Proceedings of the 9th International Conference "Distributed Computing and Grid Technologies in Science and Education" (GRID'2021), Dubna, Russia, July 5-9, 2021 304 HIGH RESOLUTION IMAGE PROCESSING AND LAND COVER CLASSIFICATION FOR HYDRO- GEOMORPHOLOGICAL HIGH-RISK AREA MONITORING G. Miniello 1,3,a , M. La Salandra 2 1 Department of Physics, University of Bari, Italy 2 Department of Earth and Geoenvironmental Sciences, University of Bari, Italy 3 Istituto Nazionale di Fisica Nucleare – Sezione di Bari, Italy E-mail: a giorgia.miniello@ba.infn.it High-resolution image processing for land surface monitoring is fundamental to analyze the impact of different geomorphological processes on Earth surface for different climate change scenarios. In this context, photogrammetry is one of the most reliable techniques to generate high-resolution topographic data, being key to territorial mapping and change detection analysis of landforms in hydro-geomorphological high-risk areas. An important issue arises as soon as the main goal is to conduct analyses over extended areas of the Earth surface (such as fluvial systems) in a short time, since the need to capture large datasets to develop detailed topographic models may limit the photogrammetric process, due to the high demand of high-performance hardware. In order to investigate the best set up of computing resources for these very peculiar tasks, a study of the performance of a photogrammetric workflow based on a FOSS (Free Open-Source Software) SfM (Structure from Motion) algorithm using different cluster configurations was conducted, leveraging the computing power of ReCaS-Bari data center infrastructure, which hosts several services such as HTC, HPC, IaaS, PaaS. Exploiting the high-computing resources available at clusters and choosing specific set up for the workflow steps, an important reduction of several hours in the processing time was recorded, especially compared to classic photogrammetric programs processed on a single workstation with commercial softwares. The high quality of the image details can be used for land cover classification and preliminary change detection studies using Machine Learning techniques. A subset of the datasets used for the workflow implementation has been considered to test the performance of different Convolutional Neural Networks, using progressively more complex layer sequences, data augmentation and callback functions for training the models. All the results are given in terms of model accuracy and loss and performance evaluation. Keywords: Photogrammetry, Unmanned Aerial Vehicles, High-Resolution Data, ReCaS-Bari, Deep Neural Networks, Land Cover Classification Giorgia Miniello, Marco La Salandra Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).