2022 – ISPRS Congress 2022 in Nice, France


Dynamics on the Earth’s surface are governed by continuous temporal processes that can be observed in discrete intervals by Earth observation satellites that cover the same location on Earth at regular temporal intervals. An increase in data availability and the development of data-driven methods allow us to use new space-borne measurements to estimate the parameters of deep learning models for a variety of applications, such as vegetation modeling, climate forecasting, or precipitation nowcasting. This tutorial covers the latest developments in deep learning techniques for time series classification with application to Earth observation. Time series classification is the task of determining a discrete class label for an unlabeled time series. Several mechanisms that often originated from related fields, like computer vision (e.g., convolutional neural networks) or natural language processing (e.g., recurrent neural networks) have proven to be useful for time series classification in the Earth observation domain. In this tutorial, we aim at providing a solid theoretical basis to understand these concepts. Practical sessions allow the participants to follow with hands-on code in Jupyter and Colab notebooks.

June 5th 2022 09:00 to 12:30 – Room 1B39

  Deep Learning for Satellite Time Series Tutorial
09:00 - 09:05 Opening
09:05 - 09:30 Introduction to Satellite Image Time Series (Lecture)
09:30 - 10:30 Data and Features (Google Colab Notebook 1)
10:30 - 11:00 break
11:00 - 11:25 Deep learning for SITS (Lecture)
11:25 - 12:25 Deep Learning (Google Colab Notebook 2)
11:25 - 12:30 Closing remarks



University Côté d’Azur - Campus Saint Jean d’Angely - 5, rue du 22ème BCA - 06300 Nice

Room 1B39