Sensors and Systems
Breaking News
Now Available: Trimble Business Center Version 5.90
Rating12345Released on May 30, 2023, Trimble® Business Center (TBC) version...
Atly Launches with $18 Million in Funding to Introduce the Next Social Paradigm for Mapping and Discovering Places to Go
Rating12345Atly combines the power of social media-like knowledge sharing...
Woolpert Contracted by NOAA for Hydrographic Survey, Bathymetric Data in Nome, Alaska
Rating12345The $7M contract supports everything from commercial fishing and...
  • Rating12345

The model correctly omitted wetlands where they had been lost to development, despite these wetlands remaining in the outdated training data, as shown in this image (outdated training data shown in green; model prediction in purple, overlaid over recent satellite imagery). (Credit: Chesapeake Conservancy’s Conservation Innovation Center)

Chesapeake Conservancy’s data science team developed an artificial intelligence deep learning model for mapping wetlands, which resulted in 94% accuracy. Supported by EPRI, an independent, non-profit energy research and development institute; Lincoln Electric System; and the Grayce B. Kerr Fund, Inc., this method for wetland mapping could deliver important outcomes for protecting and conserving wetlands. The results are published in the peer-reviewed journal Science of the Total Environment.

The team trained a machine learning (convolutional neural network) model for high-resolution (1 meter) wetland mapping with freely available data from three areas: Mille Lacs County, Minn.; Kent County, Del.; and St. Lawrence County, N.Y. The full model, which requires local training data provided by state wetlands data and the National Wetlands Inventory (NWI), mapped wetlands with 94% accuracy.

Leave a Reply

Your email address will not be published. Required fields are marked *