Projects

IOTO
This project will track and evaluate parliamentary members and their involvement and activities on environment protection and sustainable development. It will leverage public data with tools such as sentiment analysis to draw conclusions on these topics. The evaluation will focused on data from 2021 (or 2022, depending on availability) and we will start by looking at the activities of the members of Standing Committee on Environment and Sustainable Development.
Awesense
At Awesense, we’ve been building a platform for digital energy with the goal of allowing easy access to and use of electrical grid data in order to build a myriad of applications and use cases for the decarbonized grid of the future, which will need to include more and more distributed energy resources (DERs) such as rooftop solar, batteries as well as electric vehicles (EVs).
Cenovus Energy
Cenovus is a Canadian-based integrated energy company headquartered in Calgary. We’re committed to maximizing value by sustainably developing our assets in a safe, innovative and cost-efficient manner, integrating environmental, social and governance considerations into our business plans.
Innovatree Carbon Group
Model the carbon footprint of slash pile burning vs grinding for bioenergy in the forestry industry and create a web interface (using HTML) which can report those costs in real time
Multiverse Computing
This project uses machine learning techniques to analyze data regarding deforestation in the Amazon basin. In a recent blog post, Ajitesh Kumar wrote: Forest degradation is a major problem that contributes to climate change.
North Coast Skeena First Nations Stewardship Society
This project aims to determine an appropriate tissue sample size program to effectively expand Genetic Stock Identification (GSI) data which indicates proportionate stock composition. Such a program would then be applied to the estimated harvest to produce meaningful stock proportion estimates in the mixed stock recreational Chinook harvest for Pacific Fishery Management Areas managed by the North Skeena First Nations Stewardship Society.
Aerium Analytics

The ability to detect man-made objects in vegetation would aid many fields of industry including – but not limited to – search and rescue as well as agriculture. The ability to locate man-made objects such as damaged modes of transportation, camping or hiking equipment, parachutes, etc. in said vegetation aids the chances of locating missing individuals involved in these cases. While much imagery of these objects exists, imagery of them in varying degrees of repair within vegetation is limited at best making standard machine learning detection methods difficult. In such cases, focusing on the vegetation may enable the detection of such objects through machine learning methods where imagery is limited. In agriculture, farming equipment may require repair while in the field where it is easily possible to misplace equipment in vegetation. This lost equipment could damage other farming equipment while work is being done. Locating lost equipment will help limit further damage of farming equipment saving not only money, but time and production as well.

For this project, the team will be given access to aerial multispectral imagery containing a variety of man-made objects located in vegetation. The goal of this project is to develop a method which can detect these random man-made objects using machine learning and computer vision techniques while investigating the benefits of multispectral data to solving this problem. The machine learning field of focus for this problem is that of anomaly detection.

Aerium Analytics
Awesense

At Awesense we’ve been building a platform for digital energy, with the goal of allowing easy access to and use of electrical grid data in order to build a myriad of applications and use cases for the decarbonized grid of the future, which will need to include more and more distributed energy resources (DERs) such as rooftop solar, batteries as well as electric vehicles (EVs).

Awesense has built a sandbox environment populated with synthetic but realistic data and exposing APIs on top of which such applications can be built. As such, what we are looking for is to create a collection of prototype applications demonstrating the power of the platform. Given the synthetic nature of the dataset we can make available, this would be more of a “deliver a method (and implementation of it)” type project than a “deliver insights” type project.

This involves coding some analyses and visualizations on top of said data and APIs. It would require good data wrangling + statistics + data visualization skills to design and then implement the best way to transform, aggregate and visualize the data for the use case at hand (see below). The data access APIs are in SQL form, so SQL querying skills would also be required. Beyond that, the tools and programming languages used to create the analyses and visualizations would be up to the students. Typical ones we have used include BI tools like Power BI or Tableau and notebooking applications like Jupyter or Zeppelin combined with programming languages like python or R.

If the participants don’t have any electrical background, we can teach enough of it to allow handling the given use case. For this year’s project, we have chosen a use case entitled “EV charger capacity study”. At a high level, this entails determining how many new EV chargers could be installed in a particular portion of the electrical distribution grid without overloading the capacity of the grid infrastructure at that location. This would allow distribution grid planners to determine whether or not to approve requests for “interconnection” of EV chargers; it would also allow them to plan for needed infrastructure upgrades to support more EV chargers in the future.

Awesense
Big River Analytics

Local labour market information (LMI), including employment, unemployment, and participation rates, are important for planning activities conducted by municipal and First Nation governments, service providers, and many more organizations. Census of Population (Census) data, published by Statistics Canada (StatCan), provides LMI at the Census Division (CD) and Census Subdivision (CSD) levels. However, the Census is only conducted every five years. This project asks participants to build a model which estimates intercensal LMI at the CD and CSD levels.

LMI are not publicly available at the CD and CSD levels of geography for years when the Census is not conducted. StatCan’s Labour Force Survey provides data on a monthly basis. However, StatCan does not publish Labour Force Survey data at the CD or CSD levels. As such, LMI at the Economic Region level are often used in the place of intercensal data on CDs or CSDs. Figure 1 shows the CSD of the District of Kitimat, as well as the larger CD and Economic Region it is within, as an example illustrating how Economic Regions may not serve as good proxies for CSDs.

Big River Analytics
Cedar Academy Society
Cedar Academy Society is building a digital representation or “digital twin” of a Lower Mainland city, powered by Unity3D engine. During the summer of 2022, we aim to complete the integration of real-time traffic data into such model to better reflect the dynamic nature of the communities we live in. Through previous studies, we have been able to associate traffic camera-captured images to object counts. This year, we intend to drive further and to project those object counts into visualized traffic in a virtual world.
Cedar Academy Society