becquet (Mines Paris PSL Centre OIE); Hadrien Verbois (Mines  Paris PSL Centre OIE); Philippe Blanc (Mines Paris PSL Centre OIE);  Yves-Marie Saint-Drenan (Mines Paris PSL Centre OIE)

13:50. Deep Learning for Improved Phase Unwrapping. Eilish O'Grady (University of Leeds); Andy Hooper (University of  Leeds); David C Hogg (University of Leeds); Matthew Gaddes (University  of Leeds)

14:05. Phase-Noise Suppression in InSAR Phase Using a Complex-Valued Image-Translation Deep Learning Model. Gopal Singh Phartiyal (School of Earth and Environment, University of  Leeds); Andy Hooper (School of Earth and Environment, University of  Leeds); David C Hogg (University of Leeds); Eilish O'Grady (University  of Leeds); Matthew Gaddes (University of Leeds); Milan Lazecky  (University of Leeds)

14:20. Learning-Based Predictive Scheduling for Multiple Agile Satellites with Task Arrivals During Mission. Abhijit Chatterjee (University of Exeter); Ratnasingham Tharmarasa (McMaster University)

Henderson Lecture Theatre

!!! 30 people max !!!