Gain an overview of advanced remote sensing concepts and applications
Examine linear models in remote sensing as a foundation for promoting understanding of more complicated machine learning models
Understand the fundamentals of machine learning in remote sensing as implemented in GEE (Random Forest models for image classification)
Explore advanced machine learning models not available in GEE (PointNet models for point cloud classification)
About this course:
The main objective of this course is to introduce advanced remote sensing topics in the realm of cloud computing. To promote collaboration in research and interoperability of different programming languages and data sources, we will be using the Google Earth Engine Python API running on Google Colab (GEE JavaScript API will only be used for reference purposes). This advanced course in remote sensing has six units: 0) Introduction; 1) Advanced GEE operations; 2) Modeling in GEE; 3) Machine learning in GEE (raster); 4) Machine learning in KERAS (point clouds); and 5) Open topics. All units will also include lab sessions with code examples to facilitate the transition from working in various platforms using different programming languages to working in one unified cloud-based coding environment. The ultimate goal is for you to be comfortable integrating different remote sensing and geospatial analysis workflows and datasets from different sources into one unified cloud-based coding environment to enhance efficiency and to promote collaboration.
Required course in the Geospatial Imagery Analysis specialization.
This is an online course, wherein all course content is delivered online and all interaction among the instructor and the participants will take place online; additional requirements include microphone, headphones/speakers, and webcam.
Technical requirements: Students are responsible for providing a personal computer with a minimum of 4GB of RAM that is capable of running Windows 10. Apple hardware running macOS can be used provided that Windows 10 is installed either using Boot Camp or virtualization (VirtualBox, Parallels, etc.) with at least 4GB of RAM allocated to Windows. Hardware specifications in excess of these minimum requirements will offer better performance and a better student experience. Students will be provided with a student license for ArcGIS as long as they are enrolled in program courses for which ArcGIS is required.
Enrollment limited to 50 students; early enrollment recommended. Visitors not permitted. Internet access required. Materials required.
Refund Deadline
No refunds after July 05, 2024
Course Requirements
Internet access required to retrieve course materials
Contact Us
Our team members are here to help. Hours: Mon-Fri, 8am-5pm.
We use cookies to understand how you use our site and to improve your experience, including personalizing content and to store your content preferences. By continuing to use our site, you accept our use of cookies.
Read our privacy policy.
