This document provides an overview of a workshop on land cover mapping using high resolution satellite images, OpenStreetMap data, and open source software tools. The workshop will involve preprocessing SPOT satellite imagery and OSM data, performing supervised image classification, and comparing the classification results to OSM features to identify areas for updating OSM. Key steps include extracting relevant OSM features to use as training data, preprocessing images, computing indices like NDVI, training and applying a classification algorithm, and assessing accuracy by comparing to OSM polygons. The goal is to demonstrate an approach for leveraging OSM as reference data for land cover mapping with satellite imagery.
This document discusses a pragmatic approach to remote sensing image processing using the Orfeo Toolbox (OTB) library and applications. It provides an overview of OTB's capabilities for pre-processing, feature extraction, classification, and change detection on remote sensing imagery. OTB aims to make algorithm development and validation easier through a C++ library that contains many algorithms and interfaces and is open-source and multi-platform. It incorporates functionality from other open-source libraries through a common interface. The document describes OTB's modular and scalable architecture, as well as efforts to make it easier for users through graphical applications and language bindings.
The document discusses the Orfeo ToolBox (OTB), an open source library for remote sensing image processing. Some key points:
- OTB was created in 2006 by CNES to process images from the Pleiades satellite. It is written in C++ and builds on libraries like ITK, GDAL, OpenCV, and others.
- OTB provides algorithms and applications for tasks like feature extraction, classification, segmentation, and more. It aims to support large datasets and parallel/streaming processing.
- The library has grown significantly over time from around 100 lines of code initially to hundreds of thousands now. It supports multiple platforms and sees thousands of downloads each month.
ZOO-Project is an open source platform that implements the OGC WPS standard. It allows existing geospatial algorithms to be reused as WPS services with no or minor code modifications. The presentation outlined ZOO-Project's optional support for Orfeo Toolbox, which allows OTB applications to be used as WPS services. Examples were provided of running OTB smoothing and bandmath applications as WPS processes online. Future work involves using OTB WPS services from clients and deploying them in SDIs to provide web-based image processing capabilities.
Ice: lightweight, efficient rendering for remote sensing imagesotb
Ice is a lightweight library for efficient rendering of remote sensing images. It implements a scene/actors paradigm to display multiple raster or vector files together in an responsive and on-the-fly manner. Ice uses OpenGL for rendering and achieves efficiency through multi-resolution tile caching in RAM and GPU memory. Key rendering operations are performed on the GPU through GLSL shaders, making contrast adjustments incredibly fast. Ice provides a demo application and is designed to work with different OpenGL contexts and graphical toolkits.
ORFEO ToolBox at CS-SI From research to operational applicationsotb
1. CS-SI uses Orfeo ToolBox (OTB) as a development framework for image processing research and operational applications. OTB supports research projects for space agencies and customers in areas like pansharpening, automatic image analysis, and hyperspectral data analysis.
2. OTB is integrated into several operational Sentinel-2 ground segment processing systems developed by CS-SI, including the Level-0 and Level-1 Instrument Processing Facility and the Mission Performance Assessment system.
3. CS-SI also develops end-user applications for agriculture using OTB, including a composite product, LAI retrieval, and crop mask and type classification using Sentinel-2 and Landsat-8 data.
Monteverdi 2.0 - Remote sensing software for Pleiades images analysisotb
Monteverdi 2.0 is a remote sensing software for analysis of Pleiades satellite images that has been improved over time. It began as small demonstration tools but has evolved into a full platform. The latest version, Monteverdi 2.0, has been completely reworked using QT for a modern interface and focuses on processing images through command line applications. Further updates are planned to add more advanced visualization, database management, and processing capabilities.
The document presented the Orfeo Toolbox (OTB), an open source library for image processing. It contains over 65 functions for tasks like orthorectification, filtering, segmentation, classification. It also includes applications like a image viewer, road extraction tool, and supervised classification application. The toolbox uses C++ and has Python, Java and IDL bindings. Future plans include object counting, road/hydrography extraction tools, and continued integration with Monteverdi, the interactive processing interface.
USING ORFEO TOOLBOX A GROWING COMPETENCE IN A COLLABORATIVE ENVIRONMENTotb
various uses : training set for MEDDE and CEREMA users, integration in a processing chain (OTB, ogr & gdal application), thematic (land cover for city planning, coastline monitoring, hasards flood), Dominique HEBRARD
This document discusses a pragmatic approach to remote sensing image processing using the Orfeo Toolbox (OTB) library and applications. It provides an overview of OTB's capabilities for pre-processing, feature extraction, classification, and change detection on remote sensing imagery. OTB aims to make algorithm development and validation easier through a C++ library that contains many algorithms and interfaces and is open-source and multi-platform. It incorporates functionality from other open-source libraries through a common interface. The document describes OTB's modular and scalable architecture, as well as efforts to make it easier for users through graphical applications and language bindings.
The document discusses the Orfeo ToolBox (OTB), an open source library for remote sensing image processing. Some key points:
- OTB was created in 2006 by CNES to process images from the Pleiades satellite. It is written in C++ and builds on libraries like ITK, GDAL, OpenCV, and others.
- OTB provides algorithms and applications for tasks like feature extraction, classification, segmentation, and more. It aims to support large datasets and parallel/streaming processing.
- The library has grown significantly over time from around 100 lines of code initially to hundreds of thousands now. It supports multiple platforms and sees thousands of downloads each month.
ZOO-Project is an open source platform that implements the OGC WPS standard. It allows existing geospatial algorithms to be reused as WPS services with no or minor code modifications. The presentation outlined ZOO-Project's optional support for Orfeo Toolbox, which allows OTB applications to be used as WPS services. Examples were provided of running OTB smoothing and bandmath applications as WPS processes online. Future work involves using OTB WPS services from clients and deploying them in SDIs to provide web-based image processing capabilities.
Ice: lightweight, efficient rendering for remote sensing imagesotb
Ice is a lightweight library for efficient rendering of remote sensing images. It implements a scene/actors paradigm to display multiple raster or vector files together in an responsive and on-the-fly manner. Ice uses OpenGL for rendering and achieves efficiency through multi-resolution tile caching in RAM and GPU memory. Key rendering operations are performed on the GPU through GLSL shaders, making contrast adjustments incredibly fast. Ice provides a demo application and is designed to work with different OpenGL contexts and graphical toolkits.
ORFEO ToolBox at CS-SI From research to operational applicationsotb
1. CS-SI uses Orfeo ToolBox (OTB) as a development framework for image processing research and operational applications. OTB supports research projects for space agencies and customers in areas like pansharpening, automatic image analysis, and hyperspectral data analysis.
2. OTB is integrated into several operational Sentinel-2 ground segment processing systems developed by CS-SI, including the Level-0 and Level-1 Instrument Processing Facility and the Mission Performance Assessment system.
