Unmanned aerial vehicles (UAVs), commonly known as drones, have revolutionized spatial data collection in recent years by offering flexible, low-cost platforms for aerial imagery and remote sensing.

The integration of UAV imagery and deep learning algorithms in remote sensing is poised to continue transforming our ability to monitor and comprehend the Earth's surface and its dynamic processes.

Michigan Technological University’s Michigan Tech Transportation Institute (MTTI) and Michigan Tech Research Institute (MTRI), in cooperation with the Center for Automotive Research and the Michigan ...

It’s sometimes useful for a system to not just have a flat 2D camera view of things, but to have an understanding of the depth of a scene. Dual RGB cameras can be used to sense depth by contrasting ...

Remote Sensing Scientists use sensors to analyze data and solve regional, national, and global problems in areas such as natural resource management, urban planning, and climate and weather prediction ...

This project focuses on the development of more reliable estimates of carbon storage in coastal wetland systems. The goal is to use very high-resolution hyperspectral remote sensing imagery and ...

Hyperspectral imaging (HSI), or imaging spectroscopy, captures detailed information across the electromagnetic spectrum by acquiring a spectrum for each pixel in an image. This enables precise ...

A team of CU Boulder researchers has introduced a quantum sensing technique that could lead to improvements in how we monitor infrastructure, detect changes in the environment and conduct geophysical ...

Analyze images and other data to solve problems across disciplines. Imagery and other data collected by satellites, crewed aircraft, and uncrewed aerial systems (UAS) are increasingly important for ...

Macroecology is the study of the relationships between organisms and their environment at large spatial scales. In recent years, remote sensing has allowed scientists to substantially expand our ...