# Accelerating Deep Learning Applications in Space

arXiv: https://arxiv.org/abs/2007.11089 · HTML: https://mlsanity.com/paper/2007.11089/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2020.

## Built on by (most cited first, 9 of 9)

- [HyperspectralViTs: General Hyperspectral Models for On-board Remote Sensing](https://mlsanity.com/paper/2410.17248/) (2024)
- [Reaching the Edge of the Edge: Image Analysis in Space](https://mlsanity.com/paper/2301.04954/) (2023)
- [Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT](https://mlsanity.com/paper/2404.08399/) (2024)
- [Machine Learning in Space: Surveying the Robustness of on-board ML models to Radiation](https://mlsanity.com/paper/2405.02642/) (2024)
- [Optimizing Data Processing in Space for Object Detection in Satellite Imagery](https://mlsanity.com/paper/2107.03774/) (2021)
- [Mapping suburban bicycle lanes using street scene images and deep learning](https://mlsanity.com/paper/2204.12701/) (2022)
- [Accelerating AI and Computer Vision for Satellite Pose Estimation on the Intel Myriad X Embedded SoC](https://mlsanity.com/paper/2409.12939/) (2024)
- [Convolutional Autoencoders for Data Compression and Anomaly Detection in Small Satellite Technologies](https://mlsanity.com/paper/2505.00040/) (2025)
- [SatFusion: A Unified Framework for Enhancing Remote Sensing Images via Multi-Frame and Multi-Source Images Fusion](https://mlsanity.com/paper/2510.07905/) (2025)

## Abstract (arXiv)

Computing at the edge offers intriguing possibilities for the development of autonomy and artificial intelligence. The advancements in autonomous technologies and the resurgence of computer vision have led to a rise in demand for fast and reliable deep learning applications. In recent years, the industry has introduced devices with impressive processing power to perform various object detection tasks. However, with real-time detection, devices are constrained in memory, computational capacity, and power, which may compromise the overall performance. This could be solved either by optimizing the object detector or modifying the images. In this paper, we investigate the performance of CNN-based object detectors on constrained devices when applying different image compression techniques. We examine the capabilities of a NVIDIA Jetson Nano; a low-power, high-performance computer, with an integrated GPU, small enough to fit on-board a CubeSat. We take a closer look at the Single Shot MultiBox Detector (SSD) and Region-based Fully Convolutional Network (R-FCN) that are pre-trained on DOTA - a Large Scale Dataset for Object Detection in Aerial Images. The performance is measured in terms of inference time, memory consumption, and accuracy. By applying image compression techniques, we are able to optimize performance. The two techniques applied, lossless compression and image scaling, improves speed and memory consumption with no or little change in accuracy. The image scaling technique achieves a 100% runnable dataset and we suggest combining both techniques in order to optimize the speed/memory/accuracy trade-off.