Discovery Challenges
Seismic Monitoring and Analysis Challenge (SMAC)
The continuously growing amount of Satellite data has opened numerous research avenues into exciting computer vision based applications. In this context, we propose the SMAC challenge, an Earth Observation challenge that aims to identify and measure earthquakes using satellite imagery. More specifically, the proposed challenge is a classification and regression challenge that, given the satellite imagery, aims to identify affected areas and the strength of the events. Additionally, this challenge will pose an extra evaluation of resource consumption to propose the most scalable solutions possible. We propose this challenge to foster discussions and further collaborations between researchers and first responders involved in the remote sensing domain to tackle problems related to hazard management and their environmental impact.
- Organizers: Isaac Corley, Nils Lehmann, Daniele Rege Cambrin, Lorenzo Vaiani
- Start date: April 20, 2024
- End date: June 10, 2024
- Website
- Contact: lorenzo.vaiani@polito.it
Diving Deep: Forecasting Sea Surface Temperatures and Anomalies
Hurricanes, mass coral bleaching, disruption of sea mammal migration patterns, and extremely hot summers or cold winters all have one thing in common - they are driven by temperature changes in our seas and oceans. In this challenge, the aim for participants will be to investigate the predictability of global SSTs and SSTAs. Variability in sea surface temperatures (SSTs), also known as SST anomalies (SSTAs), is linked to climate oscillations and occurrences of extreme events, including the El Niño-Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) oscillation, and marine heatwaves. The participants will specifically focus on forecasting SSTs and SSTAs with a time horizon of one-month and sixmonth lead time.
- Organizers: Dr Varvara Vetrova, Dr Phil Mourot, Ding Ning, Prof Karin Byran, Prof Yun Sing Koh
- Start date: May 17, 2024
- End date: June 17, 2024
- Website
- Contact: divingdeepecml@googlegroups.com
Developing a Data driven Model for Predicting Failure Risk Levels for a Component of Volvo Heavy-duty Trucks
This industrial challenge will be organized in collaboration with Volvo Group Truck Technologies. A real-world dataset incorporating the measurements from a fleet of more than 20,000 Volvo heavy-duty trucks with survival information about an undisclosed component of the trucks will be released for the first time in connection with the challenge. The measurements are collected for a specific period through telemetries or workshop visits. The challenge aims to develop a model to predict the risk levels to help Volvo find the components requiring maintenance. This will enable the company to monitor the equipment more intelligently and prevent breakdowns by taking proper actions.
- Organizers: Mahmoud Rahat, Peyman Mashhadi, Shamik Choudhury, Leo Petrin, Thorsteinn Rögnvaldsson
- Collaborators: Halmstad University, Volvo Group Truck Technologies
- Start date: May 15, 2024
- End date: June 15, 2024
- Website
- Contact: mahmoud.rahat@hh.se
