Our goal is to transform data into actionable information that can be utilized effectively. Raw data is processed to create a digital model of the project site. Data products are generated through both traditional analysis and machine learning algorithms and include:
Feature Extraction
This analysis allows the user to isolate, classify and quantify features from a digital model. These features can be objects, conditions or characteristics depending on the project requirements. This analysis can be used on imagery, LiDAR and geophysical data.
Spatial Modeling
The features in a digital model can be analyzed based on their location and physical characteristics. The simplest form of spatial modeling is the creation of an elevation or “topo map.” This method can be used across a number of applications and industries to better understand how things relate to each other across a defined space.
Our goal is to transform data into actionable information that can be utilized effectively. Our skilled data analysis team expertly filters out the noise and delivers the insights you need.
Geotech Support
Geohazards pose a significant risk to critical infrastructure throughout the world, but particularly in mountainous regions. High resolution imagery and elevation data provides critical information for geotechnical investigations.
Temporal Analysis
Also known as “change over time” or “4D analysis,” this method locates and quantifies differences in data sets of the same site collected at different times. The magnitude of variation can be used to determine rates of change by comparing data from two different known time periods.
Machine Learning Analysis
Using machine learning techniques, Elevate can identify, spatially locate and quantify features with the data. We make use of analysis that can identify objects such as cracks, spalls and repair patches on facades. In addition, our ML software is used for analysis of roadways and other critical infrastructure.