Jaya Venkatesh
Jaya Venkatesh is a software engineer at NVIDIA, working on the RAPIDS ecosystem with a focus on simplifying deployment in the cloud and distributed systems. Previously, Jaya worked as a machine learning engineer at Pixxel Space, where he developed large scale, real-time inferencing models for Earth Observation. He holds a Master’s degree in Computer Science from Arizona State University, where his research project centered on snow melt monitoring in the Arizona region through satellite imagery analysis.
Session
Geospatial analysis often relies on raster data, n‑dimensional arrays where each cell holds a spatial measurement. Many raster operations, such as computing indices, statistical analysis, and classification, are naturally parallelizable and ideal for GPU acceleration.
This talk demonstrates an end‑to‑end GPU‑accelerated semantic segmentation pipeline for classifying satellite imagery into multiple land cover types. Starting with cloud-hosted imagery, we will process data in chunks, compute features, train a machine learning model, and run large-scale predictions. This process is accelerated with the open-source RAPIDS ecosystem, including Xarray, cuML, and Dask, often requiring only minor changes to familiar data science workflows.
Attendees who work with raster data or other parallelizable, computationally intensive workflows will benefit most from this talk, which focuses on GPU acceleration techniques. While the talk draws from geospatial analysis, key geospatial concepts will be introduced for beginners. The methods demonstrated can be applied broadly across domains to accelerate large-scale data processing.