Remote Sensing Image Acquisition, Analysis and Applications (Coursera)

Remote Sensing Image Acquisition, Analysis and Applications (Coursera)

Welcome to Remote Sensing Image Acquisition, Analysis and Applications, in which we explore the nature of imaging the earth's surface from space or from airborne vehicles. This course covers the fundamental nature of remote sensing and the platforms and sensor types used. It also provides an in-depth treatment of the computational algorithms employed in image understanding, ranging from the earliest historically important techniques to more recent approaches based on deep learning.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

The course material is extensively illustrated by examples and commentary on the how the technology is applied in practice. It will prepare participants to use the material in their own disciplines and to undertake more detailed study in remote sensing and related topics.

Syllabus

WEEK 1
Course Welcome, Instructor, Course Resources, Module 1 Introduction and Week 1 Lectures and Quiz
Remote sensing is the science and technology of acquiring images of the earth’s surface from spacecraft, aircraft and drones to aid in the monitoring and management of the natural and built environments. Extensive computer-based analysis techniques are used to extract information from the recorded images in support of applications ranging over many earth and information science disciplines. This course covers the fundamental nature of remote sensing and the platforms and sensor types used. It also provides an in-depth treatment of the computational algorithms employed in image understanding, ranging from the earliest historically important techniques to more recent approaches based on deep learning. The course material is extensively illustrated by examples and commentary on the how the technology is applied in practice. While broad in its coverage the 15 hours of instruction, supported by quizzes and tests, will prepare participants to use the material in their own disciplines and to undertake more detailed study in remote sensing and related topics.
WEEK 2: Lectures and Quiz
WEEK 3: Lectures and Quiz
WEEK 4: Lectures and Quiz
WEEK 5: Lectures and Quiz, Module 1 Test
WEEK 6: Module 2 Introduction, lectures and Quiz
WEEK 7: Lectures and Quiz
WEEK 8: Lectures and Quiz
WEEK 9: Lectures and Quiz
WEEK 10: Lectures and Quiz, Module 2 Test
WEEK 11: Module 3 Lectures and Quiz
WEEK 12: Lectures and Quiz
WEEK 13: Lectures and Quiz
WEEK 14: Lectures and Quiz
WEEK 15: Lectures and Quiz, Module 3 Test, Course Conclusion

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Introduction to Vertex AI (Coursera) Coursera
Fractal Analytics

Introduction to Vertex AI (Coursera)

Welcome to "Introduction to Vertex AI"! In this concise yet impactful microlearning course spanning around 4 hours, we're diving into the world of Vertex AI to equip you with fundamental insights and practical skills. We'll unravel the essentials of Vertex AI, guiding you through the interface to empower you to navigate this powerful platform seamlessly. Get ready to grasp strategic insights that will enable you to effectively harness the capabilities of Vertex AI in your projects.

Nov 23rd 2026
2 Weeks
Network Analysis in Systems Biology (Coursera) Coursera
Icahn School of Medicine at Mount Sinai

Network Analysis in Systems Biology (Coursera)

An introduction to data integration and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research. The course covers methods to process raw data from genome-wide mRNA expression studies (microarrays and RNA-seq) including data normalization, differential expression, clustering, enrichment analysis and network construction. The course contains practical tutorials for using tools and setting up pipelines, but it also covers the mathematics behind the methods applied within the tools.

Nov 23rd 2026
5-12 Weeks
Google Cloud Product Fundamentals en Español (Coursera) Coursera
Google Cloud

Google Cloud Product Fundamentals en Español (Coursera)

Este curso, que es una continuación de Business Transformation with Google Cloud, le permitirá conocer la perspectiva tecnológica de la transformación de una organización. Para ser más específicos, explicaremos cómo la tecnología de Google Cloud puede transformar digitalmente una organización en los siguientes aspectos: modernizar la infraestructura de TI; mejorar la forma en que los equipos desarrollan las aplicaciones que utiliza la empresa; saber cómo aprovechar el aprendizaje automático y la inteligencia artificial para generar más valor; advertir el rol fundamental de las herramientas de productividad basadas en la nube, como G Suite, para cumplir con el trabajo, y comprender los desafíos y las oportunidades de la administración de costos que trae aparejados una infraestructura de TI cambiante basada en la nube.

Nov 23rd 2026
5-12 Weeks
Blockchain Scalability and its Foundations in Distributed Systems (Coursera) Coursera
The University of Sydney

Blockchain Scalability and its Foundations in Distributed Systems (Coursera)

Blockchain promises to disrupt industries once it will be efficient at large scale. In this course, you will learn how to make blockchain scale. You will learn about the foundational problem of distributed computing, consensus, that is key to create blocks securely. By illustrating limitations of mainstream blockchains, this course will indicate how to improve the technology in terms of security and efficiency. In particular, this course will help you: understand security vulnerabilities of mainstream blockchains; design consensus algorithms that tolerate attacks, and; design scalable blockchain systems.

