Space Apps Challenge
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From to , participants from Montreal, St. John's, Mississauga, Toronto, Vancouver, Hamilton, Waterloo, Ottawa and Calgary will join programmers, designers, students, engineers and entrepreneurs from around the world to create innovative applications at NASA's 10th annual Space Apps Challenge.
The 48-hour hackathon will take place virtually in cities around the world, and will be hosted by 10 international space agencies, including the Canadian Space Agency (CSA)! Participants will share ideas and use open data to solve real-world problems on Earth and in space.
This is the fifth consecutive year that the CSA is participating in the Space Apps Challenge. Several CSA mentors will be present on the collaborative platforms to talk to participants and answer questions. This year, participants will be invited to tackle a CSA challenge.
Astronaut icon 1. Historic space storm hunter
Challenge description: Can you find perturbations of the ionosphere in historic Alouette I data? Global change is leading to more extreme weather events. Can you detect if space weather events have also become more extreme (over the course of Alouette I data collection or even up until now)?
Details about Historic space storm hunter challenge
Ground and atmospheric events can affect the ionosphere and global change is now being detected in the ionosphere. There are however few historic ionosphere datasets.
Launched in , Alouette I sent radio waves of different frequencies into the topmost layer of the atmosphere, known as the ionosphere, and collected data on the depth of penetration of these waves. The results of this (called "ionograms") were sent to ground stations around the world and stored on films, a portion of which have now been digitized (i.e., scanned). These data were used to fuel hundreds of scientific papers at the time. However, few studies have looked across Alouette I ionograms in terms of a global historic time series.
The challenge is to extract parameters of interest from the images of ionograms and examine the results. These parameters can be analyzed for temporal trends or compared to other historic datasets of the ionosphere.
Make sure to explore the "resources" section of this challenge thoroughly. There are several documents there that will help you better understand how to use Alouette I data, including how to interpret ionograms.
Some data has already been extracted from the scanned ionograms. This extracted data is available as a CSV and was also used to create a web application, which will allow you to explore the data visually. The code for the web app as well as the data and metadata extraction are all published on the CSA's GitHub page (see "resources").
The data that has already been extracted from the ionograms is by no means complete. You may choose to reexamine these ionograms to see if you can extract any additional parameters. You could do this using computer vision techniques, or using any other approach that you deem appropriate.
An ionogram is composed of 2 parts: its data and its metadata. The data gives us information on the distribution of the electron density of the ionosphere according to the altitude. The metadata provide information on the time, date and location of the satellite at the moment when the data were collected. In order to properly analyze this dataset, you must examine both the data and the metadata.
Each PNG ionogram is also associated with a PNG microfiche that contains additional metadata. None of the microfiche metadata has been extracted at present.
Please note that the metadata and parameters extracted from the ionogram images are provided primarily for demonstration purposes. These values are subject to error, and should not be directly used in a scientific context. Extracting parameters of interest is part of this challenge.
- Alouette I web application
- All Alouette I data (including support documents)
- Data has already been extracted from the ionograms
- Code that was used to create the existing Alouette I dataset
- Code that was used to create the Alouette I web application
Ionosphere, radio, satellite, history, global change, space storm, computer vision, climate change
Rocket icon 2. Art and science: prototype of an Earth observation satellite
Challenge description: The CSA's satellite development team needs your fantastic ideas to prototype the next generation of satellites. The team wants the satellite of the "future" to be as nimble as a squirrel, as quick as a rabbit, and to unfold like the wings of an eagle. Will you help them and start working like a busy beaver?
And, just like the tortoise, the new satellite will need to adapt to changes over time without changing its "home". Ask your family to help you! Have each person takes on a role just like a pack of wolves would do (e.g. someone can be the leader).
Once you have built your satellite, take a photo of it and show us how it travels (its trajectory). You can animate your satellite by creating a GIF animation.
