Melih Altay is a researcher in geomatics engineering with a strong specialization in photogrammetry, artificial intelligence, and remote sensing. His research is centered on integrating deep learning and machine learning methods with multi-source Earth observation data to address complex geospatial problems. He has developed and applied advanced AI-based segmentation, object detection, and classification approaches for analyzing optical and SAR satellite imagery, with particular emphasis on forest fire assessment, water surface detection, and agricultural land monitoring. His work contributes to improving the accuracy and automation of geospatial data extraction from high-resolution satellite platforms such as PlanetScope and Sentinel series. His research demonstrates strong interdisciplinary integration of GIS, remote sensing, and artificial intelligence, offering scalable solutions for environmental monitoring, land-use analysis, and spatial decision support. Through peer-reviewed conference publications, he has contributed comparative evaluations of deep learning architectures and innovative workflows that enhance geospatial analysis efficiency and reliability. Overall, his work reflects a forward-looking approach to geomatics engineering, emphasizing intelligent automation, high-resolution spatial analytics, and the practical application of AI technologies in Earth observation and digital twin systems.