Amazon Web Services (AWS), the cloud computing arm of e-commerce giant Amazon has made number of announcements at the AWSre:Invent 2022, AWS’s mega event.
The announcements focused towards cloud services, data management, intelligent automation, a new chip (hardware) in the cloud and so much more.
AWS VP and Chief Evangelist Jeff Barr, plus a select group of AWS Developer Advocate colleagues, have personally chosen their picks for some of the most impactful and exciting product and service launches to debut at AWS re:Invent 2022.
Here we talk about two of the other key AWS announcements: AWS SimSpace Weaver and Amazon SageMaker’s new geospatial capabilities.
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AWS SimSpace Weaver
AWS announced AWS SimSpace Weaver, which is a new fully managed computer service helps deploy large-scale spatial simulations in the cloud.
With SimSpace Weaver, user can create seamless virtual worlds with millions of objects that can interact with one another in real time without managing the backend infrastructure.
Organizations run simulations on situations that are rare, dangerous, or very expensive to test in the real world.
For example, city managers can’t wait for a natural disaster to hit a city to test the response systems. Event planners don’t want to wait until a large sporting event to start to understand the impact the games will have on traffic.
Scenarios like these need to be simulated in a safe environment in which planners can test different situations and tune each system.
Until today, spatial simulations were generally confined to being run on a single piece of hardware. If developers wanted to simulate a bigger and more complex world with lots of independent and dynamic entities, they needed to provision a bigger computer.
Simulation developers were forced to make trade-offs between scale and fidelity, in other words, deciding how big the world is and how many independent entities there are.
The world we live in is complex, and the scenarios that developers want to simulate are very complex as well—for example, how traffic will be affected by a large concert or sporting event.
Simulating these events requires modeling hundreds of thousands of independent dynamic entities to represent the people and vehicles. Each entity has its own set of behaviors that need to be modeled as it moves throughout the world and interacts with other entities. Simulating this at a real-world scale requires CPU and memory beyond what you can have in one instance.
The company said that customers can deploy spatial simulations to model dynamic systems with many data points, such as traffic patterns across an entire city, crowd flows in a venue or factory-floor layouts, and then use the simulations to visualise physical spaces, perform immersive training and get insights on different scenarios to make informed decisions.
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Amazon SageMaker’s new geospatial capabilities
People use map apps every day to find addresses of restaurants, malls or look for fastest travel route to their destinations, which basically use geospatial data.
There are two types of geospatial data: vector data that uses two-dimensional geometries such as a building location (points), roads (lines), or land boundary (polygons), and raster data such as satellite and aerial images.
AWS announced the release of Amazon SageMaker’s new geospatial capabilities that make it easy to build, train and deploy ML models using geospatial data. This collection of features offers pre-trained deep neural network (DNN) models and geospatial operators that make it easy to access and prepare large geospatial datasets. All generated predictions can be visualized and explored on the map.
Also, user can use the new geospatial image to transform and visualize data inside geospatial notebooks using open-source libraries such as NumPy, GDAL, GeoPandas, and Rasterio, as well as SageMaker-specific libraries.
With a few clicks in the SageMaker Studio console, a fully integrated development environment (IDE) for ML, user can run an Earth Observation job, such as a land cover segmentation or launch notebooks. User can bring various geospatial data, for example, their own Planet Labs satellite data from Amazon S3, or US Geological Survey LANDSAT and Sentinel-2 images from Open Data on AWS, Amazon Location Service, or bring your own data, such as location data generated from GPS devices, connected vehicles or internet of things (IoT) sensors, retail store foot traffic, geo-marketing and census data.
The Amazon SageMaker geospatial capabilities support use cases across any industry. For example, insurance companies can use satellite images to analyze the damage impact from natural disasters on local economies, and agriculture companies can track the health of crops, predict harvest yield, and forecast regional demand for agricultural produce.
Retailers can combine location and map data with competitive intelligence to optimize new store locations worldwide. These are just a few of the example use cases.
Swami Sivasubramanian, Vice President of AWS database, analytics, and machine learning announced eight new capabilities for Amazon SageMaker. One of these includes a new level of support for geospatial data, which will make it easier to develop models for climate science, urban planning, disaster response, precision agriculture, and more.
“These new Amazon Sagemaker capabilities are built for customers to take advantage of ML at scale and for social change,” he said.
