Geospatial Analyses & Remote Sensing : from Beginner to Pro
Learn Remote Sensing, QGIS & GIS , main concepts, machine learning, QGIS classification, change detection, Earth Engine
4.61 (71 reviews)
1689
Students8.5 hours
ContentFeb 2021
Last UpdateWhat you will learn
☑ Understand and implement basic concepts of Geographic Information Systems (GIS) and Remote Sensing
☑ Fully understand the basics of Land use and Land Cover (LULC) Mapping and Change Detection in QGIS
☑ Learn the most popular open-source GIS and Remote Sensing software tools (QGIS), Semi-automated classification (SCP) plugin, OTB toolbox)
☑ Learn how to obtain satellite data, apply image preprocessing, create training and validation data in QGIS
☑ Create your first GIS maps for your reports/presentations in QGIS
☑ Understand machine learning concepts and its application in GIS and Remote Sensing
☑ Apply Machine Learning image classification mapping and change detection in SCP, OTB toolbox and QGIS
☑ Fully understand and apply advanced methods in machine learning in GIS and Remote Sensing, such as random forest classification and object-based image analysis, in QGIS and Google Earth Engine
☑ You'll have a copy of the labs, step-by-step manuals and scripts used in the course for QGIS & more
Topics
GISDescription
Geospatial Data Analyses & Remote Sensing: 5 Classes in 1
Do you need to design a GIS map or satellite-imagery based map for your Remote Sensing or GIS project but you don’t know how to do this?
Have you heard about Remote Sensing object-based image analysis and machine learning or maybe QGIS or Google Earth Engine but did not know where to start with such analyses?
Do you find Remote Sensing and GIS manuals too not practical and looking for a course that takes you by hand, teach you all the concepts, and get you started on a real-life GIS mapping project?
I'm very excited that you found my Practical Geospatial Masterclass on Geospatial Data Analyses & Remote Sensing. This course provides and information that is usually delivered in 4 separate Geospatial Data Analyses & Remote Sensing courses, and thus you with learning all the necessary information to start and advance with Geospatial analysis and includes more than 9 hours of video content, plenty of practical analysis, and downloadable materials. After taking this course, you will be able to implement PRACTICAL, real-life spatial geospatial analysis and tasks, including land use and land cover mapping and change detection, machine learning for GIS, data, and maps creation, etc. in popular and FREE software tools.
This course is designed to equip you with the theoretical and practical knowledge of applied geospatial analysis, namely Remote Sensing and some Geographic Information Systems (GIS). By the end of the course, you will feel confident and completely understand the basics of Remote Sensing and GIS, learn Machine Learning applications in GIS / Remote Sensing technology, and how to use Machine Learning algorithms for various geospatial & Remote Sensing tasks, such as land use and land cover mapping (classifications) and object-based image analysis (segmentation, crop type mapping, etc). This course will also prepare you for using geospatial and Remote Sensing analysis with open source and free software tools.
In the course, you will be able to apply in QGIS such Machine Learning algorithms like Random Forest, Support Vector Machines and Decision Trees (and others) for classification of satellite imagery. You will also learn how to download and process satellite imagery, conduct supervised and unsupervised learning, implement accuracy assessment, apply object-based image analysis, and change detection. On top of that, you will practice geospatial & Remote Sensing analysis by completing an entire classification project by exploring the power of Machine Learning, cloud computing, and Big Data analysis using Google Erath Engine for any geographic area in the world.
In this course, I will teach you how to work with the popular open-source GIS & Remote Sensing, i.e. QGIS software, and its great tools: Semi-Automated classification plugin and Orfeo (OTB) toolbox. You will also get introduced to cloud computing and Big Data analysis using Google Erath Engine for any geographic area in the world.
The course is ideal for professionals such as geographers, programmers, social scientists, geologists, GIS & Remote Sensing experts, and all other experts who need to use maps in their field and would like to learn more about Machine Learning in GIS & Remote Sensing and QGIS. If you're planning to undertake a task that requires to use a state of the art Machine Learning algorithms for creating, for instance, land cover and land use maps in QGIS and Google Earth Engine, this course will give you the confidence you need to understand and solve such geospatial problem.
One important part of the course is the practical exercises. You will be given some precise instructions on Remote Sensing analysis, downloadable practical materials, scripts, and datasets to create maps and conduct analysis based on Machine Learning algorithms using the QGIS software and Google Earth Engine.
