Availability, documentation, & community support for an open-source machine learning tool
We will make cutting-edge spectral analysis and machine learning algorithms available to remote sensing and chemical quantification communities, regardless of the user’s programming skills, by releasing, documenting, presenting, and developing tutorials for the Python Hyperspectral Analysis Tool.
Remote sensing analysis is performed by nearly all USGS mission areas. However, the complex data reduction and machine learning algorithms necessary to make the best use of remote sensing data are barriers for non-experts. With CDI support, we would make these cutting-edge computational tools available to a wide range of scientific communities. By documenting the Python Hyperspectral Analysis Tool (PyHAT), developing relevant tutorials, releasing the code publicly, and promoting the tool we would increase the equity and approachability of these algorithms for novice programmers. We would also codify the process for external contributions, document the code, and produce an Open File Report (OFR) to ensure the sustainability of the project and provide citable resources. With CDI support, we would also promote PyHAT on official USGS websites and at conferences, and provide a lessons-learned internal document on our development hurdles to help increase the impact and reach of other USGS software.
We will make cutting-edge spectral analysis and machine learning algorithms available to remote sensing and chemical quantification communities, regardless of the user’s programming skills, by releasing, documenting, presenting, and developing tutorials for the Python Hyperspectral Analysis Tool.
Remote sensing analysis is performed by nearly all USGS mission areas. However, the complex data reduction and machine learning algorithms necessary to make the best use of remote sensing data are barriers for non-experts. With CDI support, we would make these cutting-edge computational tools available to a wide range of scientific communities. By documenting the Python Hyperspectral Analysis Tool (PyHAT), developing relevant tutorials, releasing the code publicly, and promoting the tool we would increase the equity and approachability of these algorithms for novice programmers. We would also codify the process for external contributions, document the code, and produce an Open File Report (OFR) to ensure the sustainability of the project and provide citable resources. With CDI support, we would also promote PyHAT on official USGS websites and at conferences, and provide a lessons-learned internal document on our development hurdles to help increase the impact and reach of other USGS software.