3. CS-SI also develops end-user applications for agriculture using OTB, including a composite product, LAI retrieval, and crop mask and type classification using Sentinel-2 and Landsat-8 data.
Monteverdi 2.0 - Remote sensing software for Pleiades images analysisotb
Monteverdi 2.0 is a remote sensing software for analysis of Pleiades satellite images that has been improved over time. It began as small demonstration tools but has evolved into a full platform. The latest version, Monteverdi 2.0, has been completely reworked using QT for a modern interface and focuses on processing images through command line applications. Further updates are planned to add more advanced visualization, database management, and processing capabilities.
The document presented the Orfeo Toolbox (OTB), an open source library for image processing. It contains over 65 functions for tasks like orthorectification, filtering, segmentation, classification. It also includes applications like a image viewer, road extraction tool, and supervised classification application. The toolbox uses C++ and has Python, Java and IDL bindings. Future plans include object counting, road/hydrography extraction tools, and continued integration with Monteverdi, the interactive processing interface.
USING ORFEO TOOLBOX A GROWING COMPETENCE IN A COLLABORATIVE ENVIRONMENTotb
various uses : training set for MEDDE and CEREMA users, integration in a processing chain (OTB, ogr & gdal application), thematic (land cover for city planning, coastline monitoring, hasards flood), Dominique HEBRARD
Usages of OTB at SERTIT OTB Users meeting and hackfest 2015otb
SERTIT uses OTB applications on both Linux and Windows platforms for remote sensing tasks. OTB is handled through Ubuntu, Scientific Linux, and Windows 7 operating systems. Examples of OTB usage at SERTIT include analyzing vegetation along a railway using Pleiades imagery in a collaboration with SNCF, and understanding changes at China's Poyang Lake using a temporal series of 11 Pleiades images to extract water, calculate submersion time, and classify land use into 16 classes.
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of ...otb
This document discusses using the open-source software OTB Applications and Monteverdi to teach remote sensing techniques to students. It provides an overview of the curriculum, which includes radiometric analysis, NDVI calculation, supervised classification, and case studies. Sample code demonstrates how to perform change detection, classify images from different dates, and construct and analyze a time series of images. The software configuration and tools in Monteverdi and OTB Applications for visualization, processing, and classification are also outlined.
Power Point Presentation on object detection using tensorflow :
TensorFlow™ is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.
Monteverdi - Remote sensing software from educational to operational context otb
The Monteverdi and Orfeo Toolbox software were developed to:
1) Provide an integrated and simple interface for processing remote sensing imagery despite the complexity of underlying processes and tools.
2) Allow both experts and non-experts to efficiently extract information from large volumes of remote sensing data.
3) Promote open-source standards and reuse of existing libraries to achieve processing capabilities without reinventing methods.
Tom and Spike classifier using TensorFlow Object Detection. Presentation slides of the meetup TFOD conducted on 17/11/2018 at Algoscale Technologies Inc.
The document summarizes a research paper that compares the performance of MLP-based models to Transformer-based models on various natural language processing and computer vision tasks. The key points are:
1. Gated MLP (gMLP) architectures can achieve performance comparable to Transformers on most tasks, demonstrating that attention mechanisms may not be strictly necessary.
2. However, attention still provides benefits for some NLP tasks, as models combining gMLP and attention outperformed pure gMLP models on certain benchmarks.
3. For computer vision, gMLP achieved results close to Vision Transformers and CNNs on image classification, indicating gMLP can match their data efficiency.
This document summarizes a research paper on scaling laws for neural language models. Some key findings of the paper include:
- Language model performance depends strongly on model scale and weakly on model shape. With enough compute and data, performance scales as a power law of parameters, compute, and data.
- Overfitting is universal, with penalties depending on the ratio of parameters to data.
- Large models have higher sample efficiency and can reach the same performance levels with less optimization steps and data points.
- The paper motivated subsequent work by OpenAI on applying scaling laws to other domains like computer vision and developing increasingly large language models like GPT-3.
This document provides an overview of the Monteverdi framework for building image processing pipelines using components from the Orfeo Toolbox. It describes Monteverdi's main menu options for file handling, visualization, filtering, learning algorithms, SAR processing, and geometry functions. Special features like caching results and modifying modules in the pipeline are also outlined. The presentation concludes by discussing future evolutions planned for Monteverdi.
This document discusses change detection techniques using the Orfeo Toolbox. It presents several use cases for identifying changes between images using modules for change detection, SVM classification, and band math. The main challenges are distinguishing exceptional changes from natural changes over time in vegetation. Menus and steps are provided to open and preprocess images, classify changes versus unchanged areas, learn from sample inputs, and refine results.
The presentation introduces the Orfeo Toolbox (OTB), an open source library for image processing. It provides functions for tasks like orthorectification, fusion, filtering, segmentation, classification. It also presents applications and Monteverdi, a framework to build processing pipelines interactively. The documentation includes user guides, Doxygen documentation, code examples and a cookbook.
- The document discusses the Orfeo ToolBox (OTB) users meeting and hackfest in 2015, specifically regarding third party dependencies and the SuperBuild system.
- It outlines how OTB has reduced the number of third party dependencies and now uses a SuperBuild system to download, compile, and install dependencies at build time rather than including their source code directly.
- The SuperBuild system allows OTB to be built on any platform with just a compiler and CMake by handling all dependency installation, and provides consistent versions of dependencies across platforms.
The document announces an Orfeo ToolBox users meeting and hackfest to take place from June 3-5 in Toulouse, France. The tentative agenda includes presentations from the development team and volunteers on Wednesday, tutorials on Thursday, and an all-day hackfest on Friday where participants can work on their own OTB projects. Attendees are encouraged to suggest discussion topics and programming changes to make the event most useful.
The new modular build system of OTB 5 organizes code into self-contained modules that have explicit dependencies. This improves on the previous system where code was organized into directories without clear dependencies, making it difficult for newcomers to add functionality. The new system uses CMake best practices and builds only enabled modules and their dependencies, allowing users to select what they want/need to build. Modules, including third parties, are now always built externally rather than having code contained within OTB.
Monitoring tropical forest cover Activities of ONFI in remote sensingotb
ONF International is an international firm specialized in forest management in tropical regions. They use remote sensing techniques like OTB and QGIS to monitor tropical forests. Their activities include REDD+ projects, forest monitoring, impact assessments, and plantation monitoring. They also build capacity by training countries to use satellite images for forest monitoring. They focus on free and open source software and tools like OTB, Sentinel-1 Toolbox, and PolSARPro. They work to improve and simplify tools like OTB for new users and support countries to monitor their forests.
This document discusses a pragmatic approach to remote sensing image processing using the Orfeo Toolbox (OTB) library and applications. It provides an overview of OTB's capabilities for pre-processing, feature extraction, classification, and change detection on remote sensing imagery. OTB aims to make algorithm development and validation easier through a C++ library that contains many algorithms and interfaces and is open-source and multi-platform. It incorporates functionality from other open-source libraries through a common interface. The document describes OTB's modular and scalable architecture, as well as efforts to make it easier for users through graphical applications and language bindings.
