This content presents how to classify satellite image by QGIS Semi-automatic classification plugin. It includes pre-processing, create a region of interest (AOI), and applying classification methods.
The objective of image classification is to classify each pixel into only one class (crisp or hard classification) or to associate the pixel with many classes (fuzzy or soft classification). The classification techniques may be categorized either on the basis of training process (supervised and unsupervised) or on the basis of theoretical model (parametric and non-parametric).
Unsupervised classification is where the groupings of pixels with common characteristics are based on the software analysis of an image without the user providing sample classes. The computer uses techniques to determine which pixels are related and groups them into classes. The user can specify which algorism the software will use and the desired number of output classes but otherwise does not aid in the classification process. However, the user must have knowledge of the area being classified when the groupings of pixels with common characteristics produced by the computer have to be related to actual features on the ground (such as waterbodies, developed areas, forests, etc.).
Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image. Input classes are selected based on the knowledge of the user. The user also sets the bounds for how similar other pixels must be to group them together. These bounds are often set based on the spectral characteristics of the input classes (AOI), plus or minus a certain increment (often based on “brightness” or strength of reflection in specific spectral bands). The user also designates the number of classes that the image is classified into.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Accuracy assessment is an important part of any classification project. It compares the classified image to another data source that is considered being accurate or ground truth data.
I prepared this presentation for the interactive class of the course "Arcgis and Remote Sensing" organized by the Research Society, Bangladesh.
Presentation on applications of AI in the geospatial domain at the Fourth Edition of AI in Practice (6th November 2019, Startup Village, Amsterdam, The Netherlands)
Erik Van Der Zee, Enterprise Architect, Geodan
The following presentation was delivered by Robert Morrison, Principal Consultant at Esri Ireland, at the 2019 NICS ICT Conference in October 2019.
The presentation focuses on taking a geographic approach to machine learning to help you "see what other's can't".
Imagery and remotely sensed data is a valuable resource for many organisations who have made substantial investment obtaining the data. The field of Machine Learning is both broad and deep and is constantly evolving. Using ArcGIS and Machine Learning allows organisations to derive valuable new content.
ArcGIS is an open, interoperable platform that allows for the integration of complementary methods and techniques that empower ArcGIS users to solve complex, real-world problems in a fundamentally spatial way.
Learn how by combining powerful built-in Image analysis tools with any machine learning package users can benefit from the spatial validation, geo-enrichment and visualisation. See how this Machine Learning is being applied in real world use-cases from marine farming and crime analysis to agriculture and sustainability.
The objective of image classification is to classify each pixel into only one class (crisp or hard classification) or to associate the pixel with many classes (fuzzy or soft classification). The classification techniques may be categorized either on the basis of training process (supervised and unsupervised) or on the basis of theoretical model (parametric and non-parametric).
Unsupervised classification is where the groupings of pixels with common characteristics are based on the software analysis of an image without the user providing sample classes. The computer uses techniques to determine which pixels are related and groups them into classes. The user can specify which algorism the software will use and the desired number of output classes but otherwise does not aid in the classification process. However, the user must have knowledge of the area being classified when the groupings of pixels with common characteristics produced by the computer have to be related to actual features on the ground (such as waterbodies, developed areas, forests, etc.).
Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image. Input classes are selected based on the knowledge of the user. The user also sets the bounds for how similar other pixels must be to group them together. These bounds are often set based on the spectral characteristics of the input classes (AOI), plus or minus a certain increment (often based on “brightness” or strength of reflection in specific spectral bands). The user also designates the number of classes that the image is classified into.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Accuracy assessment is an important part of any classification project. It compares the classified image to another data source that is considered being accurate or ground truth data.
I prepared this presentation for the interactive class of the course "Arcgis and Remote Sensing" organized by the Research Society, Bangladesh.
Presentation on applications of AI in the geospatial domain at the Fourth Edition of AI in Practice (6th November 2019, Startup Village, Amsterdam, The Netherlands)
Erik Van Der Zee, Enterprise Architect, Geodan
The following presentation was delivered by Robert Morrison, Principal Consultant at Esri Ireland, at the 2019 NICS ICT Conference in October 2019.
