ANDRES HERNANDEZ-SERNA
Scientist
“We are at the very beginning of time for the human race. It is not unreasonable that we grapple with problems. But there are tens of thousands of years in the future. Our responsibility is to do what we can, learn what we can, improve the solutions, and pass them on" Richard Feynman. 1918 - 1988
RESEARCH
I'm a Principal Faculty Specialist at the Global Land Analysis and Discovery (GLAD) laboratory in the Department of Geographical Sciences at the University of Maryland, my expertise is centered on deep learning and its application in remote sensing and High-Performance Computing (HPC), aimed at enhancing spatial data analysis. My work primarily involves the processing and analysis of massive spatial datasets, with a focus on leveraging LiDAR and high-resolution spectral imaging technologies. My research activities are designed to manage and analyze data on a global scale, often reaching petabyte sizes.
Key areas of my research include:
Remote Sensing Techniques: I develop algorithms to process and analyze satellite imagery, notably from NASA's Landsat missions and the Planet constellation. My aim is to create detailed time-series maps of urban areas, managing vast datasets with spatial resolutions as 3 meters and time series extending back to 1984 at 30 meters resolution.
Deep Learning in Spatial Data Analysis: I spearhead the development and implementation of deep learning frameworks for analyzing spatial data. My work involves leveraging U-Net architectures and Generative Adversarial Networks (GANs), along with a 24-GPU setup, to significantly enhance the accuracy and efficiency of satellite image analysis.
LiDAR Data Processing: I co-developed pyGEDI, a Python package for analyzing data from NASA's Global Ecosystem Dynamics Investigation (GEDI). Additionally, I utilize LiDAR-RIEGL data from drones to assess biomass and tree height in various ecosystems.
Comparative Analysis of Remote Sensing Data: I conduct evaluations on the effectiveness of combining Planet, Landsat, and GEDI datasets for tree height measurement, covering a period from 2019 to the present.
Field Studies and Data Validation: I participate in fieldwork in diverse regions, including South America, Africa, and the US, to validate remote sensing data, especially in relation to agricultural expansion, forest change, and soybean cultivation.
Supported by robust HPC frameworks, my research stands at the forefront of spatial data analysis, essential for enhancing our understanding of environmental changes. This capability allows for unprecedented management and analysis of complex, large-scale datasets, crucial for informed decision-making in land management and conservation.