What is geospatial data?
Geospatial data, or geodata, is data that includes information related to locations on the Earth’s surface. You can map objects, events, and other real-world phenomena to a specific geographical area identified by latitude and longitude coordinates. Geospatial data combines location information with characteristics or attributes of other business datasets over a given period. For example, geospatial data can represent where a car is parked and also track its location when it starts moving.
What is geospatial visualization?
Geospatial visualization is when you create visualizations (such as maps and 3D models) using geospatial data. Scientists typically combine data from several sources to create a complete picture of a location. For example, they combine soil quality, water quality, and temperature data to find agricultural land with the most significant yield potential.
Geospatial visualization develops a high-level understanding of specific themes, patterns, and trends on the Earth’s surface. This type of visualization brings insight into the environment, revealing its impact on people and natural life. Geospatial visualization typically involves using geospatial technology to create interactive 3D maps and graphics.
What is geospatial intelligence?
Geospatial intelligence is a term that describes the collection, analysis, and interpretation of geospatial data for informed decision-making. It combines geographic data with various other types of intelligence—including imagery, signals intelligence, and human intelligence—to accurately represent a location and its physical environment. You can use geospatial data analysis to identify patterns, assess emerging trends, identify potential threats and risks, and make data-driven decisions. Geospatial analysis is critical in military planning, emergency response, and environmental management.
What are some benefits of using geospatial data?
Businesses using geospatial technologies can achieve several benefits, which we outline below.
To collect information, geospatial data systems use complex technology such as the Global Positioning System (GPS), lasers, and remote sensing. This data is the basis for digital maps and 3D models of geographic locations. The increased accuracy yielded from geospatial data is beneficial for a variety of applications such as navigation, asset management, and risk assessment.
Geospatial technologies can help you save costs in many ways. For example, they can assist you in automating data collection and can reduce the cost of mapping and surveying projects. Additionally, the technology helps streamline processes such as asset management and resource allocation, which results in greater efficiency and cost savings.
You can use geospatial data to improve safety, as it provides a comprehensive understanding of surrounding environments. For example, you can build 3D maps of areas that highlight risks and hazards. The maps will change and send alerts if new issues emerge due to weather, traffic congestion, or natural disasters.
Efficient resource management
You can use geospatial data to identify and analyze resource availability, prioritize resources, and track usage. It helps you make better-informed choices about resource allocation and utilization. Additionally, you can use geospatial data to monitor and manage natural resources, such as water, forests, and wildlife.
What are some examples of geospatial applications?
Many enterprise businesses use geospatial software to provide new services to customers.
Arup works toward sustainable urban development
Arup is a business dedicated to sustainable development. It combines geospatial data with the machine learning capabilities of Amazon SageMaker to measure urban heat factors. Arup’s engineering teams carry out previously impossible analytics work to advise clients on sustainable urban planning.
Read how Arup uses SageMaker »
Xarvio creates digital farming solutions
Xarvio creates digital farming products to help farmers optimize crop production. It combines geospatial data with crop and disease models. This helps to deliver accurate and timely agronomic recommendations and manage the needs of individual fields. By using the geospatial capabilities of SageMaker, Xarvio can process a large volume of satellite imagery daily.
Read how Xarvio uses SageMaker »
EarthOptics measures and maps soil data
EarthOptics is a soil data measurement and mapping company. It uses proprietary sensor technology and geospatial data analytics to precisely measure the health and structure of soil. The geospatial machine learning capabilities of SageMaker allowed EarthOptics to rapidly prototype algorithms for soil carbon sequestration. These algorithms were then deployed for farms and ranches across the United States.
What are the types of geospatial data?
There are two basic geospatial data types—vector data and raster data.
Vector data is geospatial information that uses digital elements like points, lines, and polygons to represent geographic features. Vector data often represents physical features such as roads, rivers, and city boundaries. Vector data is widely used in geospatial information applications such as mapping, location information, and navigation.
Rasters are geospatial data models that define space as a grid of equally sized cells. Each grid cell represents location information like temperature or soil quality. The raster is overlaid on a map image. For example, map images are sourced from satellite imagery, digital aerial photographs, and scanned maps. You can use raster data to create digital elevation models, as shown below.
What technologies are used to collect geospatial data?
Several technologies are used to create and store geographic information. We outline some of the most popular methods below.
Geographic information systems
Geographic information systems (GIS) are software systems that create, manage, analyze, and map all data types. They combine location data with information about how things are in that location. GIS applies geographic science with tools for understanding and collaboration. For example, you can use GIS technology to collect and present data on an area's population, incomes, and other demographics. Additionally, GIS can analyze changes in regions over time, such as population density or changes to land use.
Global Positioning System
The Global Positioning System (GPS) collects exact location data required for geospatial systems. GPS receivers pick up radio signals from a satellite network to calculate precise positions on the Earth's surface. You can use GPS data to improve the location accuracy of geospatial applications and track changes in real time.
Drones collect geospatial data in various ways, including aerial photography, videos, and other imagery. You can create 3D models of geographical areas and derive locational information such as land use and environmental conditions. Additionally, you can use drones to measure distances and elevations as well as identify potential risks.
How can AWS support your geospatial data requirements?
Geospatial machine learning (ML) with Amazon SageMaker allows data scientists and engineers to efficiently build, train, and deploy ML models using geospatial data. Users can access geospatial data sources, purpose-built processing operations, pretrained ML models, and built-in visualization tools to run geospatial applications faster and at scale.
You can enjoy several benefits with SageMaker:
- Straightforward access to multiple sources of geospatial data.
- Access to prebuilt operations and common code to transform existing geospatial data collection.
- Rapid preprocessing of large-scale geospatial workloads.
- Prebuilt models to start making geospatial predictions.
- Collaboration on insights from geospatial forecasts. You can use built-in visualization tools.
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