The presentation focuses on taking a geographic approach to machine learning to help you "see what other's can't".
Imagery and remotely sensed data is a valuable resource for many organisations who have made substantial investment obtaining the data. The field of Machine Learning is both broad and deep and is constantly evolving. Using ArcGIS and Machine Learning allows organisations to derive valuable new content.
ArcGIS is an open, interoperable platform that allows for the integration of complementary methods and techniques that empower ArcGIS users to solve complex, real-world problems in a fundamentally spatial way.
Learn how by combining powerful built-in Image analysis tools with any machine learning package users can benefit from the spatial validation, geo-enrichment and visualisation. See how this Machine Learning is being applied in real world use-cases from marine farming and crime analysis to agriculture and sustainability.
Urban Land Cover Change Detection Analysis and Modelling Spatio-Temporal Grow...Bayes Ahmed
This is my final Mater thesis presentation. The thesis defense was held on March' 07, 2011 at 15:30 in the seminar room of Universitat Jaume I (UJI), Castellón, Spain.
In the context of remote sensing, change detection refers to the process of identifying differences in the state of land features by observing them at different times. This process can be accomplished either manually (i.e., by hand) or with the aid of remote sensing software. Manual interpretation of change from satellite images or aerial photos involves an observer or analyst defining areas of interest and comparing them between images from two dates. This may be accomplished either on-screen (such as in a GIS) or on paper. When analyzing aerial photographs, a stereoscope which allows for two spatially-overlapping photos to be displayed in 3D, can aid photo interpretation. Manual image interpretation works well when assessing change between discrete classes (forest openings, land use and land cover maps) or when changes are large (e.g., heavy mechanized maneuver damage, engineering training impacts). Manual image interpretation is also an option when trying to determine change using images or photos from different sources (comparing historic aerial photographs to current satellite imagery).
Automated methods of remote sensing change detection usually are of two forms: post-classification change detection and image differencing using band ratios. In post-classification change detection, the images from each time period are classified using the same classification scheme into a number of discrete categories like land cover types. The two (or more) classifications are compared and the area that is classified the same or different is tallied. With image differencing, a band ratio such as NDVI is constructed from each input image, and the difference is taken between the band ratios of different times. In the case of differencing NDVI images, positive output values may indicate an increase in vegetation, negative values a decrease in vegetation, and values near zero no change. With either post-classification or image differencing change detection, it is necessary to specify a threshold below which differences between the two images is considered to be non-significant. The specification of thresholds is critical to the results of change detection analysis and usually must be found through an iterative process.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
A multispectral image is one that captures image data from two or more ranges of frequencies along the spectrum, such as visible light and infrared energy.
In multispectral images, the same spatial region is captured multiple times using different imaging modalities.
it is highly useful for geography students in the field of remote sensing and it is in very simple and explanatory for the purpose of simplification with relevant images in this ppt.
APPLICATIONS OF REMOTE SENSING AND GIS TECHNOLOGIES IN FLOOD RISK MANAGEMENTrsmahabir
Flooding is the most common of all major disasters that regularly affect populations and results in extensive damage to property, infrastructure, natural resources, and even to loss of life. To ensure better outcomes, planning and execution of flood management projects must utilize knowledge on a wide range of factors, most of which are of a spatial nature. Advances in geospatial technologies, specifically remote sensing and Geographic Information Systems (GIS), have enabled the acquisition and analysis of data about the Earth's surface for flood mitigation projects in a faster, more efficient and more accurate manner.
Remote sensing and GIS have emerged as powerful tools to deal with various aspects of flood management in prevention, preparedness and relief management of flood disaster. GIS facilitates integration of spatial and non-spatial data such as rainfall and stream flows, river cross sections and profiles, and river basin characteristics, as well as other information such as historical flood maps, infrastructures, land use, and social and economic data. Such data sets are critical for the in-depth analysis and management of floods.
Remote sensing technologies have great potential in overcoming the information void in the Caribbean region. The observation, mapping, and representation of Earth’s surface have provided effective and timely information for monitoring floods and their effect. The potential of new air- and space-borne imaging technologies for improving hazard evaluation and risk reduction is continually being explored. They are relatively inexpensive and have the ability to provide information on several parameters that are crucial to flood mapping and monitoring.
