How are Azure Cloud services important for Space technologies?

Overview

Microsoft’s Azure is undoubtedly one of the topmost Cloud Computing platforms that have revolutionized all businesses across all industries. The space tech industry is no exception. The wide range of services that we find in Azure is something that perfectly fits into the needs of Space technologies and research. 

A few interesting facts: 

1. In January 2023, Microsoft signed MoU with ISRO to empower Space tech startups in India. Source: ISRO and Microsoft collaborate to support space-tech startups in India – Microsoft Stories India.  
2. In October 2020, Microsoft and SpaceX collaborated to work on providing internet connectivity on Azure via the SpaceX Starlink satellite network. Source: Azure Space partners bring deep expertise to a new venture – Source (microsoft.com) 
3. Among the many collaborations, Microsoft and NASA worked on implementing Azure Quantum to manage Space Missions better. Source: NASA’s JPL uses Microsoft’s Azure Quantum to manage communication with space missions – Microsoft Azure Quantum Blog
4. Microsoft and ESRI, the world’s leading GIS software (ArcGIS) provider, have collaborated to enable usage of various Azure services like Azure Virtual Desktop, Azure IoT (Internet of Things) suite, Microsoft SQL Server Database, Geospatial AI (Artificial Intelligence) in Azure, to name a few, along with ArcGIS, for Geospatial Analysis. 
5. The Azure Space Partnership Community has been set up by Microsoft and its partners in the Space tech domain to co-innovate and work on creating world-class products and technologies for Space tech using Azure. Source: What is the Azure Orbital Space Partner Community? | Microsoft Learn 
6. In October 2020, Ansys, a company that owns the software by the same name, which is used extensively for engineering simulation and 3D design by Aerospace Industry, partnered with Microsoft to enable Cloud Based Services. Ansys Collaborates with Microsoft to Enhance Cloud Engineering Productivity 

Azure Orbital Ground station

Source: Azure Orbital Ground Station – Satellite Comms | Microsoft Azure

Now let us understand a few tools Azure houses that support the Space community for various innovations and technologies.

Azure for Geospatial Data Analytics 2.1. What is Geospatial Data?

Any data related to Earth and its related entities; their positioning relative to a point – are termed Geospatial Data. This data can come from various sources – Land surveys, Aerial Photographs, Satellite imageries, IoT devices, mobile phones, etc. The data from all these sources can be broadly classified as spatial and non-spatial data.

Spatial Data

This refers to the data that is related to the Earth’s surface. It provides the key to the relation between the location’s geometry and its attribute. This is organized in a specific theme and must-have features like point, line, and area. And is divided into two categories: Raster Data and Vector Data. Raster Data refers to the real-world features defined in the form of grids. These can be scanned images of Thematic maps, Digital Aerial Photographs, and Digital Satellite Images. Objects are defined using their Pixel density in these images. Vector data, on the other hand, refers to the real-world features defined by the coordinate pair of their location. They can be either defined as Points (buildings, poles, etc.), Lines (rivers, railway, or road networks, etc.), and Polygons (features with areal extents like lakes, political boundaries, natural vegetation cover, etc.). They are obtained by GPS-derived Data, Ground Survey Data, and so on.

How Real-World information is converted into Spatial Data

Source: How to Perform Spatial Analysis (esri.com)

Non-Spatial Data  

It refers to the objects that Spatial Data represent. These can be attributes of the objects that can be represented in tabular form.  

Other types of Data 

• Data that can change with time (temporal data),  
• Data that defines physical measurements, data that can be calculated from other available data (inferred data), or  
• Data about the existing data itself (metadata). 

2.2. Analytics of Geospatial Data

Geospatial Data’s nature is vast, and processing the same requires sophisticated tools. Geospatial Data Analytics involves working around these sophisticated tools in all its stages – be it for Data Acquisition and Retrieval, Data Analysis, or Data Presentation. 