Details about Art and science: prototype of an Earth observation satellite challenge
There are several types of satellites. Your satellite's mission will be to observe the Earth. It will orbit between 400-800 km from the Earth. The main parts of the satellite are the body of the satellite, solar panels (energy source) and antennae (for navigation, communication and work-related tasks).
The satellite must have the following physical characteristics, at a minimum:
- Form: a combination of geometric shapes
- Energy: optimize the surfaces to make use of solar energy
You will need the following materials:
- Residual materials found in your recycling bin.
- Craft material to put the satellite together (e.g. glue, scissors)
A photographic device (or the camera on your iPod) with video capability.
- Animation: Graphic Interchange Format (.gif), Giphy, Gif Maker, etc.
- Types of satellites
- Observe the Earth with satellites
- Making of a Satellite – RADARSAT Constellation
- Satellite videos
Satellite, recycling, use of residual materials
Atom symbol 3. Space Radiation Danger
Challenge description: This challenge consists in establishing a risk scale of space-weather caused satellite resets, which is the risk of satellite computers shutting down because of strong radiation in space. To this end, data on single-event upsets taken directly from the Canadian satellite CASSIOPE needs to be meshed with multiple open space weather datasets to determine what factors cause the upsets, and devise a way to estimate the risk that the satellite would be affected by space radiation a day in advance, enabling better satellite operations
Details about Space Radiation Danger
Satellites orbiting Earth are in a very hostile environment, especially because of incoming radiation from the Sun, including ultraviolet rays, X-rays, and charged particles also known as solar wind. On the surface of our planet, we are protected by the Earth's magnetic field and the atmosphere. However, satellites located at an altitude of hundreds of kilometers are much more sensitive to this radiation.
Radiation can cause electrical problems, forcing satellite computers to restart, which leads to major data losses. The goal of this challenge is to anticipate the risk of these upsets by comparing past data from the Canadian satellite CASSIOPE with open space weather data.
Other factors can also increase the risk of upsets, such as the local time of the satellite, the date, and the proximity to the South Atlantic Anomaly, which is a region of the globe where the Earth's magnetic field is weaker. By combining the impact of all these factors, you are asked to obtain a real risk scale that estimates the probabilities of single event upsets due to space weather. Of course this indicator, like a weather forecast cannot be perfect, but could be used to help plan operations.
The data provided consists of the Coordinated Universal Time (UTC), position, speed and e altitude of various single-event upsets as well as non-upsets of the CASSIOPE satellite. The observe the influence of these factors can be observed, such as by listing the local time of each single-event upset to try to establish a correlation.
Also, an effective way of detecting solar activity is by measuring variations in the Earth's magnetic field. The planetary K index (Kp) and the disturbance storm index (DST) are two examples of good indicators.
Keep in mind that the data provided has been hand-picked and therefore does not constitute a random sample.
Kp index; Disturbance Storm Index (DST); South Atlantic Anomaly; Solar flares; NOAA
Water symbol 4. From coast to coast to coast: a tool for assessing climate change vulnerability (challenge from the Scientific Advisor to the President)
Challenge description: Your challenge is to develop a situational awareness viewer to assess climate change vulnerability areas in northern regions. To meet this challenge, you will need to draw upon a wealth of data, including satellite imagery from RADARSAT-1.
- Be able to ingest various types of data including RADARSAT-1 imagery
- Be able to geolocalize satellite imagery
- Be able to overlay data layers on top of RADARSAT-1 (point, line, polygon)
This challenge is a collaboration between the Canadian Space Agency, Natural Resources Canada and the Alaska Satellite Facility.
Details about From coast to coast to coast: a tool for assessing climate change vulnerability (challenge from the Scientific Advisor to the President)
- Compare a region over time
- What changes can you observe?
- If possible, flag these observed changes in the viewer and assign them a color scheme based on your assessment of their level of vulnerability (e.g., red, orange, green). A red zone would be where more changes are observed while a green zone would be an area with fewer or less significant changes.
What are vulnerability areas:
- There are multiple types of vulnerabilities, including biological, socioeconomic, archeo-cultural and physical.