The document introduces the new Orfeo ToolBox Project Steering Committee (PSC) which aims to provide more open governance and sustainability compared to the previous "benevolent dictatorship" model. Key points:
- Previously, decisions were made by a small group at CNES who also did most of the development work.
- The new PSC allows any active contributor to become a member and participate in roadmap, release, and governance decisions.
- It establishes public processes for feature requests, voting, and transparency around the project's direction.
- This is meant to encourage more contributions and involvement from beyond the original small group, improving sustainability and transparency.
OTB: logiciel libre de traitement d'images satellitesotb
La multiplication des capteurs et des satellites d'une part et l'amélioration des produits issus de la télédétection d'autre part se traduisent par des applications de plus en plus nombreuses dans les divers domaines de l'observation de la Terre. Depuis plus de 7 ans le CNES développe l'OTB, une bibliothèque libre d'algorithmes de traitement d'images dédiée aux données de télédétection. La librairie et le logiciel Monteverdi fédèrent maintenant autour d'elle une large communauté d'utilisateurs et de contributeurs.
#OSSPARIS19 - Computer Vision framework for GeoSpatial Imagery: RoboSat.pink ...Paris Open Source Summit
#IA Track - Technology and Tools
The volume of Earth Observation imagery, acquired by satellites and drones is huge (~100 To/day).
DeepLearning approach allow to switch from pixels to insights, the only way to be able to really use these initial raw data.
RoboSat.pink is a modular and extensible OpenSource Computer Vision framework, bridging cuting-edges research papers to a robust implementation.
This presentation will focus on:
- Computer Vision 101 reminder
- The specificities of geospatial data aka "Spatial is Special"
- Mains RoboSat.pink use cases
- Current research issues in Computer Vision GeoSpatial
This document discusses edge detection algorithms for images using a Raspberry Pi single-board computer. It describes configuring the Raspberry Pi operating system, installing development tools like Geany IDE and OpenCV library, and writing Python programs to test edge detection algorithms like Canny, Sobel, and Laplace. Results show that Canny edge detection produced the most accurate edges compared to other methods. The goal is to use edge detection for automated visual inspection in industry applications.
Eclipse Con Europe 2014 How to use DAWN Science ProjectMatthew Gerring
This document summarizes the DawnScience Eclipse project, which is an open source not-for-profit project on GitHub. It aims to provide APIs and reference implementations for loading, describing, slicing, transforming, and plotting multidimensional scientific data. Phase 1 from 2014-2015 defined long-term APIs and a reference implementation for HDF5 loading, data description, plotting, and slicing interfaces. Phase 2 in 2016 will release concrete implementations. The project utilizes Eclipse technologies and collaborates with scientific facilities.
Usages of OTB at SERTIT OTB Users meeting and hackfest 2015otb
SERTIT uses OTB applications on both Linux and Windows platforms for remote sensing tasks. OTB is handled through Ubuntu, Scientific Linux, and Windows 7 operating systems. Examples of OTB usage at SERTIT include analyzing vegetation along a railway using Pleiades imagery in a collaboration with SNCF, and understanding changes at China's Poyang Lake using a temporal series of 11 Pleiades images to extract water, calculate submersion time, and classify land use into 16 classes.
Teaching Remote Sensing with OTB Applications & Monterverdi (and a little of ...otb
This document discusses using the open-source software OTB Applications and Monteverdi to teach remote sensing techniques to students. It provides an overview of the curriculum, which includes radiometric analysis, NDVI calculation, supervised classification, and case studies. Sample code demonstrates how to perform change detection, classify images from different dates, and construct and analyze a time series of images. The software configuration and tools in Monteverdi and OTB Applications for visualization, processing, and classification are also outlined.
Power Point Presentation on object detection using tensorflow :
TensorFlow™ is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.
Monteverdi - Remote sensing software from educational to operational context otb
The Monteverdi and Orfeo Toolbox software were developed to:
1) Provide an integrated and simple interface for processing remote sensing imagery despite the complexity of underlying processes and tools.
2) Allow both experts and non-experts to efficiently extract information from large volumes of remote sensing data.
3) Promote open-source standards and reuse of existing libraries to achieve processing capabilities without reinventing methods.
Tom and Spike classifier using TensorFlow Object Detection. Presentation slides of the meetup TFOD conducted on 17/11/2018 at Algoscale Technologies Inc.
The document summarizes a research paper that compares the performance of MLP-based models to Transformer-based models on various natural language processing and computer vision tasks. The key points are:
1. Gated MLP (gMLP) architectures can achieve performance comparable to Transformers on most tasks, demonstrating that attention mechanisms may not be strictly necessary.
2. However, attention still provides benefits for some NLP tasks, as models combining gMLP and attention outperformed pure gMLP models on certain benchmarks.
3. For computer vision, gMLP achieved results close to Vision Transformers and CNNs on image classification, indicating gMLP can match their data efficiency.
This document summarizes a research paper on scaling laws for neural language models. Some key findings of the paper include:
- Language model performance depends strongly on model scale and weakly on model shape. With enough compute and data, performance scales as a power law of parameters, compute, and data.
- Overfitting is universal, with penalties depending on the ratio of parameters to data.
- Large models have higher sample efficiency and can reach the same performance levels with less optimization steps and data points.
- The paper motivated subsequent work by OpenAI on applying scaling laws to other domains like computer vision and developing increasingly large language models like GPT-3.
This document provides an overview of the Monteverdi framework for building image processing pipelines using components from the Orfeo Toolbox. It describes Monteverdi's main menu options for file handling, visualization, filtering, learning algorithms, SAR processing, and geometry functions. Special features like caching results and modifying modules in the pipeline are also outlined. The presentation concludes by discussing future evolutions planned for Monteverdi.
This document discusses change detection techniques using the Orfeo Toolbox. It presents several use cases for identifying changes between images using modules for change detection, SVM classification, and band math. The main challenges are distinguishing exceptional changes from natural changes over time in vegetation. Menus and steps are provided to open and preprocess images, classify changes versus unchanged areas, learn from sample inputs, and refine results.
The presentation introduces the Orfeo Toolbox (OTB), an open source library for image processing. It provides functions for tasks like orthorectification, fusion, filtering, segmentation, classification. It also presents applications and Monteverdi, a framework to build processing pipelines interactively. The documentation includes user guides, Doxygen documentation, code examples and a cookbook.
- The document discusses the Orfeo ToolBox (OTB) users meeting and hackfest in 2015, specifically regarding third party dependencies and the SuperBuild system.
- It outlines how OTB has reduced the number of third party dependencies and now uses a SuperBuild system to download, compile, and install dependencies at build time rather than including their source code directly.
