Teaching E-TRAINEE

Processing of remote sensing time series data

is a challenging goal of today’s and future research. Global Earth observation programmes provide extensive image archives dated back to several decades. Aerial images and LiDAR point clouds are acquired on the national level in two to three years cycles. Specific localities are monitored on regular (hourly, weekly, monthly, yearly) basis for research purposes such as monitoring resistance of plants to droughts, invasive species encroachment, melting of mountainous snow and glaciers, desertification, decrease of biodiversity, deforestation or urbanization

Thus, processing methods of the time series of remote sensing data of different time and spatial scales, the combination of heterogeneous and multi-modal data sources and accuracy assessment of the obtained results are becoming a key part of remote sensing and geo(infor)matics curricula.

The project’s objective is to develop a comprehensive research-oriented open e-learning course on time series analysis in remote sensing for environmental monitoring. The course offers a multidisciplinary approach connecting themes from computer science, geography, and environmental studies.
It combines well established and latest technologies of remote sensing (satellite and UAV sensing, multispectral and hyperspectral sensing, 3D point clouds) and methods of artificial intelligence (machine and deep learning) in order to use these technological developments to understand environmental changes and interaction of human activities and environment. It shows how the same environmental phenomenon can be analyzed from the perspective of different data sources, scales and time frequencies. 
Moreover, it increases students’ digital literacy in time series analysis in remote sensing, which comprises the improvement in methodological and practical data handling skills. The students also gain the skills for critical reflection and communication of complex data processing tasks, enabling them for transformative and interdisciplinary research missions.