Remote sensing is entering a new era of time-series analysis. Short revisit times of satellites allow for monitoring of many areas across the globe on a weekly basis. However, there has been little exploration of deep learning techniques to leverage this new temporal dimension at scale. Especially, existing approaches have struggled to combine the power of different sensors to make use of all available information. In addition, large scale high quality change detection benchmarks are rare.

To stimulate innovation in spatio-temporal machine learning, we (TUM, DLR and Planet labs) have partnered up to organize a unique challenge centered around modeling multi-temporal land cover changes from Planetscope and Sentinel time series data, as part of the EarthVision Worship at CVPR 2021.

This effort is jointly supported by TUM, the Munich Data Science Institute (MDSI), DLR, Planet labs, BMWi, German Space Agency, International AI future lab “AI4EO”, Munich Data Science Research School, and Helmholtz AI.

For more details, please visit http://www.classic.grss-ieee.org/earthvision2021/challenge.html