Industrial Infrastructure, July 5, 2016

Tagged by: Machine Learning

Daily, High-resolution Leaf Area Index from Sensor Fusion with Planet, Landsat and MODIS

Paper

Classic remote sensing for agricultural relies heavily on indices like NDVI (Normalized Differential Vegetation Index), which uses information from the red and near-infrared portions of the spectrum to provide an indicator of vegetation greenness and vitality. However, other indices, often implicating other parts of the spectrum, may provided added and actionable information […]

Stanford Students Deploy Deep Learning with Planet Imagery

Features

As Planet’s Education and Research Community continues to grow, students are increasingly working with Planet imagery in college courses. At Stanford University, several students recently utilized Planet imagery in Computer Science 230: Deep Learning. Ian Avery Bick, Dennis Wang and Ben Mullet examined deforestation near Kibale National Park in Southern Uganda, an […]

Sensor Fusion of Planet, Landsat and MODIS Data for Unprecedented Land Surface Monitoring

Paper

In the midst of a revolution Earth Observation, due to increasingly diverse and temporally dense data feeds enabled by cubesats and other sensors, there is a need to be interoperable across sensors. In the journal Remote Sensing of Environment, Rasmus Houborg and Matt McCabe present the Cubesat-enabled Spatio-Temporal Enhancement Method (CESTEM), which […]

Change Detection of Mediterranean Seagrasses Using RapidEye Time Series

Paper

Seagrass beds are one of the most important ecosystems in the Mediterranean region, supporting an enormous diversity of marine fauna. However, with anthropogenic influences including dredging and modification of shorelines, pollution and other drivers, seagrass ecosystems facing increasing threats. To improve monitoring of seagrass extent, Dimosthenis Tranganos and Peter Reinartz from the […]

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