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Deep Learning Application for Earth Observation

Deep Learning Application for Earth Observation
Deep Learning Application for Earth Observation

Deep Learning Application for Earth Observation

Satellite Image processing using Deep Learning Neural Network

What you’ll learn

Deep Learning Application for Earth Observation

  • 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)

Requirements

  • Intermediate understanding of deep learning
  • Basic understanding of python programming

Description

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, 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

5 Real-Time Use Cases using Machine Learning

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