Geospatial Data: AI & Deep LearningInfo Location Attendee Categories Additional Items Contact More Info Event Information
DescriptionGeospatial Data: AI & Deep Learning2nd Southampton Geospatial Summer School 2025 | |||||||||||||||
| Attendee Category | Cost | |||
|---|---|---|---|---|
| Registration | £1030.00 | |||
Event Location
- Location
- University of Southampton - Building 44
- Venue Details
Building 44 (Shackleton),
University of Southampton
Highfield
Southampton
SO17 1BJ
Attendee Categories
Registration
- Cost
- £1030.00
- Description
Accommodation (optional add-on): B&B rate is £48.50 per person/per night (including VAT)
University accommodation, single room for sole occupation.Meals:
Lunches are included in the tuition fee.
Additional Items
Registration – Optional Additional Extras
- B&B Accommodation (Halls) - Single Room For Sole Occupancy
- B&B Accommodation 13th July
- B&B Accommodation 14th July
- B&B Accommodation 15th July
- B&B Accommodation 16th July
- B&B Accommodation 17th July
Contact
Should you have any questions, please email geospatial@soton.ac.uk
More Information
What Will Be Covered?
Participants will gain practical and conceptual skills across key geospatial topics, including:
• UAVs, mobile sensors, and satellite Earth observation
• Real-world applications of geospatial data in environmental and urban contexts
• Field-based UAV data collection and analysis
• Geospatial AI and deep learning techniques
• Open-source GIS tools for data processing and automation
• Google Earth Engine (GEE) for scalable, cloud-based geospatial analysis
• Population mapping and global health use cases from WorldPop
• Facility tours and researcher-led demonstrations, including UAV and underwater survey technologies
What Will You Gain?
By the end of the course, participants will:
• Understand how to apply deep learning techniques to spatial data
• Gain hands-on experience using real datasets from Earth observation and UAVs
• Learn how to deploy geospatial AI models in cloud and local environments
• Engage with peers and instructors through interactive sessions and discussions
• Receive a certificate of completion
Who Should Attend?
This course is ideal for:
• Postgraduate students and early career researchers working in geography, environmental science, remote sensing, urban planning, or data science
• Professionals from the public or private sector seeking to integrate AI into spatial workflows
• Anyone with a technical background interested in geospatial applications of machine learning