Deep learning for Satellite Image Time Series Analysis

at ISPRS Congress 2022 - International Society for Photogrammetry and Remote Sensing XXIV ISPRS Congress
Nice - June 6-11, 2022


Dr. Charlotte Pelletier, Univ. Bretagne Sud (IRISA), France Dr. Zhou Zhang, Wisconsin-Madison, USA Marc Rußwurm, M.Sc., EPFL-ECEO, Sion, Switzerland

Satellite Image Time Series | Deep learning | Classification


Dynamics on the Earth’s surface are governed by continuous temporal processes that can be observed in discrete intervals by Earth observation satellites. Recent satellite constellations, such as Landsat-8, Sentinel-1 and 2, produce a high volume of satellite image time series by covering the same location on Earth at frequent temporal intervals. The increase in the number of acquired images, the diversity of the time series (e.g., optical and radar), and the combinations of the high spatial and high temporal resolutions enable advances in a variety of applications, such as vegetation modeling, climate forecasting, urban planning, or precipitation nowcasting. The complexity of the data (irregular temporal sampling, multi-modality, multispectral data, high volume of data, low number of high-quality reference data) requires the development of novel data-driven methods to solve (early) classification, regression, forecasting, or indexing tasks. In this thematic session, we welcome new contributions that advance the analysis of satellite image time series.

Submission Track

Please submit your abstract to the following specific track ST_STIS
Submission deadline: 10th of January 2022