Centre canadien de cartographie et d’observation de la Terre (CCCOT)
Number of internship offered : 1
Internship duration : to be discussed (12-16 weeks)
Presentation of the host organization :
As the national mapping agency, Natural Resources Canada’s Canadian Centre for Mapping and Earth Observation (CCMEO) is responsible for, among other things, the creation, management, publication and maintenance of a wide range of geospatial data covering the Canadian landmass. Through the GeoBase division, CCMEO is responsible for the maintenance of fundamental geospatial data, such as the national hydrographic network. This data is of critical importance to the country as it provides the basis for the creation of a multitude of value-added geospatial data (for example, the country’s statistical data is referenced from the fundamental data). To be relevant, this data must be continually updated. Technological developments in recent years have enabled the emergence of new approaches, such as deep learning (DL), that provide near-term solutions for updating geospatial data quickly and efficiently. For about three years, the GeoBase Division has established a research and development team to develop artificial intelligence (AI) based tools for extracting map objects from remote sensing data (optical imagery, radar, and elevation data).
Project description :
Over the past year, the GeoBase division has been working on and released two datasets of flood events. The first is a publicly released point dataset of all recorded flood events since as early as 1600s and the other is an internal dataset, compiled of flood hazard maps from the provinces and territories.
We are looking for someone to explore these datasets. With the point data, we want to explore and visualize the temporal and spatial trends in flooding over the length of the record, to identify hotspots and trends and try to understand these patterns and what factors may be influencing them. The second project aims to explore the flood hazard data and generate risk information for a test community, by utilizing other tools developed at NRCan: RiCORDE and CanFlood. This work will help us have a better understanding of the changing flood risk over time and evaluate the capability of our flood risk assessment toolbox.
We hope the results of this work can help with the National Flood Hazard Identification and Mapping Program, (FHIMP) and enable us with a better understanding of the changing flood risk over time, and evaluate the capability of our flood risk assessment toolbox.
The evaluation of the internship will be based on the quality of the literature review, the quality of the implementation of the method and the rigor of the tests performed.
Project feasability :
The objectives are very achievable. The intern will also have access to various experts in the field, who can help him/her in his/her tasks. The defined objectives can be revised during the internship, depending on the progress.
The data includes nationally available datasets from OpenMaps.ca platform and internal use datasets.
Computing resources :
The intern will have access to a laptop computer to access software and resources. For more intensive testing, the intern will have access to a high-performance infrastructure and a cloud infrastructure, both with GPUs. Everything will be accessible remotely.
Intern’s main respobsability :
The objective of the internship is 1) explore historic record of flooding and identify trends/patterns over time and space and 2) test out a workflow for risk assessment, from flood hazard polygon, to flood depth grid, and finally, computing a risk assessment.
More specifically, the tasks will be as follows:
- Become familiar with the available data inventory.
- Perform literature review of centrography and spatial point pattern analysis
- Understand and explore spatial patterns in point data, using open source tools (QGIS, Python, and/or R)
- Prepare data and run software tools (QGIS, Python) to perform a risk assessment
- Analyze the results (quantitatively and qualitatively);
- Prepare and present a report on the results obtained and recommendations for future work.
Required skills :
Familiarity with geospatial data. Experience with the Python programming language.
Additional skills :
Knowledge in geomatics is an asset.
The language used by the intern must be English. The intern’s work team will be composed of French and English speakers.
Canadian citizenship or permanent residency
If you are interested in this internhip, please contact by email coordonnateur du programme DOTS