Ing. Jiří Pihrt

Proceedings paper

10.15439/2024F5446

Department of Applied Mathematics

Efficient crop monitoring and crop dynamics fore- casting leveraging diverse satellite and point data are described. Attention-based architecture architecture is adapted for mono- temporal cloud removal which overcomes an issue of crop monitoring. Combining optical (Sentinel-2) and radar (Sentinel- 1) satellite data improves the robustness and accuracy of the model in terms of satellite image reconstruction and normalized difference vegetation index prediction and forecasting. However, available soil-type geographical data and land surface analysis products, do not improve prediction accuracy significantly

Proceedings paper

10.15439/2024F5990

Department of Applied Mathematics

Accurate local weather forecasting is vital for farmers to optimize crop yields and manage resources effectively, but existing forecasts often lack the precision required locally. This study explores the potential of combining data from local weather stations with global forecasts and reanalysis data to improve the accuracy of local weather predictions. We propose integrating the HadISD data set, which contains data from 27 stations in the Czech Republic, with the Global Forecast System predictions and ERA5-Land reanalysis data. Our goal is to improve 24-hour weather forecasts using Multilayer Perceptrons, CatBoost, and Long Short-Term Memory neural networks. The findings demonstrate that combining local weather station data with global forecasts and incorporating ERA5-Land reanalysis data improves the accuracy of weather predictions in specific locations. Notably, using deep learning to estimate ERA5-Land data and including this estimation in the final model reduced the forecasting error by 59\%. This advancement holds promise in optimizing agricultural practices and mitigating weather-related risks in the region.

Proceedings paper

Department of Applied Mathematics

Weather4cast again advanced modern algorithms in AI and machine learning through a highly topical interdisciplinary competition challenge: The prediction of hi-res rain radar movies from multi-band satellite sensors, requiring data fusion, multi-channel video frame prediction, and super-resolution. Accurate predictions of rain events are becoming ever more critical, with climate change increasing the frequency of unexpected rainfall. The resulting models will have a particular impact where costly weather radar is not available. We here present highlights and insights emerging from the thirty teams participating from over a dozen countries. To extract relevant patterns, models were challenged by spatio-temporal shifts. Geometric data augmentation and test-time ensemble models with a suitable smoother loss helped this transfer learning. Even though, in ablation, static information like geographical location and elevation was not linked to performance, the general success of models incorporating physics in this competition suggests that approaches combining machine learning with application domain knowledge seem a promising avenue for future research. Weather4cast will continue to explore the powerful benchmark reference data set introduced here, advancing competition tasks to quantitative predictions, and exploring the effects of metric choice on model performance and qualitative prediction properties.

Proceedings paper

10.1109/IV51971.2022.9827089

Department of Applied Mathematics
Faculty of Information Technology

Prediction of the movement of all traffic participants is a very important task in autonomous driving. Well-predicted behavior of other cars and actors is crucial for safety. A sequence of bird’s-eye view artificially rasterized frames are used as input to neural networks which are trained to predict the future behavior of the participants. The Lyft Motion Prediction for Autonomous Vehicles dataset is explored and adapted for this task. We developed and applied a novel approach where the prediction problem is viewed as a problem of spatiotemporal prediction and we use methods based on convolutional recurrent neural networks.