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Machine Learning for Observation Remote Sensing with the Environment Intelligence Network

20-21 April 2023

IDSAI are delighted to support The Environmental Intelligence Network with a two-day workshop to explore how machine learning can help get the most out of Remote Sensing observations.Recent years have witnessed a dramatic increase in the acquisition of remote sensing observations from satellite, aircraft and drone-based sensors, and in-situ devices. Technological advances have led to improvements in measurement resolution and precision, which is shifting the paradigm of Earth observation from data scarcity to data abundance. While these data have enormous potential for helping us achieve a range of United Nations Sustainable Development Goals, identifying the optimal approaches for handling and analysing these large datasets remains a challenge for both academia and industry. Recent breakthroughs in AI/ML offers promising solutions to these challenges, including automated identification and extraction of key observations, predicting future trends, identifying key environment factors, and dealing with noisy signals under uncertainties.


The broad aim of this workshop is to bring together remote sensing researchers, data science experts and industry partners to build links and share ideas, with a view to promote further collaboration and develop research proposals to address specific global challenges. This will be achieved by sessions that are designed to:

1. Facilitate sharing of scientific discoveries, methods and industry perspectives

2. Explore emerging applications and new opportunities for remote sensing and machine learning

3. Foster collaborations, build networks and share opportunities


Range of sessions including networking, a poster session, and themed sessions. Detailed schedule will be available at the beginning of March. To reserve a place, click on EventBrite.

How to participate:

There are several different ways to participate in this event:

• Attending the workshop as a delegate

• Oral research presentation in the area of machine learning for Earth observation

• A poster presenting preliminary results or ‘works in progress’ (ideal for PGR students)

• Chairing/contributing to a 1-2 hr thematic discussion session

Please note that this is an expression of interest and subject to a selection process. We will contact you regarding your specific input closer to the time.


Please contact Research Networks for any queries.


Organisation Committee:

Steven Palmer
Chunbo Luo
Christy Judd
Ana Duarte
Emily Paremain
Helen Chapman
Karen Anderson
Bob Brewin
Andy Richards
Tom Powell
PhD students - tbc.



Thanks goes to the following for their support in organising this event:-

  • Environmental Intelligence Network
  • Exeter Marine.