Taking a Geographic Approach to Machine Learning - Esri Ireland 'Do One Thing...Esri Ireland
Discover how you can take a geographic approach to machine learning to help you "See What Others Can't".
Imagery and remotely sensed data is a valuable resource for many organisations who have made substantial investment obtaining the data. The field of machine learning is both broad and deep and is constantly evolving. Using ArcGIS and machine learning allows organisations to derive valuable new content. ArcGIS is an open, interoperable platform that allows for the integration of complementary methods and techniques that empower ArcGIS users to solve complex, real-world problems in a fundamentally spatial way.
These slides were used as part of episode 6 of the Esri Ireland 'Do One Thing Well' Webinar Series. You can watch the webinar recording here: https://youtu.be/zAzNqw4KZRk
For any questions relating to the contents of this webinar or other GIS related inquiries, you can contact our team via mapsmakesense@esri-ireland.ie.
Urban Land Cover Change Detection Analysis and Modelling Spatio-Temporal Grow...Bayes Ahmed
This is my final Mater thesis presentation. The thesis defense was held on March' 07, 2011 at 15:30 in the seminar room of Universitat Jaume I (UJI), Castellón, Spain.
In the context of remote sensing, change detection refers to the process of identifying differences in the state of land features by observing them at different times. This process can be accomplished either manually (i.e., by hand) or with the aid of remote sensing software. Manual interpretation of change from satellite images or aerial photos involves an observer or analyst defining areas of interest and comparing them between images from two dates. This may be accomplished either on-screen (such as in a GIS) or on paper. When analyzing aerial photographs, a stereoscope which allows for two spatially-overlapping photos to be displayed in 3D, can aid photo interpretation. Manual image interpretation works well when assessing change between discrete classes (forest openings, land use and land cover maps) or when changes are large (e.g., heavy mechanized maneuver damage, engineering training impacts). Manual image interpretation is also an option when trying to determine change using images or photos from different sources (comparing historic aerial photographs to current satellite imagery).
Automated methods of remote sensing change detection usually are of two forms: post-classification change detection and image differencing using band ratios. In post-classification change detection, the images from each time period are classified using the same classification scheme into a number of discrete categories like land cover types. The two (or more) classifications are compared and the area that is classified the same or different is tallied. With image differencing, a band ratio such as NDVI is constructed from each input image, and the difference is taken between the band ratios of different times. In the case of differencing NDVI images, positive output values may indicate an increase in vegetation, negative values a decrease in vegetation, and values near zero no change. With either post-classification or image differencing change detection, it is necessary to specify a threshold below which differences between the two images is considered to be non-significant. The specification of thresholds is critical to the results of change detection analysis and usually must be found through an iterative process.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
A multispectral image is one that captures image data from two or more ranges of frequencies along the spectrum, such as visible light and infrared energy.
In multispectral images, the same spatial region is captured multiple times using different imaging modalities.
it is highly useful for geography students in the field of remote sensing and it is in very simple and explanatory for the purpose of simplification with relevant images in this ppt.
APPLICATIONS OF REMOTE SENSING AND GIS TECHNOLOGIES IN FLOOD RISK MANAGEMENTrsmahabir
Flooding is the most common of all major disasters that regularly affect populations and results in extensive damage to property, infrastructure, natural resources, and even to loss of life. To ensure better outcomes, planning and execution of flood management projects must utilize knowledge on a wide range of factors, most of which are of a spatial nature. Advances in geospatial technologies, specifically remote sensing and Geographic Information Systems (GIS), have enabled the acquisition and analysis of data about the Earth's surface for flood mitigation projects in a faster, more efficient and more accurate manner.
Remote sensing and GIS have emerged as powerful tools to deal with various aspects of flood management in prevention, preparedness and relief management of flood disaster. GIS facilitates integration of spatial and non-spatial data such as rainfall and stream flows, river cross sections and profiles, and river basin characteristics, as well as other information such as historical flood maps, infrastructures, land use, and social and economic data. Such data sets are critical for the in-depth analysis and management of floods.
