Research

Growing global populations have increasingly engaged in unsustainable practices, leading to the alarming depletion of natural resources, disruption of biodiversity, and climate disturbances. These actions pose significant threats to the well-being of future generations. In order to comprehend and address these land transformations effectively, the application of imaging technologies has facilitated the observation and comprehension of these complex changes and underlying drivers. By harnessing such technologies, we can design sustainable solutions and make informed decisions to mitigate these challenges. Notably, microwave remote sensing data, specifically Synthetic Aperture Radar (SAR), has emerged as a game-changing tool, providing near real-time imaging capabilities regardless of weather conditions or time of day. However, the sheer volume of SAR image data acquired from various satellites in orbit exceeds the capacity of skilled professionals available for analysis, hindering the derivation of valuable insights.

To address this disparity, my research focuses on machine learning-based approaches that process vast amounts of global-scale data and autonomously extract intricate insights from SAR imagery, thereby reducing the need for extensive human involvement. My ambition lies in developing robust machine learning methodologies that leverage radar remote sensing images to derive accurate, reliable, and actionable information pertaining to large-scale land changes and their underlying drivers. By enabling this advancement, I aim to bridge the gap between the growing need for comprehensive analysis of SAR data and the limited availability of skilled experts, ultimately facilitating sustainable land management practices.