- An area can be vulnerable to climate change when, for instance, species must modify their behavioral patterns in order to survive (e.g., finding food, finding a new habitat, needing to move to stay within a specific temperature range). Another example is when changing temperatures affect sea ice coverage (ice type and thickness), which in turn impacts human activities such as fishing, hunting, travel, and safety.
- More information about vulnerabilities.
Here are some ideas to get you started. Feel free to use them, or to come up with something entirely new!
- Visualize sea ice analysis products from the Canadian Ice Service's (CIS) archive alongside RADARSAT-1 data.
- Synthetic aperture radar (SAR) imagery like RADARSAT-1 is a vital tool for monitoring sea ice but it can be hard to interpret without training. Let the experts from CIS be your guides! They produce ice charts for all Canadian waters on a weekly and even daily basis (during the navigation season) as well as a variety of climatology charts and graphs. Their public archives go all the way back to the s. Create a tool that shows georeferenced RADARSAT-1 imagery alongside the relevant ice charts and ice climatology products to help assess what the ice conditions were then and how they compare to the climatological average.
- Visualize land cover using Earth Observation data (RADARSAT-1) and make a relationship between wildfire areas and biodiversity changes.
- Forest fire events play a key role in population dynamics. Fires are natural disturbances. Depending of the intensity, the size or the frequency of fire in an area, habitat fragmentation can influence the biodiversity to regenerate. Create a tool that shows georeferenced RADARSAT-1 imagery alongside wildfires data inventory to help identify locations where biodiversity is most likely vulnerable to changes.
- Canadian Space Agency RADARSAT-1 Data
- Natural Resources Canada Earth Observation Data
- Alaska Satellite Facility Earth Observation Data
- Base map data:
- Environment and Climate Change Canada – Canadian Ice Services:
- Historical climate data: https://climate.weather.gc.ca/ and https://climatedata.ca/
- Northern Canada shoreline classification
- Protected Areas
- Territorial Governments:
- Land Use Designations Polygon (Yukon)
- Contaminated Sites (Yukon)
- Yukon Biological Information
- Ecoregions (Northwest Territories)
- Fire History (Northwest Territories)
- Species at Risk (Northwest Territories)
- Department of Fisheries and Oceans:
- Statistics Canada Census
- National Hydro Network (watersheds)
- Catalog Data, https://geogratis.gc.ca, https://open.canada.ca, and https://open.canada.ca/en/open-maps, https://www.maps.geomatics.gov.nt.ca/Html5Viewer_PROD/index.html?viewer=SDW, https://www.geomatics.gov.nt.ca/en, https://mapservices.gov.yk.ca/GeoYukon/
- SNAP – ESA
- MapReady – ASF
- GitHub (including https://github.com/asc-csa/)
- Google Earth
- Geo Tools
- Programming languages of your choosing. You can use the following to get yourself started:
- Python: GDAL, folium, pandas, numpy, plotly, matplotlib, dash, etc.
- R: shiny, rgdal, leaflet, sf, raster, etc.
- The RADARSAT-1 data provided is in CEOS format. More information about this format is available here: https://asf.alaska.edu/information/data-formats/data-formats-in-depth/, https://asf.alaska.edu/how-to/data-recipes/how-to-view-and-geocode-ceos-data-in-asf-mapready/
- CEOS resources: https://gdal.org/drivers/raster/sar_ceos.html, https://github.com/asfadmin/ASF_MapReady
climate change, arctic, sea ice, wildlife, geospatial, data assimilation, data viewer, software, viewing tool, coding, biodiversity, synthetic aperture radar, SAR, earth observation, geomatics
Smog symbol 5. Pollution spills high above us
Challenge description: Can you spot major atmospheric pollution events (e.g. major explosions, fires or chemical spills) in atmospheric gas measurements from SCISAT?
Details about Pollution spills high above us
The effects of wildfires can be seen in SCISAT data, but what other events can be detected in these data?
