Synthetic Aperture Radar (SAR) has the capability to observe the Earth's surface both day and night and through most weather conditions, making it an ideal sensor to support a wide range of science and applications.
NASA’s Applied Remote Sensing Training Program (ARSET) has opened a new open, online webinar series: Large Scale Applications of Machine Learning using Remote Sensing for Building Agriculture Solutions. Remote sensing data is becoming crucial to solve some of the most important environmental problems, especially pertaining to agricultural applications and food security. Participants will become familiar with data format and quality considerations, tools, and techniques to process remote sensing imagery at large scale from publicly available satellite sources, using cloud tools such as AWS S3, Databricks, and Parquet. Additionally, participants will learn how to analyze and train machine learning models for classification using this large source of data to solve environmental problems with a focus on agriculture.
NASA’s Applied Remote Sensing Training Program (ARSET) has opened a new open, online webinar series: Assessing the Impacts of Fires on Watershed Health. This advanced-level, three-part training will focus on using remote sensing observations for monitoring post-fire impacts on watershed health. Specifically, this training will highlight uses of NASA Earth observations (EO) for pre-fire land cover mapping, watershed delineation and stream mapping, post-fire burn severity mapping, and pre- and post-fire riverine and freshwater water quality.
/*-->*/ Land cover mapping is a semantic segmentation problem: each pixel in an aerial or satellite image must be classified into one of several land cover classes. These classes describe the surface of the earth and are typically broad categories such as “forest” or “field”. High-resolution land cover data (≤1m / pixel) is essential in many sustainability-related applications. Its uses include informing agricultural best management practices, monitoring forest change over time and measuring urban sprawl. However, land cover maps quickly fall out of date and must be updated as construction, erosion, and other processes act on the landscape. Source: Large Scale High-Resolution Land Cover Mapping with Multi-Resolution Data Caleb Robinson, Le Hou, Kolya Malkin, Rachel Soobitsky, Jacob Czawlytko, Bistra Dilkina, Nebojsa Jojic The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019
