# Learnings from Frontier Development Lab and SpaceML -- AI Accelerators for NASA and ESA

arXiv: https://arxiv.org/abs/2011.04776 · HTML: https://mlsanity.com/paper/2011.04776/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Reducing Effects of Swath Gaps on Unsupervised Machine Learning Models for NASA MODIS Instruments](https://mlsanity.com/paper/2106.07113/) (2021)
- [Technology Readiness Levels for Machine Learning Systems](https://mlsanity.com/paper/2101.03989/) (2021)
- [Scalable Data Balancing for Unlabeled Satellite Imagery](https://mlsanity.com/paper/2107.03227/) (2021)
- [Scalable Reverse Image Search Engine for NASAWorldview](https://mlsanity.com/paper/2108.04479/) (2021)

## Abstract (arXiv)

Research with AI and ML technologies lives in a variety of settings with often asynchronous goals and timelines: academic labs and government organizations pursue open-ended research focusing on discoveries with long-term value, while research in industry is driven by commercial pursuits and hence focuses on short-term timelines and return on investment. The journey from research to product is often tacit or ad hoc, resulting in technology transition failures, further exacerbated when research and development is interorganizational and interdisciplinary. Even more, much of the ability to produce results remains locked in the private repositories and know-how of the individual researcher, slowing the impact on future research by others and contributing to the ML community's challenges in reproducibility. With research organizations focused on an exploding array of fields, opportunities for the handover and maturation of interdisciplinary research reduce. With these tensions, we see an emerging need to measure the correctness, impact, and relevance of research during its development to enable better collaboration, improved reproducibility, faster progress, and more trusted outcomes. We perform a case study of the Frontier Development Lab (FDL), an AI accelerator under a public-private partnership from NASA and ESA. FDL research follows principled practices that are grounded in responsible development, conduct, and dissemination of AI research, enabling FDL to churn successful interdisciplinary and interorganizational research projects, measured through NASA's Technology Readiness Levels. We also take a look at the SpaceML Open Source Research Program, which helps accelerate and transition FDL's research to deployable projects with wide spread adoption amongst citizen scientists.