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Adam Tejkl


I’m a doctoral student at the Faculty of Civil Engineering. My dissertation focus on the evaluation of erosion risk of land using remote sensing methods, especially satellite scenes. Within this focus, it is also involved in the solution of the project QK1720289 “Development of an automated tool for optimizing the monitoring of agricultural soil erosion using distance methods” and TH02030428 “Design of technical measures for stabilization and protection of slopes against erosion”. I’m also actively involved in activities around the laboratory and field rain simulator. Another of my field of expertise is measuring and developing measuring devices.

All Sessions by Adam Tejkl

  • Day 3 - Poster sessions

    June 15 in the lobby

  • 11:30  - 11:50
    The high time required for hand rill identification is an obstacle in erosion research. Nevertheless, a significant number of grooves have already been manually marked for various studies. It is therefore possible to use these already obtained data to train an algorithm, which will then automatically identify the grooves. Before the simulation, during the pause between the simulations and after the experiment, the surface is scanned to create a 3D model. Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. The image is converted to a matrix, subsequently, this matrix is sequentially traversed in steps corresponding to the individual squares. The corresponding square is selected from each band and a mosaic is then created from these squares. The Kaggle Cat Dog model was used as the basis for creating the model. This model was modified by inserting mosaics into the model instead of images. The training dataset is loaded into the model and divided into calibration and validation parts for the purpose of model calibration. Loading mosaics for classification is controlled by a CSV file. The probability value with which the mosaic is classified as erosive or not is then added to this CSV file. The CSV file of the classified image is loaded back into the GIS environment using a Python script. The script loads the CSV file, and creates an according classified raster.
    Land cover
    Machine Learning
    Poster session
    Where
    Lobby - Poster session

  • Day 4 - Poster Session

    June 16 in the lobby

  • 11:30  - 11:50
    The high time required for hand rill identification is an obstacle in erosion research. Nevertheless, a significant number of grooves have already been manually marked for various studies. It is therefore possible to use these already obtained data to train an algorithm, which will then automatically identify the grooves. Before the simulation, during the pause between the simulations and after the experiment, the surface is scanned to create a 3D model. Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. The image is converted to a matrix, subsequently, this matrix is sequentially traversed in steps corresponding to the individual squares. The corresponding square is selected from each band and a mosaic is then created from these squares. The Kaggle Cat Dog model was used as the basis for creating the model. This model was modified by inserting mosaics into the model instead of images. The training dataset is loaded into the model and divided into calibration and validation parts for the purpose of model calibration. Loading mosaics for classification is controlled by a CSV file. The probability value with which the mosaic is classified as erosive or not is then added to this CSV file. The CSV file of the classified image is loaded back into the GIS environment using a Python script. The script loads the CSV file, and creates an according classified raster.
    AI
    Big Data
    Land cover
    Machine Learning
    Poster session
    Where
    Lobby - Poster session




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Adam Tejkl