Launched on , SCISAT helps a team of Canadian and international scientists improve their understanding of the depletion of the ozone layer, with a special emphasis on the changes occurring over Canada and in the Arctic.
SCISAT can now measure over 44 gases globally at a range of altitudes through the atmosphere, potentially allowing new insight into the sources and sinks of theses gases.
The authoritative source data for the Atmospheric Chemistry Experiment (ACE), also known as SCISAT, is available on the ACE site (external site only available in English).
Please read this Important Mission Information Document before using the ACE/SCISAT data. Please refer to the relevant scientific literature when interpreting SCISAT data.
Visit our Github page to learn more about our applications..
- Accessible through the web app
- Accessible through the portal
- A list of publications using ACE-SCISAT data
SCISAT, Atmospheric event, Atmospheric Chemistry, Atmospheric gases, Atmospheric pollution, satellite, greenhouse gases, artic, ozone
Telescope symbol 6. Explore space with Canada's space telescope, NEOSSat
Challenge description: Launched in , Canada's microsatellite-based space telescope NEOSSat tracks Near-Earth asteroids, comets, stars with potential exoplanet transits, and other astronomical targets, alongside surveillance of resident space objects in Earth's orbit (such as satellites and space debris), producing thousands of images per week. This challenge involves designing a solution to characterize and classify space objects of interest within the public image datasets, and potentially visualize the time-series evolution of these objects through engaging animations or other graphical tools.
Details about Explore space with Canada's space telescope, NEOSSat
Launched in , Canada's Near-Earth Object Surveillance Satellite (NEOSSat) is a successful partnership between the Canadian Space Agency (CSA) and Defense Research & Development Canada (DRDC), with a dual mission supporting space astronomy and space situational awareness (surveillance of resident space objects in Earth orbit). A nimble suitcase-sized micro-satellite orbiting at 800 km altitude, NEOSSat is equipped with a 15-cm telescope capturing a 0.8 degree field of view on a 1024x1024 pixel CCD. The satellite is now producing thousands of images per week, for various applications including space situational awareness, near-earth asteroid and comet imagery, and photometry of exoplanets and other astronomical targets. After each downlink, the astronomy images are published to an open data platform to support a variety of astrometry and photometry applications defined by NEOSSat Guest Observers, complete with relevant meta-data.
The archive offers a wealth of information that can be mined. For example, one challenge could be about identifying potential asteroids and other objects in the images, by producing an algorithm that characterizes and categorizes space objects of interest that are taken using NEOSSat's near-Sun surveys. By integrating an astrometry component, the software could also find the position of the space object you have imaged at the time it has been imaged for publication in applicable official records. Building a searchable tool where one can look up whether NEOSSat has or hasn't imaged a particular object/location would help make the archives more useful and potentially lead to new discoveries through "precovery" (finding something new and unexpected from images taken for another purpose).
Another challenge could be a tool that brings together a large image set to better visualize NEOSSat asteroid and comet data through engaging animations such as a time-series as the object makes its close approach to Earth. Alternately, NEOSSat could be represented on an interactive star map, enabling users to see where NEOSSat has or has not observed. Again, the project could integrate an astrometry component, showing on a visual map the evolution of the position of the Near-Earth Asteroid (NEA) or comet object as it is being imaged through this time series. Or, by integrating photometry, one can study and demonstrate brightness changes of objects to help characterize its rotation or other properties. Algorithms developed to identify, categorize and/or characterize asteroids, comets, satellites, and space weather events captured during NEOSSat's space surveillance surveys could streamline the processes used to detect potential space outliers.
Challenges like these are useful for improving the appeal of space situational awareness and NEA imagery, making it more accessible to the general public. Identifying objects that are being imaged is an excellent first step to performing useful science on these images and is good to introduce to those unfamiliar with image analysis for astronomical or space surveillance purposes. Engaging visualizations of space objects is a great way to increase public interest in Canada's research efforts, and promote the NEOSSat mission as a whole. By integrating astrometry and/or photometry, the developed software could further improve scientific usefulness of the dataset, with a view towards deepening our understanding of the dynamic behavior of these space objects, which, in turn, would help to plan future imaging priorities/opportunities, predict conjunction events, and lead to a better understanding of the cosmos and the things in it.