Nov 23rd 2026
5-12 Weeks
VLSI CAD Part I: Logic (Coursera) Coursera
University of Illinois at Urbana-Champaign

VLSI CAD Part I: Logic (Coursera)

A modern VLSI chip has a zillion parts -- logic, control, memory, interconnect, etc. How do we design these complex chips? Answer: CAD software tools. Learn how to build thesA modern VLSI chip is a remarkably complex beast: billions of transistors, millions of logic gates deployed for computation and control, big blocks of memory, embedded blocks of pre-designed functions designed by third parties (called “intellectual property” or IP blocks). How do people manage to design these complicated chips? Answer: a sequence of computer aided design (CAD) tools takes an abstract description of the chip, and refines it step-wise to a final design.

Nov 16th 2026
5-12 Weeks
Natural Attenuation of Groundwater Contaminants: New Paradigms, Technologies, and Applications (Coursera) Coursera
Rice University

Natural Attenuation of Groundwater Contaminants: New Paradigms, Technologies, and Applications (Coursera)

Cleaning up the large number of groundwater contamination sites is a significant and complex environmental challenge. The environmental industry is continuously looking for remediation methods that are both effective and cost-efficient. Over the past 10 years there have been amazing, important developments in our understanding of key attenuation processes and technologies for evaluating natural attenuation processes, and a changing institutional perspective on when and where Monitored Natural Attenuation (MNA) may be applied.

Nov 23rd 2026
5-12 Weeks
Machine Learning in Healthcare: Fundamentals & Applications (Coursera) Coursera
Northeastern University

Machine Learning in Healthcare: Fundamentals & Applications (Coursera)

Examines data mining perspectives and methods in a healthcare context. Introduces the theoretical foundations for major data mining methods and studies how to select and use the appropriate data mining method and the major advantages for each. Students are exposed to contemporary data mining software applications and basic programming skills. Focuses on solving real-world problems, which require data cleaning, data transformation, and data modeling.

Nov 23rd 2026
4 Weeks
Ethical Issues in Data Science (Coursera) Coursera
University of Colorado Boulder

Ethical Issues in Data Science (Coursera)

Computing applications involving large amounts of data – the domain of data science – impact the lives of most people in the U.S. and the world. These impacts include recommendations made to us by internet-based systems, information that is available about us online, techniques that are used for security and surveillance, data that is used in health care, and many more. In many cases, they are affected by techniques in artificial intelligence and machine learning.

Nov 23rd 2026
5-12 Weeks
Approximation Algorithms Part II (Coursera) Coursera
École normale supérieure

Approximation Algorithms Part II (Coursera)

This is the continuation of Approximation algorithms, Part 1. Here you will learn linear programming duality applied to the design of some approximation algorithms, and semidefinite programming applied to Maxcut. By taking the two parts of this course, you will be exposed to a range of problems at the foundations of theoretical computer science, and to powerful design and analysis techniques.

Nov 23rd 2026
4 Weeks
Decision Making and Reinforcement Learning (Coursera) Coursera
Columbia University

Decision Making and Reinforcement Learning (Coursera)

This course is an introduction to sequential decision making and reinforcement learning. We start with a discussion of utility theory to learn how preferences can be represented and modeled for decision making. We first model simple decision problems as multi-armed bandit problems in and discuss several approaches to evaluate feedback. We will then model decision problems as finite Markov decision processes (MDPs), and discuss their solutions via dynamic programming algorithms. We touch on the notion of partial observability in real problems, modeled by POMDPs and then solved by online planning methods.

Nov 23rd 2026
5-12 Weeks
Approximation Algorithms Part I (Coursera) Coursera
École normale supérieure

Approximation Algorithms Part I (Coursera)

How efficiently can you pack objects into a minimum number of boxes? How well can you cluster nodes so as to cheaply separate a network into components around a few centers? These are examples of NP-hard combinatorial optimization problems. It is most likely impossible to solve such problems efficiently, so our aim is to give an approximate solution that can be computed in polynomial time and that at the same time has provable guarantees on its cost relative to the optimum.

Nov 23rd 2026
5-12 Weeks
Analysis of Algorithms (Coursera) Coursera
Princeton University

Analysis of Algorithms (Coursera)

This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. In addition, this course covers generating functions and real asymptotics and then introduces the symbolic method in the context of applications in the analysis of algorithms and basic structures such as permutations, trees, strings, words, and mappings.

Nov 23rd 2026
5-12 Weeks