An Innovative and Modular Approach for Deep Learning for Constellations of Smallsats

Metadata Updated: November 12, 2020

The primary research and development goal of this research is to enable the usage of advanced machine learning algorithms, particularly deep learning, on embedded hardware systems such as satellites. This work would allow for powerful classification engines to be run on modest hardware, and lead the way for constellations of small satellites to perform near real-time triage of interesting events on the ground.The primary long term objective of this project is have a machine learning framework that is geared towards being used on highly constrained hardware, and to show that framework being used in a simulated distributed spacecraft application. In the short term, the research will focus on one particular application of deep learning as a proof of concept. In order to do this, the first objective will be to curate a dataset that will be used as a training set for all permutations of the deep learning artificial neural network (ANN). The next objective will be to determine an optimal architecture for the ANN, and then to implement it in software for testing. Finally, once an optimal architecture is chosen, it will be mapped to hardware using energy efficient FPGAs.The secondary objective is to use the previously developed ANN to do scene classification on real data, such as the AVIRIS, MODIS, and Hyperion multispectral data sets. The ANN will be trained to look for interesting scenes in a vast amount of data, and can be configured a number of different ways in order to look for fires, floods, algal blooms, and other situations. The final milestone will be a full definition of a prototypical mission that can be used as an example of the proper use of the framework. This will include a generalization of the framework, which means that more cores will be added to accommodate a wider range of ANN architectures. Additionally, a mission will be described for using the framework to do near real-time detection of wildfires by analyzing multispectral data with the ANN developed in the previous milestone. This milestone is the final one for FY17 and will conclude in September.

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Dates

Metadata Created Date November 12, 2020
Metadata Updated Date November 12, 2020

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date November 12, 2020
Metadata Updated Date November 12, 2020
Publisher Space Technology Mission Directorate
Unique Identifier Unknown
Maintainer
Identifier TECHPORT_90791
Data First Published 2018-09-01
Data Last Modified 2020-01-29
Public Access Level public
Bureau Code 026:00
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://data.nasa.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Homepage URL https://techport.nasa.gov/view/90791
Program Code 026:027
Source Datajson Identifier True
Source Hash 101eb0c4e83c08076f2417f464482a84ab7dc74b
Source Schema Version 1.1

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