Zoubir BARRAZ is a PhD candidate specializing in drone photogrammetry and deep learning, focusing on optimizing UAV-based photovoltaic inspection workflows. He has a background in geomatics science and land survey engineering. His research revolves around detecting and classifying anomalies in photovoltaic systems through a production-ready, multimodal deep learning approach. Zoubir has experience in UAV-based photogrammetry, 3D reconstruction, and computer vision, leveraging tools like TensorFlow, PyTorch, and OpenCV. He also has a strong background in developing web-based mapping interfaces to enhance user experiences in geospatial applications. His work demonstrates a holistic, innovative approach to integrating advanced AI techniques with real-world inspection and monitoring challenges.