- The SuperBuild system allows OTB to be built on any platform with just a compiler and CMake by handling all dependency installation, and provides consistent versions of dependencies across platforms.
The document announces an Orfeo ToolBox users meeting and hackfest to take place from June 3-5 in Toulouse, France. The tentative agenda includes presentations from the development team and volunteers on Wednesday, tutorials on Thursday, and an all-day hackfest on Friday where participants can work on their own OTB projects. Attendees are encouraged to suggest discussion topics and programming changes to make the event most useful.
The new modular build system of OTB 5 organizes code into self-contained modules that have explicit dependencies. This improves on the previous system where code was organized into directories without clear dependencies, making it difficult for newcomers to add functionality. The new system uses CMake best practices and builds only enabled modules and their dependencies, allowing users to select what they want/need to build. Modules, including third parties, are now always built externally rather than having code contained within OTB.
Monitoring tropical forest cover Activities of ONFI in remote sensingotb
ONF International is an international firm specialized in forest management in tropical regions. They use remote sensing techniques like OTB and QGIS to monitor tropical forests. Their activities include REDD+ projects, forest monitoring, impact assessments, and plantation monitoring. They also build capacity by training countries to use satellite images for forest monitoring. They focus on free and open source software and tools like OTB, Sentinel-1 Toolbox, and PolSARPro. They work to improve and simplify tools like OTB for new users and support countries to monitor their forests.
This document discusses a pragmatic approach to remote sensing image processing using the Orfeo Toolbox (OTB) library and applications. It provides an overview of OTB's capabilities for pre-processing, feature extraction, classification, and change detection on remote sensing imagery. OTB aims to make algorithm development and validation easier through a C++ library that contains many algorithms and interfaces and is open-source and multi-platform. It incorporates functionality from other open-source libraries through a common interface. The document describes OTB's modular and scalable architecture, as well as efforts to make it easier for users through graphical applications and language bindings.
The document introduces the new Orfeo ToolBox Project Steering Committee (PSC) which aims to provide more open governance and sustainability compared to the previous "benevolent dictatorship" model. Key points:
- Previously, decisions were made by a small group at CNES who also did most of the development work.
- The new PSC allows any active contributor to become a member and participate in roadmap, release, and governance decisions.
- It establishes public processes for feature requests, voting, and transparency around the project's direction.
- This is meant to encourage more contributions and involvement from beyond the original small group, improving sustainability and transparency.
OTB: logiciel libre de traitement d'images satellitesotb
La multiplication des capteurs et des satellites d'une part et l'amélioration des produits issus de la télédétection d'autre part se traduisent par des applications de plus en plus nombreuses dans les divers domaines de l'observation de la Terre. Depuis plus de 7 ans le CNES développe l'OTB, une bibliothèque libre d'algorithmes de traitement d'images dédiée aux données de télédétection. La librairie et le logiciel Monteverdi fédèrent maintenant autour d'elle une large communauté d'utilisateurs et de contributeurs.
#OSSPARIS19 - Computer Vision framework for GeoSpatial Imagery: RoboSat.pink ...Paris Open Source Summit
#IA Track - Technology and Tools
The volume of Earth Observation imagery, acquired by satellites and drones is huge (~100 To/day).
DeepLearning approach allow to switch from pixels to insights, the only way to be able to really use these initial raw data.
RoboSat.pink is a modular and extensible OpenSource Computer Vision framework, bridging cuting-edges research papers to a robust implementation.
This presentation will focus on:
- Computer Vision 101 reminder
- The specificities of geospatial data aka "Spatial is Special"
- Mains RoboSat.pink use cases
- Current research issues in Computer Vision GeoSpatial
This document discusses edge detection algorithms for images using a Raspberry Pi single-board computer. It describes configuring the Raspberry Pi operating system, installing development tools like Geany IDE and OpenCV library, and writing Python programs to test edge detection algorithms like Canny, Sobel, and Laplace. Results show that Canny edge detection produced the most accurate edges compared to other methods. The goal is to use edge detection for automated visual inspection in industry applications.
Eclipse Con Europe 2014 How to use DAWN Science ProjectMatthew Gerring
This document summarizes the DawnScience Eclipse project, which is an open source not-for-profit project on GitHub. It aims to provide APIs and reference implementations for loading, describing, slicing, transforming, and plotting multidimensional scientific data. Phase 1 from 2014-2015 defined long-term APIs and a reference implementation for HDF5 loading, data description, plotting, and slicing interfaces. Phase 2 in 2016 will release concrete implementations. The project utilizes Eclipse technologies and collaborates with scientific facilities.
The document provides an overview of the Orfeo Toolbox (OTB), an open-source library for processing remote sensing imagery. It discusses the history and goals of OTB, how it was developed by leveraging existing open-source libraries, and its growing user community. It also describes how OTB integrates with GIS software like QGIS through plugins that allow processing remote sensing data within a GIS environment.
OpenPOWER Webinar from University of Delaware - Title :OpenMP (offloading) o...Ganesan Narayanasamy
This presentation discusses the on-going project on building a validation and verification (V&V) testsuite of the widely popular directive-based parallel programming model, OpenMP. The talk will present results of the OpenMP offloading features implemented in various compilers targeting Summit among other systems. This project is open-source and the SOLLVE V&V team welcomes collaborations.
Toolchain for real-time simulations: GSN-MeteoIO-GEOtopRiccardo Rigon
This document describes a real-time toolchain for collecting sensor data through GSN, feeding it into the GEOtop hydrological model, and publishing the real-time simulation results. Key aspects include making GEOtop capable of starting, pausing and resuming simulations using recovery files, accessing sensor data through the MeteoIO library and its GSNIO plugin, and demonstrating the end-to-end workflow of retrieving real-time data and running GEOtop simulations. Future work includes enhancing GSN and the models to better support real-time gridded data and multiple concurrent users.
Slides for a talk at PyCon AU 2013. Integrating PyDAP + WMS + OpenLayers + IPython Notebook.
Video: http://www.youtube.com/watch?v=YJqBGi48RAM
The IPython Notebook is a powerful web app for exploring ideas and data sets with Python. It has excellent integration with Matplotlib, giving the user highly customisable static plots with ease. But for larger data sets, a static plot may not be ideal - the ability to pan, zoom, choose dynamic layers and sample the data at particular points would be nice. This talk will demonstrate just how easy it is to integrate a Web Map Service/client such as Pydap/Leaflet.js into the IPython Notebook.
5.3 Produits & Services en Observation de la Terre au service de la coopérati...grisicap
The document discusses how the Galileo satellite navigation system can help with earth observation products and services. It describes Spot Image's mission to provide geographic information solutions using satellite imagery. Galileo could improve the accuracy of satellite imagery and derived maps by providing precise positioning without ground control points. This would increase the value of earth observation data and enable new location-based services.