• Acquiring the Geospatial Data  

Satellite imagery, Aerial Photography, and Ground Surveys are the common Data sources for acquiring Geospatial data. Acquiring data through satellite imagery involves designing the satellite for specific business requirements (climate studies, oceanography, urban planning, telecommunications, military purposes, or launching of satellites itself, and so on); tuning the Ground station to receive the radio signals from the satellites and maintaining their infrastructure; having necessary permissions to collect the data from the agencies that own it; ensuring data collection is compliant with all the policies in place and is secure against any potential cyber-threats. Aerial Photography and Ground Survey data acquisition also include sophisticated tools. This data can be obtained by India’s Bhuvan portal maintained by ISRO, US Geological Survey, and NASA Earth Observations, to name a few.

• Performing Data cleansing and Analysis  

Data thus obtained from various sources are available in various formats – IMG, PNG, GeoTIFF, DWG, DXF, GDB, Shapefile, and GeoJSON are a few formats for Spatial Data; CSV, PostgreSQL, SpatiaLite are a few formats for Non- Spatial Data. Data cleaning, data transformation, and data analytics for such data require sophisticated software and tools due to their vastness and rawness. Spatial data processing involves ingesting the data (real-time and stored) into the software system, transforming them for business requirements, and keeping them ready for service by enabling various machine learning operations and automating the entire process empowered by AI (Artificial Intelligence). This also involves overlaying the themes of raster data, making it compatible with image sources, and continuously generating the data into a meaningful presentation. Traditionally, these activities are performed using commercially off The Shelf software like ERDAS Imagine, ESRI’s ArcGIS, etc., or Free Open-Source Software like ILWIS, GRASS–GIS, Q-GIS, etc. 

• Presenting the Data  

Data thus generated are presented as maps, databases, and dashboards, all supporting data-driven decision-making for any business. 

2.3. Azure Architecture for Geospatial Data Analytics

General Geospatial Architecture in Azure

Source: Geospatial reference architecture – Azure Orbital | Microsoft Learn

Microsoft Azure houses various tools that help perform all computing activities quickly and efficiently, all while supporting low latency and quality. Various components in Azure support Geospatial analytics operations too. To enlist a few: 

• Data Ingestion  

Spaceborne data is pulled from sources obtained from providers like agriculture agencies, defense agencies, Government portals, etc., into Azure Data Lake Storage or Azure Cosmos DB. Azure Data Factory or Azure Synapse Analytics supports this operation. Azure Event Hubs ingest streams of IoT data for real-time ingestion of data.  

• Data transformation 

Geospatial libraries, including GDAL, OGR, Rasterio, and GeoPandas, can transform this Big Data with the help of Spark clusters available in Azure Synapse Analytics and Azure Databricks. The prepared raster and vector data can also be loaded into Azure Database for PostgreSQL and Azure Data Explorer. APIs (Application Programming Interfaces) make these data available in standard formats.  

• Analysis and execution of AI models 

Powerful business models are built with the support of Azure Machine Learning. Azure’s AI services, like Cognitive Services, Language Detection, Computer Vision, Knowledge mining, and Chatbots, help enable GPS services for public use.  

• Post-analysis and visualization 

The Web Apps feature of Azure App Service works with Azure Maps to create visuals of the data. GIS features of Azure Data Explorer work with Azure Maps to create insightful visualizations. Powerful dashboards can be created using PowerBI. 

All the above operations are monitored using Azure Monitor and Log Analytics services. Azure Key Vault helps maintain and secure the keys, secrets, and certificates. The entire activity can be virtualized using Azure Virtual Desktop. High-performing computing can be enabled with the support of Azure Batch. 

3. Azure Space and Azure Orbital

Launched in October 2020, Azure Space is a niche technology Microsoft offers. It is designed to enable Cloud connectivity for Space missions. Processing data from drones, aerial photography, satellite imagery, LiDAR, gridded model results, etc., is much easier with Azure technologies, including Azure Space. One of its products, Azure Orbital, is designed as a “Ground Station as a Service.” It provides the infrastructure to control the satellites and collect data from them, like how Ground Stations work. This technology enables the Space agencies to tap the highly scalable Cloud computing services provided by the low latency global network of Microsoft’s Infrastructure. It also helps them in better management at lower costs.   

