Ondřej Pešek

Workshop room 1 - C221 Brief explanation of the general concept of deep learning and CNNs Writing a simple CNN from scratch and testing it on an easy geo-classification task Using a predefined CNN architecture to perform land cover classification in Prague Introduction to a deep learning quest that continues throughout the rest of the workshop week (optional)

Workshop room 1 - C221 Introduction into web processing (OGC WPS, OGC API - Processes, REST API) in a comparative view Accessing WPS processes Accessing WPS processes from Python code using OWSLib Accessing WPS processes from QGIS actinia cloud computing with REST API (@neteler @metzm) explain how REST and actinia work actinia in Jupyter notebook actinia-QGIS plugin

If we simplify the picture of the world to an absolute minimum, we can say that there are only two remote sensing classes - urban areas and nature. A big part of the nature section is vegetation. Therefore, it is not a surprise that the vegetation detection, as well as the urban areas detection, constitutes a big part of remote sensing tasks, and very often these classes appear in remote sensing scenes together, side by side or within each other, separately or affecting one another. Such an effect can be seen in urban vegetated areas that could be either of natural or artificial origin and could serve different land use purposes. This talk focuses on seeing urban vegetation as one of two classes - for recreational purposes (such as public parks) and for other purposes (such as alleys or grass next to a road) - and explores whether we are able to determine such class from Sentinel-2 data using convolutional neural networks.

If we simplify the picture of the world to an absolute minimum, we can say that there are only two remote sensing classes - urban areas and nature. A big part of the nature section is vegetation. Therefore, it is not a surprise that the vegetation detection, as well as the urban areas detection, constitutes a big part of remote sensing tasks, and very often these classes appear in remote sensing scenes together, side by side or within each other, separately or affecting one another. Such an effect can be seen in urban vegetated areas that could be either of natural or artificial origin and could serve different land use purposes. This talk focuses on seeing urban vegetation as one of two classes - for recreational purposes (such as public parks) and for other purposes (such as alleys or grass next to a road) - and explores whether we are able to determine such class from Sentinel-2 data using convolutional neural networks.