20230092122. SYSTEM AND METHOD FOR A GLOBAL DIGITAL ELEVATION MODEL simplified abstract (Climate Central)

From WikiPatents
Jump to navigation Jump to search

SYSTEM AND METHOD FOR A GLOBAL DIGITAL ELEVATION MODEL

Organization Name

Climate Central

Inventor(s)

Scott A. Kulp of Kendall Park NJ (US)

Benjamin H. Strauss of Princeton NJ (US)

SYSTEM AND METHOD FOR A GLOBAL DIGITAL ELEVATION MODEL - A simplified explanation of the abstract

This abstract first appeared for US patent application 20230092122 titled 'SYSTEM AND METHOD FOR A GLOBAL DIGITAL ELEVATION MODEL

Simplified Explanation

The abstract describes a system and method for creating a digital elevation model (DEM) and reducing vertical bias and root mean square error (RMSE) of an elevation dataset. The system includes processors that receive input data and use a neural network (NN) to generate a DEM based on predicted elevations.

  • The system uses a neural network to analyze input data and learn nonlinear relationships between the input data and actual elevation.
  • The neural network includes an input layer, multiple hidden layers, and an output layer.
  • The hidden layers iteratively analyze the input data to predict elevations, while the output layer provides the final predicted elevation based on the analysis.

Potential Applications

This technology has potential applications in various fields, including:

  • Geographical mapping and surveying: The system can be used to generate accurate digital elevation models for mapping and surveying purposes.
  • Environmental monitoring: It can help in monitoring changes in terrain elevation over time, which is crucial for studying environmental impacts.
  • Infrastructure planning: Accurate elevation data is essential for planning infrastructure projects such as roads, bridges, and buildings.

Problems Solved

The system addresses the following problems:

  • Vertical bias: By using a neural network to analyze input data and learn nonlinear relationships, the system reduces vertical bias in the elevation dataset.
  • Root mean square error (RMSE): The system aims to minimize RMSE, which is a measure of the difference between predicted and actual elevations. This helps in improving the accuracy of the digital elevation model.

Benefits

The technology offers several benefits:

  • Improved accuracy: By reducing vertical bias and minimizing RMSE, the system generates more accurate digital elevation models.
  • Efficiency: The use of a neural network allows for efficient analysis of large datasets, enabling faster generation of elevation models.
  • Automation: The system automates the process of creating digital elevation models, reducing the need for manual intervention and saving time and resources.


Original Abstract Submitted

a system and method for creating a digital elevation model, and for reducing vertical bias and/or root mean square error (rmse) of an elevation dataset may be provided. the system may include one or more processors configured to receive input data, provide the input data to a neural network (nn), and generate a digital elevation model based on the predicted elevations output by the nn. the nn may be configured to include an input layer; a plurality of hidden layers connected to the input layer, the plurality of hidden layers configured to iteratively analyze the input data and learn nonlinear relationships between the input data and actual elevation; and an output layer connected to the plurality of hidden layers, the output layer configured to output a predicted elevation based on the analysis of the input data.

Retrieved from "http://wikipatents.org/index.php?title=20230092122._SYSTEM_AND_METHOD_FOR_A_GLOBAL_DIGITAL_ELEVATION_MODEL_simplified_abstract_(Climate_Central)&oldid=20359"