The NEOSSat data archive (in the astronomical standard FITS format) is accessible from two locations:
- CSA's Open Data portal
- National Research Council (NRC)'s Canadian Astronomy Data Centre (CADC)
Given the variety of imaging targets and modes, the image meta-data will be invaluable to help determine the images to use. Full details are provided in documentation, but a few key meta-data fields are:
- MODE: Images are generally intended to be taken in FINE_POINT mode (where NEOSSat is stably pointing relative to the stars) and FINE_SLEW mode (where NEOSSat is tracking the rate of a moving object). Different factors may prevent NEOSSat from reaching the intended mode
- RA/DEC/ROLL: these fields provides the space coordinates being imaged, based on the best estimate from the satellite control system (which isn't always right), helpful to identify objects against an independent catalog
- OBJECT: this field specifies the intended target of the observer as provided by the scheduler; if a specific astronomical target was being imaged, its name might be provided here. It should also be noted that objects sometimes go by another name. However, if the image is a general survey image (no specific target), then the field could contain the word "Survey" and the coordinates being targeted would appear here. Survey fields typically contains at least 4 different survey images of the same field at different times.
For solutions based on image processing, It would be useful to apply dark subtraction as detailed in the Jason Rowe's Python routines for NEOSSat. For solutions where multiple images of the same target are combined into an animation, it should also be noted that subsequent images may not necessarily have the stars in the same position due to the telescope adjusting its pointing. Participants will need to develop a solution to freeze the stars in place as to make only the Near-Earth objects appear animated. FITS libraries are available to support these kinds of image transformations.
The table below provides an example of the dataset contains a subset of astronomical images that may be helpful in this challenge.
|Type||Nom de l'objet||Jours auxquelles les images ont été prises|
near-Sun field to search for objects hidden to ground telescopes
| / DOY 318-319, 324-325, 333-334, 339-341, 346-348, 351-358, 364-365;
/ DOY 001- 008, 016-031; 349-355
/ DOY 020-028, 330-335, 357-366
/ DOY 125, 128, 133, 137, 138,
|Astéroïde géocroiseur||-RC||/ DOY 250,|
|-KW4||/ DOY 118-126 (every day), 128-152 (every day)|
|Comet||46P||/ 281, 284, 285, 290, 293, 300, 319, 321, 335, 345, 348, 349|
|4P||/ DOY 105, 120, 126, 131, 211, 212, 216, 225, 229, 230, 245, 256|
|10P||/ DOY 152, 156, 163, 170, 171, 175, 182|
|15P||/ DOY 105, 111, 119, 129, 135, 142, 145, 147, 149, 153, 156, 165, 169, 174, 177, 182, 184, 208, 212, 213, 217, 226, 227, 230, 256|
(aka 2I/ Q4 or Borisov)
| / DOY 256–365,
2020 / DOY 001-051
|Exoplanet transit||TOI 1823.01||/ DOY 160, 239|
|TOI 2046.01||/ DOY 119, 164, 209, 232|
|TOI 1516.01||/ DOY 134, 138, 159, 245|
- Image archives:
- CSA Open Data Archive for NEOSSat
- Government of Canada Open Data Archive: NEOSSat - Astronomy Data - Open Government Portal (canada.ca)
- Canadian Astronomy Data Centre: Near-Earth Object Surveillance Satellite (NEOSSat) - Canadian Astronomy Data Centre (nrc-cnrc.gc.ca). The above link also contains the FITS Image User Guide which explains all of the metadata within the regularly published raw images in the FITS format.
