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Deep Learning for Remote Sensing Images with Open Source Software
In today’s world, deep learning source codes and a plethora of open access geospatial images are readily available and easily accessible. However, most people are missing the educational tools to make use of this resource. Deep Learning for Remote Sensing Images with Open Source Software is the first practical book to introduce deep learning techniques using free open source tools for processing real world remote sensing images. The approaches detailed in this book are generic and can be adapted to suit many different applications for remote sensing image processing, including landcover mapping, forestry, urban studies, disaster mapping, image restoration, etc. Written with practitioners and students in mind, this book helps link together the theory and practical use of existing tools and data to apply deep learning techniques on remote sensing images and data.
Specific Features of this
The first book that explains how to apply deep learning techniques to public, free available data (Spot-7 and Sentinel-2 images, OpenStreetMap vector data), using open source software (QGIS, Orfeo ToolBox, TensorFlow)
Presents approaches suited for real world images and data targeting large scale processing and GIS applications
Introduces state of the art deep learning architecture families that can be applied to remote sensing world, mainly for landcover mapping, but also for generic approaches (e.g. image restoration)
Suited for deep learning beginners and readers with some GIS knowledge. No coding knowledge is required to learn practical skills.
Includes deep learning techniques through many step by step remote sensing data processing exercises.
Specific Features of this
The first book that explains how to apply deep learning techniques to public, free available data (Spot-7 and Sentinel-2 images, OpenStreetMap vector data), using open source software (QGIS, Orfeo ToolBox, TensorFlow)
Presents approaches suited for real world images and data targeting large scale processing and GIS applications
Introduces state of the art deep learning architecture families that can be applied to remote sensing world, mainly for landcover mapping, but also for generic approaches (e.g. image restoration)
Suited for deep learning beginners and readers with some GIS knowledge. No coding knowledge is required to learn practical skills.
Includes deep learning techniques through many step by step remote sensing data processing exercises.
164 pages, Kindle Edition
Published July 15, 2020
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February 28, 2021
This book is aimed at researchers/professionals/students that are already familiar with the basics of machine learning (mostly with deep learning and random forests) and GIS, and therefore goes straight to the point, which is explaining how to use OTBTF. This software is an extension of the well known Orfeo Toolbox that allows its integration with TensorFlow, providing a small number of extremely useful functions that solve the issues that usually occur to a GIS expert when trying to use TF for geospatial analysis, namely:
• A function to extract patches from a list of input images
• A function to train a TF model on a list of input images
• A function to serve a TF model on a list of input images
OTBTF is served via Docker images, which are still being updated while remaining compatible with the code shown in the book.
• A function to extract patches from a list of input images
• A function to train a TF model on a list of input images
• A function to serve a TF model on a list of input images
OTBTF is served via Docker images, which are still being updated while remaining compatible with the code shown in the book.
Displaying 1 of 1 review