Remote sensing technologies have great potential in overcoming the information void in the Caribbean region. The observation, mapping, and representation of Earth’s surface have provided effective and timely information for monitoring floods and their effect. The potential of new air- and space-borne imaging technologies for improving hazard evaluation and risk reduction is continually being explored. They are relatively inexpensive and have the ability to provide information on several parameters that are crucial to flood mapping and monitoring.
Taking a Geographic Approach to Machine Learning - Esri Ireland 'Do One Thing...Esri Ireland
Discover how you can take a geographic approach to machine learning to help you "See What Others Can't".
Imagery and remotely sensed data is a valuable resource for many organisations who have made substantial investment obtaining the data. The field of machine learning is both broad and deep and is constantly evolving. Using ArcGIS and machine learning allows organisations to derive valuable new content. ArcGIS is an open, interoperable platform that allows for the integration of complementary methods and techniques that empower ArcGIS users to solve complex, real-world problems in a fundamentally spatial way.
These slides were used as part of episode 6 of the Esri Ireland 'Do One Thing Well' Webinar Series. You can watch the webinar recording here: https://youtu.be/zAzNqw4KZRk
For any questions relating to the contents of this webinar or other GIS related inquiries, you can contact our team via mapsmakesense@esri-ireland.ie.
BDVe Webinar Series - Toreador Intro - Designing Big Data pipelines (Paolo Ce...Big Data Value Association
In the Internet of Everything, huge volumes of multimedia data are generated at very high rates by heterogeneous sources in various formats, such as sensors readings, process logs, structured data from RDBMS, etc. The need of the hour is setting up efficient data pipelines that can compute advanced analytics models on data and use results to customize services, predict future needs or detect anomalies. This Webinar explores the TOREADOR conversational, service-based approach to the easy design of efficient and reusable analytics pipelines to be automatically deployed on a variety of cloud-based execution platforms.
- How to tackle an object detection competition
- Schwert's 6th-place solution on Open Images Challenge 2019
- presented at the lunch workshop of the 26th Symposium on Sensing via Image Information (2020).
Morichetta, A., Casas, P., & Mellia, M. (2019). EXPLAIN-IT: Towards explainable AI for unsupervised network traffic analysis. In Proceedings of the 3rd ACM CoNEXT Workshop on Big DAta, Machine Learning and Artificial Intelligence for Data Communication Networks (pp. 22–28).
Battista Biggio @ MCS 2015, June 29 - July 1, Guenzburg, Germany: "1.5-class ...Pluribus One
Pattern classifiers have been widely used in adversarial settings like spam and malware detection, although they have not been originally designed to cope with intelligent attackers that manipulate data at test time to evade detection.
While a number of adversary-aware learning algorithms have been proposed, they are computationally demanding and aim to counter specific kinds of adversarial data manipulation.
In this work, we overcome these limitations by proposing a multiple classifier system capable of improving security against evasion attacks at test time by learning a decision function that more tightly encloses the legitimate samples in feature space, without significantly compromising accuracy in the absence of attack. Since we combine a set of one-class and two-class classifiers to this end, we name our approach one-and-a-half-class (1.5C) classification. Our proposal is general and it can be used to improve the security of any classifier against evasion attacks at test time, as shown by the reported experiments on spam and malware detection.
Learn about going from 3D scans of core samples and other rock types to visualisation, analysis and model generation with Simpleware. Trials available here: http://www.simpleware.com/software/trial/
A presentation introducing Spectra\'s product family, competitive advantage, CX V3.2\'s solutions, features & benefits, availability, price, collateral & demonstrating its uses.
Application of OpenStreetMap in Disaster Risk ManagementNopphawanTamkuan
This content presents the four procedures were investigated in detail with an emphasis on simplicity for application to disaster management (download from OSM website, download using QGIS plugin, download a file converted to a universal file format (shapefile) and adding rendered map in the background). The use of these data for resilient urban planning are demonstrated including setting a hazard layer (flood Model), setting an exposure layer (population) and exposure analysis using InaSAFE plugin.
This content presents a guide to access satellite (Landsat-8) and microsatellite (Diwata), and how to use gdal and AROSIC (Python-based open-source software) for co-registration.
