Course Description
Advanced problems in photointerpretation, photogrammetry, and remote sensing for landscape and urbanscape analysis. Topics include emerging geospatial data from unmanned aerial systems (UAS or drones), airborne LiDAR, satellite sensors, mobile devices, and social media. Methods of analysis will explore machine learning/deep learning, spatio-temporal patterns, and ethics/privacy concerns.
Athena Title
Prob Remote Sensing Environ II
Prerequisite
GEOG 4350/6350-4350L/6350L or permission of department
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Course Objectives
This seminar course focuses on advanced remote sensing techniques and integration with several fields of geographic information science (GIScience), including geographic information systems (GIS), Global Positioning Systems (GPS), spatial analysis, modeling, and photogrammetry for landscape and urbanscape analysis. A component of this course is a discussion of geospatial ethics and privacy concerns related to the geospatial techniques we explore. Students are exposed to current state-of-the-art geospatial techniques, with an aim to identifying techniques best suited to their personal research areas of interest. The intent of this course is to provide students with insights to new methods and technologies in remote sensing. The first half of the course consists of student-led lectures, readings, and discussion on individual research topics. The second half of the course focuses on student projects. Working individually or in pairs, students explore new techniques in image analysis, including Deep Learning and Machine Learning. Oral and written presentations of project results demonstrate an understanding of advanced remote sensing techniques and their applications.
Topical Outline
Topics are updated regularly to include state-of-the-art geospatial techniques in remote sensing and may include: Advanced methods of image classification and feature extraction for terrestrial and aquatic systems Modeling biophysical processes with imagery Challenges of remotely sensed imagery as Big Data Integration of remotely sensed images, LiDAR, citizen science crowdsourced data and geolocated social media data Remotely sensed data as input to Location-based Services and the Internet of Things Remote sensing data of high spatial, temporal, and spectral resolution Spectral reflectance analysis of landscapes, water bodies, and urbanscapes 3D terrain representation and tangible landscapes for geospatial decision support Geospatial ethics and privacy issues
Syllabus
Public CV