Planetary Rover Simulation for Lunar Exploration Missions
Overview of synthetic lunar terrain generation methods used for the Resource Prospector and Volatiles Investigating Polar Exploration Rover projects.
simulation↗
Engineering topics
Scott Mcmichael
Publications and source records attributed to Scott Mcmichael.
DELTA: An Open-Source Framework to Simplify Deep Learning with Satellite Imagery
DELTA (Deep Earth Learning, Tools, and Analysis) is an open-source framework developed at NASA for deep learning on satellite imagery based on tensorflow. It helps simplify data engineering and preprocessing steps and reduces the need for a lot of the boilerplate code that needs written to make datasets palatable for machine learning. This lets data scientists focus on model development while DELTA handles the grunt work. This presentation will demonstrate DELTA’s functionality and share some examples from an active project using it for flood mapping.
Michael von Pohle↗
DELTA: An Open-Source Framework to Simplify Machine Learning with Satellite Imagery
DELTA (Deep Earth Learning, Tools, and Analysis) is an open-source framework developed at NASA to simplify running and training machine learning (ML) models on satellite imagery. Users new to machine learning can run existing ML models on satellite imagery with minimal setup and configuration. For experienced ML users, DELTA helps simplify data engineering, preprocessing steps, and reduces the need for boilerplate code that needs written to make satellite imagery datasets palatable for machine learning. This lets data scientists focus on model development while DELTA handles the imagery manipulation. This presentation will demonstrate DELTA’s functionality and share some examples from an active project using it for flood mapping using imagery from multiple satellite sources.
deep learning↗