Conference Title: SPIE Remote Sensing 2020
Dates: 21st – 24th September 2020
Location: Edinburgh International Conference Centre

You are invited to participate in the 2020 SPIE Remote Sensing symposium.

Over the past 26 years, SPIE Remote Sensing has become the largest and most prestigious annual international meeting on this subject in Europe.

Each year, comprehensive coverage of scientific topics including remote sensing applications, sensors, systems, and satellite platforms are presented.

With more than 25 countries represented at every meeting, the event provides a unique opportunity for scientists, engineers, programme managers and policy makers from around the world to learn about the trends, recent developments and achievements in the area of remote sensing.

Attendees exchange ideas, as well as present and discuss the most recent developments and applications.

This year will offer nine conferences covering the most exciting and prosperous areas in the field of remote sensing:

• Remote Sensing for Agriculture, Ecosystems, and Hydrology
• Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions
• Sensors, Systems, and Next-generation Satellites
• Remote Sensing of Clouds and the Atmosphere
• Environmental Effects on Light Propagation and Adaptive Systems
• Microwave Remote Sensing: Data Processing and Applications
• Image and Signal Processing for Remote Sensing
• Remote Sensing for Environmental Remote Sensing/GIS Applications
• Remote Sensing Technologies and Applications in Urban Environments

The conferences are designed to meet the scientific, technical, and particularly the business needs of the remote sensing community and each one will include oral and poster presentations with top researchers and company representatives as invited speakers.

Remote Sensing systems create a tremendous amount of data and machine learning and deep learning methods are widely seen as powerful instrument to solve the problems that ‘big data’ creates. So, during this 2020 symposium special sessions and training related talks concerning machine learning based solutions will be especially encouraged.