Source: Azure Space – Satellite Connection and Innovation | Microsoft Azure

Briefly, the following are the steps taken to link a satellite with Azure Orbital: 

• Sign in to Azure 
• Register & authorize a spacecraft 
• Prepare your virtual machine (VM) and network to receive satellite data. 
• Configure a contact profile for the satellite downlink mission. 
• Schedule a contact with the satellite using Azure Orbital and save the downlinked data. 

Some salient features of Azure Orbital are: 

• The Data thus obtained is downloaded into Azure VNet.
• Contact scheduling is made available for Microsoft-owned and operated ground stations in X, S, and UHF band frequencies via shared high-gain antennas.
• It connects Azure global networking with the partner’s ground station network.
• SLA of 99.9% guaranteed. 

4. A note on the Microsoft Planetary Computer

Source: Home | Planetary Computer (microsoft.com)

The Microsoft Planetary Computer is another niche technology offered by Microsoft and launched in December 2020. It enables data-driven decision-making for scientists, researchers, policymakers, and developers by providing global-scale environmental monitoring capabilities. This platform consists of multi-petabyte catalogs of global environmental data with intuitive APIs. Keeping the world ecosystem as the main purpose, this technology aims at building advanced tools required to use AI, Azure storage services, and distributed computing frameworks. It also creates sustainability apps and maintains petabytes of environmental data and satellite images. The Global Flood Risk and Global Water Reservoir datasets are two examples of datasets available in the Planetary Computer. Azure technologies help generate curated maps and visualizations needed for business processes with the help of Microsoft Planetary Computer.

5. Potential use cases and scope of using Azure Cloud in Space

• Geospatial data technologies in Azure are highly beneficial to cater to the quick and demanding requirements of the Telecom industry, Aerospace industry, Mining Industry, Defence organizations, Government organizations, and organizations working on Urban Planning and Environment management.
• Microservices architecture in Azure, like Azure Kubernetes Services and Azure Container, helps in maintaining efficient computing operations for Drone technologies. 
• Aircraft engines can be monitored and maintained more efficiently using a Real-Time Analytics engine, Azure Stream Analytics, and highly scalable storage services like Azure Blobs, Azure Cosmos DB, Databases, etc. Data can be ingested in Real-time using Azure Event Hubs and Azure IoT hubs. 
• With Azure AI services’ support, Microsoft AirSim Drone Simulator helps in activities like rescue, simulation, robotics, aircraft, aerospace, and aviation industries. 

How can CloudThat help?

CloudThat is a pioneer in providing training and consultancy services, with over 10 years in the business. Their consultancy division includes supporting projects based on Data Architecting, Data Modelling and Analytics, IoT development, DevOps, AI/ML, and Supporting Infrastructure designing in Azure, to name a few, which supports Space Industry working with Azure. CloudThat also provides a platform for Well ARchitected (WAR) reviews for the Projects.
They are also a Microsoft Gold Partner, holding several standard certifications such as Azure Data Engineer Associate, Azure AI Engineer Associate, DevOps Engineer Expert, Azure Solutions Architect Expert, and Azure IoT Developer Specialty. These certifications provide valuable insights into using Azure services. Moreover, they offer customized training tailored to meet specific client requirements to understand Azure Cloud Services better. This can significantly improve the ability to handle workloads related to projects in the Space industry.  

About CloudThat

Incepted in 2012 is the first Indian organization to offer Cloud training and consultancy for mid-market and enterprise clients. Our business goal is to provide global services on Cloud Engineering, Cloud Training, and Cloud Expert Line. The expertise in all major cloud platforms including Microsoft Azure, Amazon Web Services (AWS), VMware, and Google Cloud Platform (GCP) position us as pioneers in the realm.