Satellites have long contributed to monitoring Earth from space. The implications of this technology have been so grave that it has cemented itself as a critical tool for most nations around the world. But there is still a lot to be desired.
Since the first Earth observation prototypes using Synthetic Aperture Radar (SAR) were developed, our ability to capture imagery and subsequently, our access to data and information about disaster-struck areas, conflict zones, and more, has been revolutionized.
But what is perhaps most exciting is the possibility that can be achieved with SAR. Despite representing a modest portion of the global space economy, the Earth observation (EO) sector has experienced exponential growth and according to Morgan Stanley Research, is estimated to skyrocket from a value of $4.4 billion to a staggering $25.273 billion by 2040.
This rampant growth, alongside the development of AI and machine learning technologies, and a commitment to collaborate and share information, is creating a fertile ground for pushing the boundaries of what SAR-powered EO is capable of.
Mission So Far
In recent years, facilitated by advancements in satellite imaging, remote sensing, and data analytics, SAR has revolutionised our ability to understand and monitor the dynamics of our planet. The increasing demand for accurate and timely environmental data coupled with the rise of commercial space ventures is empowering faster delivery of data to tackle global challenges such as climate change, natural resource management, and disaster response.
SAR satellites, unhindered by low visibility at night or adverse weather, offer a clear and comprehensive perspective of Earth’s surface and movements. SAR data contains information that contributes to the understanding of the shape and physical properties of the terrain and structures. Furthermore, SAR enables monitoring of various objects, from urban vehicles to disaster-prone buildings and maritime vessels at major ports.
This capability makes SAR satellites suited to time-series analysis, facilitating continuous monitoring across several applications on land and at sea. Advancements in design, computing and reduced launch costs have made small satellites more widely available. These compact alternatives, being more cost-effective and lighter, have made the deployment of multiple SAR satellites on a single payload possible, expanding the accessibility of space exploration.
This availability of small SAR satellite constellations enables near real-time global observations with unprecedented detail and high-resolution image clarity.
Towards Common Goal
The enhancement of EO technology is not merely a scientific endeavour: it’s a critical tool for informed decision-making across various sectors and applications. From monitoring environmental health and biodiversity to supporting national security initiatives, urban planning efforts, and disaster response operations, EO data provides invaluable insights that drive evidence-based decisions across the entire value chain.
Yet collaboration between government agencies, research institutions, industry stakeholders, and international organisations must be leveraged to accelerate technological innovation, data sharing, and capacity-building efforts. A data-driven approach and collective learning are of paramount importance for the creation of a better world for future generations.
Shared knowledge will result in standardized processes and abilities, unlocking a generation of innovative applications and use cases through open data. We have already seen the value of open-source SAR imagery to help track conflicts and disasters. Collective learning, drawing from diverse backgrounds and experiences also plays a critical role in the achievement of systematic innovation.
To this end, a data-driven approach is also imperative, along with the integration of academia with international entities like the United Nations and the World Bank to ensure a linear global progression in knowledge accumulation. International stakeholders can establish analytical platforms to facilitate learning, while companies can contribute through APIs and insights, fostering collaboration.
Synspective relies on the development of close partnerships with experts in construction, engineering consultancies, and insurance companies. We provide the analytic datasets, but our solutions are developed through discussions with these experts.
New Tech Landscape
There are unavoidable questions about the role that Artificial Intelligence (AI) will play in society. For EO at least, AI-powered algorithms and Machine Learning (ML) techniques enable automated analysis, pattern recognition, and predictive modelling, unlocking new possibilities for extracting actionable insights at scale.
The combination of SAR satellite constellation with an AI-driven analytics platform can unlock the path toward a continuously learning world, enabling customers across industries to make data backed timely decisions. AI’s capacity to predict, analyse, and interpret vast amounts of data in real time is poised to revolutionise the field.
Despite the wealth of EO data available from satellites and ground-based sensors, the challenge lies in deciphering complex patterns, identifying relevant trends, and extracting intelligence on time. AI algorithms offer powerful tools for detecting anomalies and generating predictive analytics. By leveraging AI capabilities, EO practitioners can enhance data processing efficiency, improve decision-making accuracy, and unlock new applications across diverse sectors.
EO data serves as the backbone for geospatial analytics, enabling the creation of actionable insights. Generative AI, a cutting-edge technology, has the potential to revolutionise the geospatial industry with its capabilities to streamline processes, such as land cover mapping and 3D modelling, while also empowering predictive analytics for future scenarios.
The impact of improved SAR data processing has already been witnessed and can give us a preview of what might be achieved with AI. In 2011, during the Tōhoku earthquake in Japan, there wasn’t a sufficient amount of satellite data to provide immediate information for risk assessment and recovery activities.
The Noto Peninsula earthquake of 2024 highlighted SAR satellites as a crucial tool in effective natural disaster management. Other than the obvious path to enhance our capabilities by increasing the number of satellites providing data, and applying AI capabilities to reduce processing times, the solution lies in sharing live URLs of satellite data to help with disaster management efforts in the event of earthquakes, floods, or forest fires.
Managing Disasters
Within the next few years, we anticipate being able to predict damages from a disaster and have enough information to proactively prepare and minimise the impacts, while leveraging relevant data for the planning and construction of robust infrastructures, all thanks to AI capabilities.
By harnessing historical disaster data and real-time satellite imagery, AI-powered algorithms can generate predictive models that forecast potential hazards, assess vulnerability, and prioritize resource allocation.
Through ML techniques, AI systems can analyse satellite images to identify the damage, help establish disaster-resilient infrastructures, and support decision-making in crises. For example, accurate landslide risk assessments can be provided, highlighting potential risk areas. By integrating AI-driven solutions into disaster management workflows, stakeholders can mitigate risks, reduce response times, and enhance community resilience.
With four satellites combining SAR data with advanced machine learning currently in Low Earth Orbit (LEO), Synspective delivers data and analytical services across several industries. Its proprietary Land Displacement Monitoring (LDM) solution uses interferometric SAR (InSAR) analysis to monitor ground risks, particularly beneficial for reclaimed lands and mining sites.
Continuous monitoring with LDM can help track historical changes and find patterns using a cloud-based data platform. Notably, LDM’s subsidence detection utilizes a unique algorithm, providing early alerts for potential ground deformations, crucial in the prevention of disasters in facilities such as tailing dams.
A Better Future
Continued advancements in EO technologies are indispensable for addressing complex global challenges, including climate change research, natural resource management, and disaster risk reduction. The integration of AI capabilities catalyses sectoral development, enabling the rapid generation of actionable insights and data-driven decision-making.
By harnessing the synergy between EO and AI, societies can better understand, monitor, and manage the Earth’s dynamic systems, promoting sustainable development in an increasingly interconnected world.
