This webinar brings together a diverse group of scholars and experts to discuss some of the inequity and systemic vulnerabilities of covid-19 pandemic. Nathaniel Osgood serves as Professor in the Department of Computer Science at the University of Saskatchewan, and Director of the Computational Epidemiology and Public Health Informatics Laboratory. His research focuses on combining tools from Systems Science, Data Science, Computational Science and Mathematics to inform decision making in health & health care. Dr. Osgood serves as Chief Research Advisor for the Saskatchewan Centre for Patient Oriented Research and has contributed to or co-led over a dozen initiatives involving people with lived experience with dynamic modeling, machine learning and/or big data collection efforts. Dr. Osgood served as the technical director of COVID-19 modeling for the Province of Saskatchewan from March 2020-April 2021.

We propose an Ontology-Based Information Extraction (OBIE) system to automate the extraction of the criteria and values applied in Land Use Suitability Analysis (LUSA) from bylaw and regulation documents related to the geographic area of interest. The results obtained by our proposed LUSA OBIE system (land use suitability criteria and their values) are presented as an ontology populated with instances of the extracted criteria and property values. This latter output ontology is incorporated into a Multi-Criteria Decision Making (MCDM) model applied for constructing suitability maps for different kinds of land uses. The resulting maps may be the final desired product or can be incorporated into the cellular automata urban modeling and simulation for predicting future urban growth. A case study has been conducted where the output from LUSA OBIE is applied to help produce a suitability map for the City of Regina, Saskatchewan, to assist in the identification of suitable areas for residential development. A set of Saskatchewan bylaw and regulation documents were downloaded and input to the LUSA OBIE system. We accessed the extracted information using both the populated LUSA ontology and the set of annotated documents. In this regard, the LUSA OBIE system was effective in producing a final suitability map.

Climate scientists at Agriculture Canada labs have been building and refining an artificial intelligence-based program for the past couple of years. And with severe drought continuing to plague parts of the Prairies, along with Great Plains states, they can apply their AI program to real-world conditions. Drought monitor programs have been in place in most developed countries for decades. But a monitor program simply provides current weather events and conditions. But Trevor Hadwen, a climate specialist at Agriculture Canada's research lab in Regina, explains their program, called Drought Outlook, is built to forecast drought conditions and looks 30 days into the future.

A Regina-based tech startup says it's the first company to use artificial intelligence (AI) to detect leaks at oil wells and pump jacks. In the past, oil and gas companies have used staff to drive to oil wells to inspect them for any issues, such as leaks. One solution is using remote cameras to monitor oil wells, but it results in hundreds or thousands of photos being taken that have to be inspected by employees. Founded in 2018, Wave9 takes the arduous task of inspecting those photos and hands it off to AI. Cameras and sensors placed on pump jacks are processed by the software. The user can then be alerted to issues through apps that run on tablets and smartphones.

Innovation Saskatchewan plans to use robots to help reduce the amount of solid waste that is placed in landfills. The group unveiled its new technology at Innovation Place on the University of Regina campus on Tuesday from its two winners of the 2019 Innovation Saskatchewan challenge. Prairie Robotics used artificial intelligence and cameras to capture waste data in real time while researchers at the U of R created a system designed to handle Saskatchewan's extreme weather all while being able to weigh moving vehicles. The province expects the two solutions to be beneficial in help to reduce sold waste dumped into landfills by 30 per cent by 2030. "Our tech community has developed a tracking and reporting mechanism using artificial intelligence which can reduce the expense of landfill operations and lead to long-term environmental efficiencies," Innovation Minister Tina Beaudry-Mellor said.

It is the middle of the winter and a six-month-old child is brought with acute respiratory distress to a nursing station in a remote community in the Canadian North. The nurse realizes that the child is seriously ill and contacts a pediatric intensivist located in a tertiary care centre 900 kilometres away. The intensivist uses her tablet to activate a remote presence robot installed in the nursing station and asks the robot to go to the assessment room. The robot autonomously navigates the nursing station corridors and arrives at the assessment room two minutes later. With the help of the robot's powerful cameras, the doctor "sees" the child and talks to the nurse and the parents to obtain the medical history.

It is the middle of the winter and a six-month-old child is brought with acute respiratory distress to a nursing station in a remote community in the Canadian North. The nurse realizes that the child is seriously ill and contacts a pediatric intensivist located in a tertiary care centre 900 kilometres away. The intensivist uses her tablet to activate a remote presence robot installed in the nursing station and asks the robot to go to the assessment room. The robot autonomously navigates the nursing station corridors and arrives at the assessment room two minutes later. With the help of the robot's powerful cameras, the doctor "sees" the child and talks to the nurse and the parents to obtain the medical history.

The transition from traditional to self-driving cars is likely to take years if not decades, and Saskatchewan's least favourite form of precipitation could make that process even longer, according to one expert. Autonomous vehicles use a variety of sensors to detect objects in the environment, and the province's climate is likely to prove more challenging than that of the southern United States, said Jonathan Cliffen, an engineer with 3M. "I think the value proposition is going to be a challenge at first," said Cliffen, who leads the Maplewood, Minn.-based materials company's efforts to build signs and road markings optimized for self-driving cars in Canada. "Much like owning a motorcycle, it's going to be a two-thirds-of-the-year solution, right? Autonomous vehicles, you're going to be able to use them year-round in manual mode and hopefully six to eight months of the year you're going to be able to use it in autonomous mode."