Screenshots
Content
Introduction to the course, GIS and Remote Sensing
Introduction to the course
Applications of GIS
Applications of Remote Sensing
Software used in this course
About open-source QGIS software
Lab: QGIS installation
Semi-Automatic Classification Plugin for QGIS
Lab: QGIS interface
Lab: QGIS Toolbars
Lab: Creating account in Google Earth Engine
Basics of GIS
Definition of GIS
Main principles of GIS
Basics of Geodata and its main types
GIS software and your PC set up
Lab: Your First GIS Map in QGIS
Introduction to Remote Sensing
Definition of Remote Sensing
Introduction to digital images
Sensors and Platforms
Understanding Remote Sensing for LULC mapping
Lab: How to download satellite images with SCP plug-in
Lab: Layerstacking, True and False Colour composites
Lab: Image preprocessing - atmospheric correction
Sources of Remote Sensing images
Basics of land use and land cover (LULC) mapping and change detection in QGIS
Introduction to LULC classification based on satellite images
Supervised and unsupervised image classification
Unsupervised (K-means) image analysis in QGIS
Stages of LULC supervised classification
Lab: Training data collection
Overview of image classification algorrithms
Lab: LULC with the use of spectral angle mapping
Lab: LULC with the use of Maximum Likelihood Algorithm
Lab: LULC with the use of Minimum Distance Classification Algorithm
Accuracy assessment of LULC map
Lab: Validation data creation
Lab: Accuracy Assessment
Project: LULC mapping of Landsat 8
Image Classification in Google Earth Engine
Section Overview
Import images and their visualization in Google Earth Engine
Unsupervised (K-means) image analysis in Google Earth Engine
Random Forest Supervised CLassification in Earth Engine
Accuracy Assessment in Earth Engine
Introduction to change detection
Introduction to change detection
Lab: Change Detection in QGIS
Lab: How to make a map in QGIS
Introduction to Machine Learning in GIS
Introduction to Machine Learning
On Machine Learning in GIS and Remote Sensing
Supervised and Unsupervised Learning (classification) in GIS and Remote Sensing
Object detection in GIS
Segmentation and object-based image analysis (OBIA)
Prediction in GIS and deep learning for Big Data Analysis
Project: Machine Learning for GIS on cloud (Google Earth Engine)
Machine Learning and Object-based Analysis (OBIA) in GIS: an example of crop map
OTB installation
Object-based image classification (OBIA) VS pixel-based image classification
Introduction to object-based crop type classification in QGIS
Feature Extraction for object-based crop classification
Training of Machine learning Classifiers for object-based crop classification
Object-based crop classification with Machine Learning algorithms in QGIS
Machine Learning and Object-based Analysis (OBIA) in GIS: part 2
Section Overview
Segmentation of high-resolution satellite image
Creating training data from satellite image based on the segmented layer
Object-based image classification with the Machine Learning algorithm
Object-based crop classification with Machine Learning algorithms in QGIS
Section Overview
Introduction to object-based crop type mapping in QGIS
Feature Extraction for object-based crop classification
Training of Machine learning Classifiers for object-based crop classification
Object-based crop classification with Machine Learning algorithms in QGIS
Reviews
This course has really been an eye-opener for me in Remote Sensing. I will definitely recommend this to my friends and colleagues.
This course is a good start for someone who wants to learn the basics of remote sensing. Easy to understand. The teacher keeps the concept simple and brief.
This is a great course to gain a general overview of remote sensing. The course is clear and all concepts are well explained. The slides that accompany the course are particularly good.
The course starts strong and covers many topics very well. Further, the instructor teaches very well and in an easy to follow way. It rips through some of the advance tools, but you learn enough to use them in your applications. Occasionally, the instructor doesn't supply the correct resources, or the resources have already been transformed by the techniques being taught, so you don't get to do it with her. However, this is very minor and the instructor almost always provides more than enough examples for you to understand very well. All in all, especially if you have a decent understanding of GIS and machine learning already, this will be a good continuation to QGIS, remote sensing, google earth engine, and how to download and assess satellite imagery. The object detection was also really interesting and felt like the natural advanced progression in GIS topics! I felt confident that I could begin to apply my knowledge to professional projects, but there will still be a lot more to teach myself.
Very informative course, I have learned a lot from this course; a very detailed explanation; easily understandable; get answers to my questions in a very short time
It's very good ....and the person explaining has a thorough knowledge of the subject. thanks for this course.
the provided information on Geospatial analysis, QGIS, and remote sensing are very practical and complete
Very useful and detailed course, high-quality teaching, detailed instructions, knowledgable instructor!
This is the best course on Remote Sensing on Udemy. The video lectures are excellent prepared and the practicals make sure you understand the geospatial analysis and theory.
I loved this course. It started with basics and moves to advanced methods. All the concepts are perfectly explained and there are plenty of labs!
It's an amazing course for someone who wants to know the fundamentals of remote sensing and also learn more advanced spatial techniques.
I never knew about machine learning on QGIS and I have was so excited on seeing such. I can practical produce a map using GIS
Excellent course for beginners and bit experienced learner ... A very thorough and clear explanation of concepts with real-life examples. Appreciate your hard work.
The last video of this first section did not play perfectly. The audio stopped at around 8mins while the video continued to 9.09mins. I would have appreciated the course more if a few definitions were given and emphasized, especially GIS and Remote Sensing.
Coupons
| Status | Date | Discount | ||
|---|---|---|---|---|
| Expired | 5/27/2020 | 100% OFF | ||
| Expired | 6/1/2020 | 94% OFF | ||
| Expired | 7/10/2020 | 86% OFF | ||
| Expired | 8/2/2020 | 90% OFF | ||
3095096
Udemy ID5/6/2020
Course created date5/27/2020
Course Indexed date
