Using machine learning to detect and predict changes of land use from aerial imagery

0152f4e8 01d8 47dd a0fe 5122aedb5f46.png 500 500

Starting Date: earliest start: (2019-12-01) latest end: (2020-06-30)

Organization: Photogrammetry and Remote Sensing (Prof. Schindler)

Involved Host(s): Wegner Jan Dirk

Abstract: The ability to understand the evolution of land use (e.g. construction, change of land type) is crucial in fields such as urban planning, agriculture, natural resources management, and even autonomous flight. We will develop a Bayesian Recurrent Neural Network approach.

PLEASE LOG IN TO SEE DESCRIPTION: This project is set to limited visibility by its publisher. To see the project description you need to log in at SiROP. Please follow these instructions: - Click link "Open this project..." below. - Log in to SiROP using your university login or create an account to see the details. If your affiliation is not created automatically, please follow these instructions:

Bayesian Deep Learning Recurrent Neural Networks Remote Sensing Agriculture Environmental Sciences Computer Vision

Labels: Master Thesis CLS Student Project (MPG ETH CLS)
Topics: Information, Computing and Communication Sciences Engineering and Technology