This document provides an introduction and overview of an upcoming hands-on session for using open source software for scientific data analysis. It will include demonstrations of importing different data formats into QGIS, performing spatial analysis and data visualization, and briefly introducing other open source software like GDAL/OGR, GMT, R, and GRASS GIS. The presentation slides, hands-on data, software website links, and a reference are available at a provided URL. The hands-on session will focus on using the open source GIS software QGIS to work with various scientific data formats from remote sensing and perform basic spatial analysis and mapping.
On Implementation of Neuron Network(Back-propagation)Yu Liu
This document outlines Yu Liu's work implementing and comparing different parallel versions of a neural network using backpropagation. It discusses motivations for parallel programming practice and library study. It provides an introduction to neural networks and backpropagation algorithms. Three implementations are compared: sequential C++ STL, Skelton library, and Intel TBB. Benchmark results show improved speedups from parallel versions. Remaining challenges are also noted, like addressing local minima problems and testing on larger data.
Scaling Deep Learning Algorithms on Extreme Scale Architecturesinside-BigData.com
This document summarizes a presentation on scaling deep learning algorithms on extreme scale architectures. It discusses challenges in using deep learning, a vision for machine/deep learning R&D including novel algorithms, and the MaTEx toolkit which supports distributed deep learning on GPU and CPU clusters. Sample results show strong and weak scaling of asynchronous gradient descent on Summit. Fault tolerance needs and the impact of deep learning on other domains are also covered.
Project Matsu: Elastic Clouds for Disaster ReliefRobert Grossman
The document discusses Project Matsu, an initiative by the Open Cloud Consortium to provide cloud computing resources for large-scale image processing to assist with disaster relief. It proposes three technical approaches: 1) Using Hadoop and MapReduce to process images in parallel across nodes; 2) Using Hadoop streaming with Python to preprocess images into a single file for processing; and 3) Using the Sector distributed file system and Sphere UDFs to process images while keeping them together on nodes without splitting files. The overall goal is to enable elastic computing on petabyte-scale image datasets for change detection and other analyses to support disaster response.
Map Styling Tools and Interactive maps on the web with OpenLayers - Addy Pope...JISC GECO
Presentation given as part of the DevCSI/JISC GECO Open Mapping Workshop which was held at the Electron Club, CCA, Glasgow on Thursday 25th August 2011. The event was connected to the OpenStreetMap State of the Map Scotland event.
Integration for Planet Satellite ImagerySafe Software
Planet offers up-to-date, high-quality images of the entire earth from 150+ satellites. Learn how to build automated workflows that integrate your data with these images in various ways. You’ll see how to transform and analyze bands, blend your data with the most up-to-date satellite images, and create cloud-based workflows to deliver images automatically. We’ll walk through an example that overlays live transit data on an up-to-date basemap with minimal cloud coverage. Plus, see what’s in beta for FME 2018 and Planet basemaps.
MapInfo Professional 12.5 and Discover3D 2014 - A brief overviewPrakher Hajela Saxena
MapInfo Professional and Discover3D is a complete suite of software specifically designed for geoscientists, environmentalists, and geochemists.
The software is being used in various industries today like, environment, mining, exploration, hydrology, etc.
Exploration of U-Net and Support Vector Machine classification methods for UAV multispectral image segmentation
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1. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Land cover mapping with high resolution satellite images using
Orfeo Toolbox, QGIS and OSM
Workshop - W04
Julien Michel (CNES), Jordi Inglada (CNES/CESBIO), Manuel Grizonnet (CNES)
July 14th 2015 - FOSS4GE, Como, Italy
2. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Disclaimer
We come from the remote sensing world, not the GIS one,
What we propose in this workshop works, we tested it!
But ... It may look over-complicated to the GIS ninjas in the audience
There are for sure many smarter ways for the GIS processing we will do
Not to mention better GIS data sources or more adapted tools!
Keep in mind: The workshop focuses on the concept, not the tools (except for
Orfeo ToolBox)
3. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
About land cover mapping and supervised classification
Land cover maps are produced using supervised classification
A supervised classifier is trained to assign class labels to input features
Reference data is used for training and validation
Data Preparation
Supervised Learning
Map Production
Reference Data
Sample Selection
sample ratio
Training Samples
Validation Samples
Input Images
Feature Extraction
Radiances, NDVI, NDWI
Training
SVM, RF
Classification
Model
Classification Land Cover Map Validation
OA, FScore
4. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Can we use OpenStreetMap as reference data?
Reference data has to:
represent the land cover classes of interest
be spatially coherent with the input images
contain few errors
OSM pros:
available everywhere
rich nomenclature
good geometric quality
OSM cons:
may be out of date in many places
polygons of one class may contain objects of other classes
May have improper classes for land-cover mapping (e.g. land use)
5. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Outline of the workshop
In this workshop we will do the following
1. Extract relevant data from OpenStreetMap to perform supervised image
classification
2. Experiment several classification set-ups and assess their performances
3. Use the final classification map to highlight OpenStreetMap shapes that may
need an update
Using the following software
Gdal for vectorial data manipulation
Orfeo ToolBox for image processing and classification
QGis for visualisation
And data
SPOT4 (Take5) time series over Ardeche, France as a proxy for future Sentinel-2
data,
Offline OpenStreetMap export from geofrabrick.de
6. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Data
Software
Outline
About the data and software
Data
Software
Preparing the data
Image preprocessing
OpenStreetMap preprocessing
Image Classification
Estimation of image statistics
Training of the classification algorithm
Classifying the image
Noise cleaning
Getting back to OpenStreetMap
Accuracy assesment
Where do OSM and Land Cover differs
7. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Data
Software
SPOT4 (Take5) data and upcoming Sentinel-2 data
Sentinel-2
2 satellites ESA mission, first
launch june 2015
Will observe all continental lands
every 5 days
13 spectral bands and spatial
resolution of 10 to 60 meters
Application friendly licence
SPOT4 (Take5)
CNES short term experiment for
SPOT4 end of life
Simulate temporal revisit of future
Sentinel-2 mission
on 42 sites of 60x60 squared km.
around the Earth
With a spatial resolution of 20
meters and 4 spectral bands
SPOT4 (Take5) data will be used in this workshop as a proxy of future Sentinel-2 data.
8. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Data
Software
Images overview (SPOT4 (Take5) Ardeche site, France)
9. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Data
Software
OpenStreetMap data (exports from geofabrick.de)
Land use, natural and waterways layers
10. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Data
Software
The Orfeo ToolBox
What is the Orfeo ToolBox?
An image processing library dedicated to remote sensing,
An open-source software under the CeCILL-v2 licence (French equivalent to
GPL),
Funded by CNES in the frame of the Orfeo program (and beyond),
Written in C++ on top of ITK (free medical image processing library),
Based on many other image processing and remote sensing open-source software
such as Gdal, OSSIM or OpenCV
Designed to process large data volumes seamlessly thanks to piece-wise
processing and multi-threading
www.orfeo-toolbox.org
11. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Data
Software
How to use the Orfeo ToolBox
Write your own code
Flexible, full API available, requires C++ knowledge
Use OTB applications
High level functions (e.g. segmentation), command-line, graphical interface, or Python
writing availabe. Can be extended (write your own app). Also available in Qgis
processing framework.
