A Season Independent U-net Model for Robust Mapping of Solar Arrays Using Sentinel-2 Imagery
We have an unprecedented ability to map the Earth's surface as deep learning technologies are applied to an abundance of high-frequency Earth observation data. Simple, free, and effective methods are needed to enable a variety of stakeholders to …
Chesapeake Solar Fields AI Output
The growth of renewable energy is critical to slowing climate change, and the number of solar energy facilities has been expanding within the U.S. With such a rapid increase, there is a need to quickly update maps of these facilities - information …
Unpacking the eagle collision risk model: practical guidance for wind energy
Climate change is one of the greatest threats facing biodiversity, and solutions to reduce carbon emissions are needed to conserve species. Renewable energies are a prominent means to achieve this goal, but the potential for direct harm to wildlife …
Effective, low-cost methods of applying computer vision to public Earth observation data
We have an unprecedented ability to analyze and map the Earth's surface, as deep learning technologies are applied to an abundance of Earth observation systems collecting images of the planet daily. In order to realize the potential of these data …
Computer Vision for Renewable Energy
Defenders of Wildlife supports the development of renewable energy sources and works to ensure that this development is done in a manner that does not negatively impact wildlife. We used artificial intelligence technology and satellite data to help …