On-Demand Webinar

Crowd-Driven AI to Protect the Planet

Learn how SAS and IIASA are using computer vision to help save the Amazon rainforest.

About the webinar

The Amazon rainforest is home to the greatest variety of plant and animal species in the world. It plays a vital role in absorbing billions of tons of CO2 from our atmosphere every year.

But today, large areas of the Amazon are in danger of disappearing because of deforestation for timber extraction, crop production and infrastructure development.

During this webinar, we’ll show how SAS and IIASA are collaborating to develop a computer vision system to detect early signs of deforestation in the Amazon.

We’ll focus on the approach and the choices we made along the modeling cycle to use AI to address this global problem.

What you'll learn:

  • How we used refined and relevant data.
  • Ways deep learning models were accurately applied.
  • The value of using composite AI for complex challenges.

Have a SAS profile? To complete this form automatically Sign In

*
*
*
*
 
 

All personal information will be handled in accordance with the SAS Privacy Statement.

 
  Yes, I would like to opt-in to receive occasional emails from SAS Institute Inc. and its affiliates about SAS products and services.
 
 

About the Experts


Ian McCallum
IIASA 

Research Leader of Novel Data Ecosystems for Sustainability, IIASA

Ian McCallum contributes to numerous research projects applying geospatial analysis to global environmental problems. He helped develop the Geo-Wiki, a globally recognized citizen science platform for Earth observation. His research interests lie along areas of environmental monitoring, earth observation, citizen science, the carbon cycle and natural hazards.

Prior to joining IIASA, he worked for both forest industry and forest consulting firms in central and western Canada and the United States.


Gerhard  Svolba
SAS

Analytical Solutions Architect and Data Scientist, SAS

Gerhard Svolba, PhD, is involved in numerous analytics and data science projects that include demand forecasting, analytical CRM, risk modeling, fraud prediction and production quality. He has worked on projects ranging from business and technical conceptual considerations to data preparation and analytical modeling across industries. He is the author of Data Preparation for Analytics Using SAS®, Data Quality for Analytics Using SAS and Applying Data Science: Business Case Studies Using SAS. Svolba also teaches data science methods at the University of Vienna, the Medical University of Vienna and other business schools. See his contributions on Medium | LinkedIn and Github.