Use Monteverdi2
Data visualization and access to all applications in an integrated software
12. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Data
Software
Other software used in this workshop
Do we really need to introduce them ?!?
We will be using the OSgeoLive 8.5 (Orfeo ToolBox 4.2.1, Gdal 1.11.0, Qgis 2.4.0)
USB stick provided by FOSS4GE organization. . . Time to insert it into your computer!
13. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Image preprocessing
OpenStreetMap preprocessing
Outline
About the data and software
Data
Software
Preparing the data
Image preprocessing
OpenStreetMap preprocessing
Image Classification
Estimation of image statistics
Training of the classification algorithm
Classifying the image
Noise cleaning
Getting back to OpenStreetMap
Accuracy assesment
Where do OSM and Land Cover differs
14. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Image preprocessing
OpenStreetMap preprocessing
Objectives of this section
1. Pre-process image to get it ready for visualisation and classification
2. Learn some basics of command-line OTB processing
15. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Image preprocessing
OpenStreetMap preprocessing
A first glimpse at the image
The image is located here :
Folder:
raw_data/spot4t5/SPOT4_HRVIR1_XS_20130607_N2A_CArdecheD0000B0000/
Image file:
SPOT4_HRVIR1_XS_20130607_N2A_ORTHO_SURF_CORR_PENTE_CArdecheD0000B0000.TIF
In the following slides, it will be refered to as im.tif.
1. Open the image in Qgis
2. In layer properties:
2.1 Set the no data value to -10 000 in the transparency tab,
2.2 In the style tab, change color composition to r=b3,g=b2,b=b1,
2.3 Reload statistics in the Style tab.
What do you see ?
16. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Image preprocessing
OpenStreetMap preprocessing
Natural colors synthesis
SPOT4 images have the following spectral bands: Green (b1), Red (b2), Near
Infra-Red (b3), Short Wavelength Infra-Red (b4)
To get familiar with Orfeo ToolBox processing, and to ease data visualisation, we will
build a synthetic green and blue channel with the following formula:
b = 0.7 ∗ green + 0.24 ∗ red − 0.14 ∗ swir (1)
We are going to use the BandMath application, as well as the ConcatenateImages
application.
Caution: this synthetic image is only for visualisation, not for processing!
17. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Image preprocessing
OpenStreetMap preprocessing
Natural colors synthetis (Solution)
Extract red band:
$ otbcli_BandMath -il im.tif -out red.tif int16 -exp "im1b2"
Extract green band:
$ otbcli_BandMath -il im.tif -out green.tif int16 -exp "im1b1"
Compute synthetic blue band:
$ otbcli_BandMath -il im.tif -out blue.tif int16 -exp "im1b1==-10000?-10000:0.7*im1b1+0.24*im1b2-0.14*im1b3"
Concatenate all bands:
$ otbcli_ConcatenateImages -il red.tif green.tif blue.tif -out rgb.tif int16
Open resulting image in qgis, and set the rendering option likewise.
18. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Image preprocessing
OpenStreetMap preprocessing
Radiometric indices that help classification
We will compute two more things that will help the classification process:
A Normalize Difference Vegetation Index:
ndvi = (nir − red)/(nir + red) (2)
A Normalize Difference Water Index:
ndvi = (nir − swir)/(nir + swir) (3)
You can visualize the result in qgis, and concatenate these two bands with the
original image
Solution
$ otbcli_BandMath -il im.tif -out ndvi.tif int16 -exp "im1b1==-10000?-10000:(im1b3-im1b2)/(im1b3+im1b2)*1000"
$ otbcli_BandMath -il im.tif -out ndwi.tif int16 -exp "im1b1==-10000?-10000:(im1b3-im1b4)/(im1b3+im1b4)*1000"
$ otbcli_ConcatenateImages -il im.tif ndvi.tif ndwi.tif -out im4classif.tif
19. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Image preprocessing
OpenStreetMap preprocessing
Objectives of this section
1. Turn OSM data into a vector layer suitable for classifier training:
1.1 Filter and join features to get a limited number of well-defined classes (in the sense of
classification)
1.2 Build a single layer with polygon feature bearing an integer class attribute
2. Learn how training polygons should be and which features of OSM are suitable
20. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Image preprocessing
OpenStreetMap preprocessing
Pre-processing OpenStreetMap data
The data are located in the raw data/osm/ folder.
For each region, open the following files on top of the image in Qgis:
landuse.hsp
natural.hsp
waterways.hsp
To get OpenStreetMap data ready for classification, we will do the following:
1. Extract geometries that actually cover our image (area of interest)
2. Reproject all geometries in the image SRS
3. Filter OSM classes to build a set of 3 classes suitable for supervised classification
4. Process waterways to turn polylines into polygons with buffers
5. Separate our set of polygons between a training set and a validation set
21. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Image preprocessing
OpenStreetMap preprocessing
Extracting geometries that cover our image
Extracting image footprint
1. Use the ImageEnvelope application
2. Output a shapefile or sqlite file
3. Control results in Qgis
4. For a more accurate result, draw the envelope by hand in Qgis
Clip OSM layers to ROI and reproject to image SRS
1. For each region and each layer
2. Use ogr2ogr
3. Use the -clipsrc option to filter by image footprint
4. Use the -t srs option to define the output SRS using the raw data/l93.wkt file
5. Use the -append option to merge both regions for each layer
22. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Image preprocessing
OpenStreetMap preprocessing
Extracting geometries that cover our image (solution)
Extract image enveloppe:
$ otbcli_ImageEnvelope -in SPOT4_HRVIR1_XS_20130607_rgb.tif -proj "EPSG:32631" -out env.shp
Clipping, reprojecting and merging land use layers:
$ ogr2ogr -append -t_srs raw_data/l93.wkt -clipsrc env.shp landuse_l93.shp raw_data/osm/auvergne/landuse.shp
$ ogr2ogr -append -t_srs raw_data/l93.wkt -clipsrc env.shp landuse_l93.shp raw_data/osm/rhone-alpes/landuse.shp
Clipping, reprojecting and merging natural layers:
$ ogr2ogr -append -t_srs raw_data/l93.wkt -clipsrc env.shp natural_l93.shp raw_data/osm/auvergne/natural.shp
$ ogr2ogr -append -t_srs raw_data/l93.wkt -clipsrc env.shp natural_l93.shp raw_data/osm/rhone-alpes/natural.shp
Clipping, reprojecting and merging waterways layers:
$ ogr2ogr -append -t_srs raw_data/l93.wkt -clipsrc env.shp waterways_l93.shp raw_data/osm/auvergne/waterways.shp
$ ogr2ogr -append -t_srs raw_data/l93.wkt -clipsrc env.shp waterways_l93.shp raw_data/osm/rhone-alpes/waterways.shp
Open the three new layers in Qgis.
23. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Image preprocessing
OpenStreetMap preprocessing
Build consistent classes for supervised classification
The idea
Select and join OSM features from landuse and natural layers
That form simple landcover classes (e.g. water or vegetation)
Which are big enough wrt. to image resolution (20m)
You can walk the layers and have a look at OSM spec here1
Our Proposal
Water:
basin, pond, reservoir, salt pond, water larger than 1000 squared meters from land use
layer
water larger than 1000 squared meters from natural layer
main rivers (Loire, Rhˆone) from waterways layer, buffered with 25 meters
Vegetation: forest from natural layer
Built-up: residential, commercial, cemetery, construction, industrial, recreational,
harbour, allotments, yard, brownfield, from land use layer
1
https://wiki.openstreetmap.org/wiki/Map_Features
24. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Image preprocessing
OpenStreetMap preprocessing
Building the water class
1. Extract the two large rivers covering the image:
$ ogr2ogr -append -sql "select * from waterways_l93 where name in ("La Loire", "Le Rh^one")" large_rivers.shp waterway
2. In Qgis, use the vector/geoprocessing/buffer tool to build a 25m buffer around,
and save it to water.shp.
3. Append selected features from land use layer:
$ ogr2ogr -append -sql "select * from landuse_l93 where type in
("basin","pond","reservoir","salt_pond","water")
and OGR_GEOM_AREA > 10000" water.shp landuse_l93.shp
4. Append selected features from natural layer:
$ ogr2ogr -append -sql "select * from natural_l93 where type in
("water") and OGR_GEOM_AREA > 1000" water.shp natural_l93.shp
25. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Image preprocessing
OpenStreetMap preprocessing
Building the vegetation and built-up classes
Vegetation class
Extract selected features from natural layer:
$ ogr2ogr -append -sql "select * from natural_l93 where type in
("forest")" forest.shp natural_l93.shp
Built-up class
Extract selected features from land use layer:
$ ogr2ogr -append -sql "select * from landuse_l93 where type in
("residential","commercial","cemetery","construction",
"industrial", "recreational","harbour",
"allotments","brownfield")" builtup.shp landuse_l93.shp
26. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Image preprocessing
OpenStreetMap preprocessing
Final steps (1/2): add class label, exclude overlaps
Add class labels
Goal: create a new integer field with a unique id for each of the 3 classes
Can be done in Qgis attribute table manager
Or With the VectorDataSetField application in OTB
Exclude overlaps
Overlapping polygons of different classes confuses training and evaluation
We will therefore ignore those areas
To do so, we will use the Vector/Geoprocessing tools/Differentiate tool in Qgis
Twice for each layer, e.g. Builtup vs. water then vs. forest . . .
27. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Image preprocessing
OpenStreetMap preprocessing
Final steps (2/2): build separate sets for training and validation, merge
Build separate sets for training (250 polygons of each class) and
validation (the remaining)
$ ogr2ogr -append -dialect SQLITE -sql "select * from forest order by osm_id limit 250" training.shp forest.shp
$ ogr2ogr -append -dialect SQLITE -sql "select * from water order by osm_id limit 250" training.shp water.shp
$ ogr2ogr -append -dialect SQLITE -sql "select * from builtup order by osm_id limit 250" training.shp builtup.shp
$ ogr2ogr -append -dialect SQLITE -sql "select * from forest order by osm_id limit 250,1000000" validation.shp forest.shp
$ ogr2ogr -append -dialect SQLITE -sql "select * from water order by osm_id limit 250,1000000" validation.shp water.shp
$ ogr2ogr -append -dialect SQLITE -sql "select * from builtup order by osm_id limit 250,100000" validation.shp builtup.shp
Also merge everything in a single layer
$ ogr2ogr -append all.shp water.shp
$ ogr2ogr -append all.shp forest.shp
$ ogr2ogr -append all.shp builtup.shp
28. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Estimation of image statistics
Training of the classification algorithm
Classifying the image
Noise cleaning
Outline
About the data and software
Data
Software
Preparing the data
Image preprocessing
OpenStreetMap preprocessing
Image Classification
Estimation of image statistics
Training of the classification algorithm
Classifying the image
Noise cleaning
Getting back to OpenStreetMap
Accuracy assesment
Where do OSM and Land Cover differs
29. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Estimation of image statistics
Training of the classification algorithm
Classifying the image
Noise cleaning
Objectives of this section
1. Learn the steps of supervised classification processing with Orfeo ToolBox
2. Perform the classification based on the OSM pre-processed data
30. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Estimation of image statistics
Training of the classification algorithm
Classifying the image
Noise cleaning
Step 1: Estimation of image statistics
Some machine learning algorithm require the input features to have similar ranges
Also, the SVM algorithm will converge faster if this range is [−1, 1]
We will therefore center and reduce all image bands prior to classification
For this, we need to estimate the mean and variance of each band
Which is what this step is about
For this we will use the EstimateImagesStatistics application. Do not forget to set
the background value (-10 000)!
31. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Estimation of image statistics
Training of the classification algorithm
Classifying the image
Noise cleaning
Step 1: Estimation of image statistics (solution)
$ otbcli_ComputeImagesStatistics -il im4classif.tif -out stats.xml -bv -10000
Look at the stats.xml file. Does it look correct?
32. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Estimation of image statistics
Training of the classification algorithm
Classifying the image
Noise cleaning
Step 2: Training the classification algorithm
We will be using the TrainImagesClassifier application
The application should receive the image, the training layer and the xml stats file,
We will use the libsvm implementation, with a RBF kernel,
We will select at most 1000 random samples per class (2000 for validation),
You also need to set the name of the class field in the training layer.
33. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Estimation of image statistics
Training of the classification algorithm
Classifying the image
Noise cleaning
Step 2: Training the classification algorithm (solution)
$ otbcli_TrainImagesClassifier -io.il im4classif.tif -io.vd training.shp -io.out model.svm
-classifier libsvm -classifier.libsvm.k rbf -sample.mt 1000 -sample.mv 2000
-sample.vfn "class" -io.imstat stats.xml
2015 Jun 15 13:56:21 : Application.logger (INFO) Confusion matrix (rows = reference labels, columns = produced labels):
[1] [2] [3]
[ 1] 1668 278 73
[ 2] 46 1773 146
[ 3] 52 208 1772
2015 Jun 15 13:56:21 : Application.logger (INFO) Precision of class [1] vs all: 0.944507
2015 Jun 15 13:56:21 : Application.logger (INFO) Recall of class [1] vs all: 0.826152
2015 Jun 15 13:56:21 : Application.logger (INFO) F-score of class [1] vs all: 0.881374
2015 Jun 15 13:56:21 : Application.logger (INFO) Precision of class [2] vs all: 0.784861
2015 Jun 15 13:56:21 : Application.logger (INFO) Recall of class [2] vs all: 0.90229
2015 Jun 15 13:56:21 : Application.logger (INFO) F-score of class [2] vs all: 0.839489
2015 Jun 15 13:56:21 : Application.logger (INFO) Precision of class [3] vs all: 0.890005
2015 Jun 15 13:56:21 : Application.logger (INFO) Recall of class [3] vs all: 0.872047
2015 Jun 15 13:56:21 : Application.logger (INFO) F-score of class [3] vs all: 0.880935
2015 Jun 15 13:56:21 : Application.logger (INFO) Global performance, Kappa index: 0.799899
34. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Estimation of image statistics
Training of the classification algorithm
Classifying the image
Noise cleaning
Step 3: Classifying the image
Now that we trained a classifier with a satisfactory level of performance, lets
classify the image,
We will use the ImageClassifier application,
It should receive the input image, the model and the stats files,
We will also build a mask of no data pixels, so that they will be ignored during
classification (use the BandMath app for this)
35. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Estimation of image statistics
Training of the classification algorithm
Classifying the image
Noise cleaning
Step 3: Classifying the image (solution)
$ otbcli_BandMath -il im4classif.tif -out mask.tif uint8 -exp "im1b1>-10000?255:0"
$ otbcli_ImageClassifier -in im4classif.tif -mask mask.tif -model model.svm -imstat stats.xml -out classif.tif uint8
Open the resulting land cover map in Qgis. Set the rendering to color each class with
a discriminative color. Does the classification map look correct?
36. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Estimation of image statistics
Training of the classification algorithm
Classifying the image
Noise cleaning
Step 4: Classification noise cleaning
Classification maps often suffer from salt and pepper noise (isolated pixels with
class different from neighborhood)
We can filter that with the ClassificationMapRegularization application
It will perform a majority voting of classified pixels in a fixed neighborhood
(radius = 2 for instance)
37. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Estimation of image statistics
Training of the classification algorithm
Classifying the image
Noise cleaning
Step 4: Classification noise cleaning
$ otbcli_ClassificationMapRegularization -io.in classif.tif -io.out classif_reg.tif -ip.radius 2
Load the cleaned map into QGis as well.
38. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Accuracy assesment
Where do OSM and Land Cover differs
Outline
About the data and software
Data
Software
Preparing the data
Image preprocessing
OpenStreetMap preprocessing
Image Classification
Estimation of image statistics
Training of the classification algorithm
Classifying the image
Noise cleaning
Getting back to OpenStreetMap
Accuracy assesment
Where do OSM and Land Cover differs
39. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Accuracy assesment
Where do OSM and Land Cover differs
Getting a better idea of our land cover accuracy
What is our final accuracy after regularization (e.g. denoising)?
What is our final accuracy wrt. our validation layer?
We will find out with the ComputeConfusionMatrix application
Which can cross-compare our land-cover map with our validation layer
40. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Accuracy assesment
Where do OSM and Land Cover differs
Accuracy assesment (solution)
$ otbcli_ComputeConfusionMatrix -in classif_reg.tif -ref vector -ref.vector.in validation.shp
-ref.vector.field "class" -nodatalabel 0 -out conf.txt
2015 Jun 15 17:36:11 : Application.logger (INFO) Confusion matrix (rows = reference labels, columns = produced labels):
[ 1] [ 2] [ 3]
[ 1] 24532 4491 1258
[ 2] 8903 1761939 141961
[ 3] 2830 36018 573860
2015 Jun 15 17:36:11 : Application.logger (INFO) Precision of class [1] vs all: 0.676465
2015 Jun 15 17:36:11 : Application.logger (INFO) Recall of class [1] vs all: 0.810145
2015 Jun 15 17:36:11 : Application.logger (INFO) F-score of class [1] vs all: 0.737295
2015 Jun 15 17:36:11 : Application.logger (INFO) Precision of class [2] vs all: 0.977526
2015 Jun 15 17:36:11 : Application.logger (INFO) Recall of class [2] vs all: 0.921129
2015 Jun 15 17:36:11 : Application.logger (INFO) F-score of class [2] vs all: 0.94849
2015 Jun 15 17:36:11 : Application.logger (INFO) Precision of class [3] vs all: 0.800274
2015 Jun 15 17:36:11 : Application.logger (INFO) Recall of class [3] vs all: 0.936596
2015 Jun 15 17:36:11 : Application.logger (INFO) F-score of class [3] vs all: 0.863086
2015 Jun 15 17:36:11 : Application.logger (INFO) Precision of the different classes: [0.676465, 0.977526, 0.800274]
2015 Jun 15 17:36:11 : Application.logger (INFO) Recall of the different classes: [0.810145, 0.921129, 0.936596]
2015 Jun 15 17:36:11 : Application.logger (INFO) F-score of the different classes: [0.737295, 0.94849, 0.863086]
2015 Jun 15 17:36:11 : Application.logger (INFO) Kappa index: 0.811052
2015 Jun 15 17:36:11 : Application.logger (INFO) Overall accuracy index: 0.923522
41. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Accuracy assesment
Where do OSM and Land Cover differs
Where do OSM and Land Cover differs?
Now that we have a rough idea of the level of agreement between our land cover map
and the OSM layer, it would be useful to see where the differences are.
We can use the Rasterization application to make a raster out of our layer
And then use the BandMath to make a raster of the disagreements between
landcover and OSM
Write the proper expression so that the raster is null or bears the land cover class
if it differs from OSM
42. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Accuracy assesment
Where do OSM and Land Cover differs
Where do OSM and Land Cover differ? (Solution)
Rasterize our layer
$ otbcli_Rasterization -in all.shp -out all.tif -im im4classif.tif
-mode attribute -mode.attribute.field "class" -background 0
Compute the difference map
$ otbcli_BandMath -il classif_reg.tif all.tif -out errors.tif
-exp "im2b1>0?(im1b1!=im2b1?im1b1:0):0"
Display the difference map in Qgis, and superimpose the original OSM layers. Find
evidence of:
Forest areas that are endangered by urban growth,
Parks and other green areas in residential neighborhood that are not referenced in
OSM,
Water bodies that are not referenced as well.
43. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Conclusion
Time for a talk
According to what you did in this workshop, do you think OpenStreetMap can be used
for S2 land-cover applications? And vice-versa?
To go further
How well does the trained model generalize to other dates / areas?
Can we add new classes for a more detailed map?
Can we set-up automatic alerts on some features (e.g. forest polygons that do
not seem to contain a lot of forest) ?
44. Introduction
About the data and software
Preparing the data
Image Classification
Getting back to OpenStreetMap
Conclusion
Thank you for attending this workshop! Any questions?