What's new
Warez.Ge

This is a sample guest message. Register a free account today to become a member! Once signed in, you'll be able to participate on this site by adding your own topics and posts, as well as connect with other members through your own private inbox!

Deep Learning Application for Earth Observation | Udemy

tut4it

Active member
j6LHyl3.png
Deep Learning Application for Earth Observation | Udemy
English | Size: 2.37 GB
Genre: eLearning​

Satellite Image processing using Deep Learning Neural Network

What you'll learn
Practical example use case of deep learning for satellite imagery
Satellite imagery analysis
Object detection
Image classification
Image segmentation
Keras, Tensorflow
ArcGIS Pro (Optional)
QGIS (Optional)

Deep Learning is a subset of Machine Learning that uses mathematical functions to map the input to the output. These functions can extract non-redundant information or patterns from the data, which enables them to form a relationship between the input and the output. This is known as learning, and the process of learning is called training.

With the rapid development of computing, the interest, power, and advantages of automatic computer-aided processing techniques in science and engineering have become clear-in particular, automatic computer vision (CV) techniques together with deep learning (DL, a.k.a. computational intelligence) systems, in order to reach both a very high degree of automation and high accuracy.

This course is addressing the use of AI algorithms in EO applications. Participants will become familiar with AI concepts, deep learning, and convolution neural network (CNN). Furthermore, CNN applications in object detection, semantic segmentation, and classification will be shown. The course has six different sections, in each section, the participants will learn about the recent trend of deep learning in the earth observation application. The following technology will be used in this course,

Tensorflow (Keras will be used to train the model)

Google Colab (Alternative to Jupiter notebook)

GeoTile package (to create the training dataset for DL)

ArcGIS Pro (Alternative way to create the training dataset)

QGIS (Simply to visualize the outputs)

Who this course is for:
Deep learning beginners
Geospatial data science student
Beginners python learner who is curious about data science and imagery analysis

yMNlxlr.png

Rapidgator
Code:
https://rapidgator.net/file/1ded445e1d099ac8daa1a716ac551e0d/UD-DeepLearningApplicationforEarthObservation.part1.rar.html
https://rapidgator.net/file/53406de802c9461adf8035c16cabc1ec/UD-DeepLearningApplicationforEarthObservation.part2.rar.html
https://rapidgator.net/file/b3e87b2504ad8731552185a91198fa2a/UD-DeepLearningApplicationforEarthObservation.part3.rar.html
Ddownload
Code:
https://ddownload.com/tsu6pcj723tn/UD-DeepLearningApplicationforEarthObservation.part1.rar
https://ddownload.com/8t1q1kvsnmkl/UD-DeepLearningApplicationforEarthObservation.part2.rar
https://ddownload.com/igf494laan33/UD-DeepLearningApplicationforEarthObservation.part3.rar
If any links die or problem unrar, send request to
Code:
https://forms.gle/e557HbjJ5vatekDV9
 

Users who are viewing this thread

Back
Top