- Image processing software:
- Raw, unprocessed astronomy images are always available on the portal, uploaded shortly after downlink from the satellite. In general, users perform their own image processing to remove image artifacts and perform analysis. In some cases, the resulting processed images are returned back to CSA and made available on Open Data Portal. (you can consult these files in _cord.fits format)
- The NASA FITS Support Office provides a list of FITS software libraries for various programming languages, including an overview of each package to help in the selection of an appropriate library
- The following software packages are particularly popular and mature:
- FITS viewers/converters are also available, such as:
- In addition, custom-built software for NEOSSat image cleaning and photometry
- This program, which demonstrates how to apply image cleaning on NEOSSat raw images, could help serve as a template to get started with NEOSSat data processing and manipulation.
Space Astronomy, FITS, Space Situational Awareness
Brain symbol 7. Leveraging AI/ML to monitor, detect and quantify ozone recovery in the stratosphere
Challenge description: The UN Montreal Protocol has been responsible for the recovery of the stratospheric ozone layer. The global recovery of this gas is of international concern and important for human health. Recently, an increasing amount of trace gases in our atmosphere including greenhouse gases, have been blamed to mask the rate of this recovery. For this challenge, we would like to leverage Canadian and international space-based datasets to explain this slow recovery.
Details about Leveraging AI/ML to monitor, detect and quantify ozone recovery in the stratosphere
This challenge will ask participants to take existing space-based stratospheric ozone data, along with other atmospheric trace gases including GHGs, and apply Machine Learning (ML) and Artificial Intelligence (AI) capabilities in order to:
- Understand the potential and limitations of using AI/ML algorithms to detect and quantify stratospheric ozone recovery;
- Based on these algorithms, create a visualization database that will aid in classifying and detecting ozone recovery using remote sensing datasets.
Participants are requested to start with Canada's 20-year stratospheric ozone dataset as measured by OSIRIS onboard the Swedish Odin satellite.
From there, participants may select any complimentary satellite-based dataset containing stratospheric ozone concentrations.
From there, participants may select any ozone depleting substance that can be used to show ozone recovery, along with other trace gases, including GHGs, that can be used to illustrate a slower rate of ozone recovery.
Finally, participants are requested use any AI/ML algorithms that can be trained on these datasets and eventually quantify the rate of ozone recovery based on a select group of trace gas concentrations.
- Canada's OSIRIS Dataset (20 years since first launch in
- Ozone dataset directly from Canadian science team at U Saskatchewan
- Complimentary satellite-based stratospheric ozone datasets:
- OMPS and OMPS-LP Mission:
- SAGE on ISS:
- SAGE III on International Space Station | NASA
- NASA Data Aids Ozone Hole's Journey to Recovery | NASA
- About SAGE III on ISS – SAGE (Stratospheric Aerosol and Gas Experiment) (nasa.gov)
- SAGE III Carries on Critical Measurements of Stratospheric Aerosols and Ozone – SAGE (Stratospheric Aerosol and Gas Experiment) (nasa.gov)
- Validation of SAGE III/ISS Solar Occultation Ozone Products With Correlative Satellite and Ground‐Based Measurements | NASA Airborne Science Program
- Tracing Changes in Ozone-Depleting Chemicals
- SCISAT Stratospheric Ozone Dataset
- Ozone and ozone depleting substances directly from Canadian science team at University of Waterloo
Stratospheric, ozone recovery, greenhouse gases, chlorofluorocarbons, CFCs, OSIRIS instrument on Odin satellite.
If you complete a CSA challenge, register via our form for the chance to win one of the prizes offered by the CSA.
A personalized electronic certificate signed by a Canadian astronaut for each participant.
A Space Apps Challenge Canada bag containing several CSA giveaways for each member of the local winning teams.
A CSA blanket for each member of winning teams.
Global Canadian prize
Virtual mentoring sessions with CSA experts.
Special prize for the challenge : From coast to coast to coast: a tool for assessing climate change vulnerability
One-on-one meeting with Scientific Advisor to the CSA President (Sarah Gallagher)
Previous Space Apps Challenge editions
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