SpaceTech Digital Twins

Data generated from space has been a good success story for end-user applications, to a large extent the use of maps for navigation and GPS-based tracking for everything from traffic in my neighbourhood to the status of my 10-minute grocery deliveries. Outside the realm of consumers, there are applications in areas of infrastructure, defence, weather, agriculture, etc. These sectors rely on expensive computing to get their results - after many hours (if not days) of computing on GPUs. There is a gap between handheld smartphones using space-generated data and critical sectors using GPUs to crunch the numbers, and this is an area worth exploring for applications using space-generated data.

Satellite data offers a unique perspective of our world from high above, capturing a vast array of information including high-resolution imagery, environmental monitoring, and human activity tracking. Human activity tracking was not possible just 20 years ago, and now one of the most comprehensive sources of accurate human mobility information. Combine this with a broad, planetary view, and one can start filling up the gaps between 10-minute deliveries and weather models. Phenomena like climate change, urban expansion, and natural resource management are just some examples of combining human mobility insights with natural physical parameters captured at a planetary scale.

However, while satellite data is invaluable, it often lacks the granularity and context provided by on-the-ground data sources. Ground-based sensors, drone footage, pointclouds, computer-aided drawing and building information models provide critical, detailed insights into specific assets, processes, and environments. They provide insights to understand local conditions, monitor precise changes, and make informed decisions at a micro level. Since these systems are designed to be specific, they miss the broader context that satellite data provide. There's a natural complementarity between ground-based methods and data generated from the low-earth orbit, and combining both gives a great journey from bird's eye view to worm's eye view. There are areas inbetween where planes and drones can contribute to enrich this data source, but that's for another post.

There's a natural complementarity between ground-based methods and data generated from the low-earth orbit.

Unifying The View From Space

This is where digital twins offer a comprehensive solution by merging data from both terrestrial and satellite sources. This fusion build smarter, more comprehensive digital twin models - to understand interconnected systems well, identify patterns and trends at scale, and make decisions for global impact. The comprehensive visibility unlocks new applications in areas of:

• Land use, land cover for deforestation and urbanization.
• Tracking human mobility patterns for urban planning.
• Mine reclamation where we restore mines to the pre-mining conditions.
• Traffic regulation for safe usage of roads.

Some of the key challenges:

1. Data Volume and Variety: The datasets generated from satellite constellations are massive, comprising petabytes of imagery and other sensor readings in multiple formats, resolutions, and spatiotemporal framings. Efficiently structuring, processing, and modelling this data is complex.
2. Varying Context and Resolution: While satellites provide the global context, there are resolution and perspective gaps compared to ground-level sensing. Fusing these datasets into coherent multi-scale models requires robust data fusion and transformation capabilities.
3. Timely Data Pipelines: Many digital twin use cases require low-latency data pipelines to incorporate streaming updates from space in real- or near-real time, enabling the monitoring of dynamic phenomena.

We have made some progress to address these challenges, but it's just the beginning and use-cases will prioritise the data-types that will be prioritised.

The Future of Digital Twins and SpaceTech

As the capabilities of satellite constellations continue to advance and digital twin platforms become more robust, we'll see the emergence of increasingly sophisticated virtual worlds that blur the lines between the physical and digital realms. Leveraging AI, machine learning, and advanced simulations, these hyper-connected digital twins will continuously learn, evolve, and automatically prescribe solutions in response to the real world.

While these types of autonomous digital twin environments are still on the horizon, innovators pushing the boundaries of space tech and digital twin development are bringing that future closer every day. The opportunities to build more sustainable, resilient, and optimized systems have never been greater. By unleashing the power of digital twins with space-based data, we can create a smarter, better world.