The Alberta Chapter of the Canadian Land Reclamation Association (CLRA) is looking to fill Board of Directors positions to work alongside the executive team and continuing directors for a 2-year term starting April 2021.
Read More
L’Association Canadienne de Réhabilitation des Sites Dégradés (ACRSD) – Chapitre Québec lance son premier webinaire afin de promouvoir la « Décennie des Nations Unis pour la restauration des écosystèmes ».
Read More
Vegetation health assessment is used in a wide range of environmental assessment programs such as spill clean-up verification and annual vegetation health assessment at risk-managed contaminated sites.
Timely cost-effective assessments using traditional (boots-on-the-ground) field assessment year after year can be difficult (and subjective) due to high variances in vegetation growth rates and species influenced by meteorological conditions, diverse soil types, and topography. This presentation provides a summary of a proof-of-concept demonstration project that uses the power of artificial intelligence to complete vegetation health assessments. The demonstration project exploits the abilities of machine learning computer scripts to independently recognize, identify, sort, and classify complex patterns.
For this initiative, a supervised Machine Learning System (MLS) is trained with a known dataset of vegetation, examples of photos that correspond to vegetation feature classes that require identification and classification. The training dataset is created by subject matter experts who use ground-truth vegetation assessment data to ensure that the training dataset inputted to MLS is accurate. In addition, unsupervised MLS classification analyses are completed and compared against the supervised MLS classification outputs to minimize the introduction of human bias.
MLS scans ultra-high-resolution visible wavelengths of georeferenced air-photos obtained from low altitude Unmanned Aerial Vehicle (UAV) flights to identify and classify the desired vegetation features by matching the vegetation patterns in the UAV photo with the training dataset. MLS is programmed to automatically scan and classify billions of pinpoint locations before it finalizes its prediction. The MLS automation requires little human intervention, enabling it to review air-photo(s) covering areas ranging from thousands of square metres (UAV photo) to millions of square kilometres (satellite photo).
This presentation will also cover how artificial neural network technology can be used to leverage the information from the MLS and site-specific environmental investigation data to solve perplexing real-world problems.
Read More
The Alberta Chapter of the Canadian Land Reclamation Association (CLRA) will be hosting its Annual General Meeting (AGM) and Conference virtually on Wednesday, February 24, and Thursday, February 25, 2021.
Read More
Showcase your organization’s best practices or technologies for efficient retirement of oil and gas assets!
Successful applicants will be given a segment in an hour-long video seminar to showcase how their organization, product, approach, technology, or digital application results in improved outcomes in the environmental management of legacy oil and gas assets. Participation is free!
Read More
Vegetation health assessment is used in a wide range of environmental assessment programs such as spill clean-up verification and annual vegetation health assessment at risk-managed contaminated sites.
Timely cost-effective assessments using traditional (boots-on-the-ground) field assessment year after year can be difficult (and subjective) due to high variances in vegetation growth rates and species influenced by meteorological conditions, diverse soil types, and topography. This presentation provides a summary of a proof-of-concept demonstration project that uses the power of artificial intelligence to complete vegetation health assessments. The demonstration project exploits the abilities of machine learning computer scripts to independently recognize, identify, sort, and classify complex patterns.
For this initiative, a supervised Machine Learning System (MLS) is trained with a known dataset of vegetation, examples of photos that correspond to vegetation feature classes that require identification and classification. The training dataset is created by subject matter experts who use ground-truth vegetation assessment data to ensure that the training dataset inputted to MLS is accurate. In addition, unsupervised MLS classification analyses are completed and compared against the supervised MLS classification outputs to minimize the introduction of human bias.
MLS scans ultra-high-resolution visible wavelengths of georeferenced air-photos obtained from low altitude Unmanned Aerial Vehicle (UAV) flights to identify and classify the desired vegetation features by matching the vegetation patterns in the UAV photo with the training dataset. MLS is programmed to automatically scan and classify billions of pinpoint locations before it finalizes its prediction. The MLS automation requires little human intervention, enabling it to review air-photo(s) covering areas ranging from thousands of square metres (UAV photo) to millions of square kilometres (satellite photo).
This presentation will also cover how artificial neural network technology can be used to leverage the information from the MLS and site-specific environmental investigation data to solve perplexing real-world problems.
Read More
Congratulation to Samantha McGarry, recipient of the Dr. Jack Winch Early Career Award for her body of work in reclamation. Sam began her fourth-year thesis work with Dr. Graeme Spiers and Dr. Peter Beckett in 2009. Sam’s thesis and later, Master’s work contributed towards a project was entitled the Green Mines Green Energy (GMGE) project and aimed to grow energy crops on mine tailings using organic residuals. The project addressed many challenges present in society with cross-cutting themes related to environmental sustainability and recycling, as well as reclaiming brownfields in a sustainable manner. Sam’s work focused on growing corn, canola, and switchgrass in biosolids applied to mine tailings at Xstrata (now Sudbury Integrated Nickel Operations, A Glencore Company where Sam currently is the Site Rehabilitation Lead).
Read More
Like other conferences in 2020, the 27th Annual BC-MEND ML/ARD Workshop was changed to a virtual event. The conference retained the half-hour presentation format and there were eighteen presentations with three-half day sessions on December 1, 2, and 3. The extra capacity of the virtual format allowed the conference to provide free registration to Canadian students, First Nations, and community groups. Money saved on not having a social for thirsty delegates allowed the conference to cut the registration fee for everyone else from $200 to $50. Seven hundred registrants from across Canada and abroad was a record.
Read More
The Ontario chapter of the Canadian Land Reclamation Association (CLRA) along with Vale, the Ontario Mining Association (OMA), and the Ontario Ministry of Energy, Northern Development and Mines (MENDM) are pleased to announce that Jonathan Lavigne has been selected to receive the 2020 Tom Peters Memorial Mine Reclamation student bursary. Jonathan is currently a 1st year Ph.D. student in the Boreal Ecology program at Laurentian University, under the primary supervision of Dr. Nathan Basiliko. Jonathan’s Ph.D. research focuses on the introduction of novel reclamation strategies to improve soil fertility in mining impact areas, encompassing both smelter impacted lands and legacy aggregate mining sites. The award will be used to enhance both GIS and statistical analysis components of Jonathan’s research. The competition was very stiff this year, with four excellent submissions, and we thank all candidates.
Read More
The preliminary conference program is now available! You can get a sneak peek at the speakers and their sessions by clicking the buttons to the right.
Please note that a more detailed schedule will be available shortly.
Read More