FIRESAT

Project Objectives

The project by Silicon Austria Labs and its partners OroraTech and Joanneum Research aims at realizing a proof-of-concept that explores feasibilities of an onboard-AI-enabled responsive EO mission. In FIRE-SAT tailored state-of-the-art machine-learning methodologies are deployed to operational satellite sensors to process RGB imaging data onboard for a remote fire detection use case. The satellites for the in-flight experiment are ESA's OPS-SAT mission (sun-synchronous dawn/dusk orbit 3U CubeSat launched 2019), which provides an experimental platform for registered users to test new ideas in mission scenarios and operations, and a satellite of the “NewSpace” company OroraTech. In in-flight experiments, the satellites’ live camera images are analysed onboard for the presence of smoke plumes by suitably trained and FPGA/GPU-implemented resource-constrained convolutional neural networks. In this regard the participation of the “NewSpace” company OroraTech – a provider of wildfire information services and a developer of a dedicated CubeSat constellation – as an international partner in the collaborative exploratory project, not only contributes in-depth use case & data expertise, but adds unique value to the project in terms of satellite availability and ecosystem beyond the OPS-SAT mission with a satellite that is optically compatible and complementary with respect to processing hardware (GPU vs. FPGA). The methodologies developed in the course of the project represent an approach that holistically investigates the feasibility of acquisition/generation of suitable data (training/validation set), machine learning modelling & experiment application concept and software development and the implementation on resource-constrained embedded hardware aspects of an EO mission enabled by onboard AI.