Chesapeake Conservancy's data science team developed an artificial intelligence deep learning model for mapping wetlands, which resulted in 94% accuracy. This method for wetland mapping could deliver important outcomes for protecting and conserving wetlands. "We're happy to support this exciting project as it explores new methods for wetlands delineation using satellite imagery," said EPRI Principal Technical Leader Dr. Nalini Rao. "It has the potential to save natural resource managers time in the field by using a GIS tool right from their desks. Plus, it can help companies and the public manage impacts to wetlands as infrastructure builds are planned to meet decarbonization targets."

Annapolis, MD – 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 (1m) wetland mapping with freely available data from three areas: Mille Lacs County, Minnesota; Kent County, Delaware; and St. Lawrence County, New York. The full model, which requires local training data provided by state wetlands data and the National Wetlands Inventory (NWI), mapped wetlands with 94% accuracy.

SEATTLE (Thomson Reuters Foundation) - In December 2016, environmental group Chesapeake Conservancy unveiled one of the largest, high-resolution land-cover maps made in the United States. The bay, North America's biggest estuary, has struggled to recover from overfishing and pollution, and the conservancy hopes the map will guide environmental restoration decisions like where to plant stormwater-absorbing trees. Creating a 100,000-square-mile (259,000 square kilometres) digital map that defined land use - water, vegetation or concrete - at such a fine scale was "gruelling", said project director Jeff Allenby. "(It was) day after day of having staff process and correct the tiles," he said. First a computer analysed almost 80,000 tiles - each of which corresponds to about 13 square miles and digitally records the landscape.

On May 27, a deluge dumped more than 6 inches of rain in less than three hours on Ellicott City, Maryland, killing one person and transforming Main Street into what looked like Class V river rapids, with cars tossed about like rubber ducks. The National Weather Service put the probability of such a storm at once in 1,000 years. Yet, "it's the second time it's happened in the last three years," says Jeff Allenby, director of conservation technology for Chesapeake Conservancy, an environmental group. Floods are nothing new in Ellicott City, located where two tributaries join the Patapsco River. But Allenby says the floods are getting worse, as development covers what used to be the "natural sponge of a forest" with paved surfaces, rooftops, and lawns.

On May 27, a deluge dumped more than 6 inches of rain in less than three hours on Ellicott City, Maryland, killing one person and transforming Main Street into what looked like Class V river rapids, with cars tossed about like rubber ducks. The National Weather Service put the probability of such a storm at once in 1,000 years. Yet, "it's the second time it's happened in the last three years," says Jeff Allenby, director of conservation technology for Chesapeake Conservancy, an environmental group. Floods are nothing new in Ellicott City, located where two tributaries join the Patapsco River. But Allenby says the floods are getting worse, as development covers what used to be the "natural sponge of a forest" with paved surfaces, rooftops, and lawns.

A toolkit used across Microsoft to achieve breakthroughs in artificial intelligence is generally available to the public via an open-source license, a team of researchers and software engineers announced today. "The 2.0 version of the toolkit is now in full release," said Chris Basoglu, a partner engineering manager at Microsoft. He has played a key role in developing Microsoft Cognitive Toolkit (previously known as CNTK). The full release of Microsoft Cognitive Toolkit 2.0 for use in production-grade and enterprise-grade deep learning workloads includes hundreds of new features incorporated since the beta to streamline the process of deep learning and to ensure the toolkit's seamless integration throughout the wider AI ecosystem. New with the full release today is support for Keras, a user-friendly open-source neural network library that is popular with developers working on deep learning applications.