This content describes Call Detail Records (CDR) data format, data acquisition method, visualize in Mobmap and the applications for disaster management.
Disaster Damage Assessment and Recovery Monitoring Using Night-Time Light on GEENopphawanTamkuan
This content shows the possibility and useful cases of night-time light data to assess disaster damages and recovery in post-disaster situations such as Hokkaido earthquake, dam eruption in Laos and Kerala flood in India. Moreover, how to browse and profiling night-time light on GEE are demonstrated here.
This content presents for basic of Synthetic Aperture Radar (SAR) including its geometry, how the image is created, essential parameters, interpretation, SAR sensor specification, and advantages and disadvantages.
Differential SAR Interferometry Using Sentinel-1 Data for Kumamoto EarthquakeNopphawanTamkuan
This content presents step by step of Differential SAR Interferometry or DInSAR analysis in SNAP. The case study is Kumamoto Earthquake using Sentinel-1.
Earthquake Damage Detection Using SAR Interferometric CoherenceNopphawanTamkuan
This content presents how to apply interferometric analysis for damage detection. The case study is the Kumamoto earthquake in 2016. ALOS-2 images are used to calculate interferometric coherence, and estimate coherence change of images between before- and during earthquake to estimate possible degree of damage areas.
How to better understand SAR, interpret SAR products and realize the limitationsNopphawanTamkuan
This content shows how to better understand SAR (how to interpret SAR images and read SAR interferogram ). Moreover, capacities and limitations of SAR are discussed for each disaster emergency mapping (Flood, Landslide and Earthquake).
This content presents how to detect water or flood areas using ALOS-2 images before and during floods. First, it shows how to calibrate intensity to dB, find threshold value and apply to images.
Differential SAR Interferometry Using ALOS-2 Data for Nepal EarthquakeNopphawanTamkuan
This content presents Differential SAR Interferometry or DInSAR analysis with GMTSAR (on Linux based OS, download DEM, prepare directories for processing). The case study is Nepal earthquake in 2015 using ALOS-2.
This content shows geospatial data sources for Japan and global data, coordinate reference system, and create a map of population density (Vector analysis: dissolve vector, join table, calculate area and population density.
Raster Analysis (Color Composite and Remote Sensing Indices)NopphawanTamkuan
This content shows how to download data from USGS explorer, color composition for Landsat-8 and Sentinel-2, extract specific area, and remote sensing indices (NDVI and NDWI) using raster calculator.
This content provides basic python before starting geospatial analysis. It starts from data type, variable, basic coding, condition statement, loop, while, and how to read file.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
1. Center for Research and Application for Satellite Remote Sensing
Yamaguchi University
Image Classification
2. • The Semi-Automatic Classification Plugin (SCP) is a free open source
plugin for QGIS that allows for the semi-automatic classification (also
supervised and unsupervised classification) of remote sensing images.
Also, it provides several tools for the download of free images (Landsat,
Sentinel-2, Sentinel-3, ASTER, MODIS), the preprocessing of images, the
postprocessing of classifications, and the raster calculation.
Congedo Luca (2016). Semi-Automatic Classification Plugin Documentation.
DOI: http://dx.doi.org/10.13140/RG.2.2.29474.02242/1
Semi-Automatic Classification Plugin
8. • Go to icon of semi-automatic classification plugin → Preprocessing
• Or directly to preprocessing tool
Input
Run → select
directory
to save
Import image and pre-processing
9. • In band set will show image from preprocessing → click “RUN”
In band set will show image from preprocessing
12. Classes (MC ID)
• 1 Water
• 2 Tree and high vegetation
• 3 Low vegetation, grassland
• 4 Building, manmade
• 5 Openland, bareland
• # you can decide your own classes
• In this workshop, we will make just MC class
Optional → you can make sub-classes
SCP allows for the definition of Macroclass ID (i.e. MC
ID) and Class ID (i.e. C ID), which are the identification
codes of land cover classes. A Macroclass is a group of
ROIs having different Class ID, which is useful when one
needs to classify materials that have different spectral
signatures in the same land cover class.
Create ROI
30. There are 3 classification algorithm for this
plugin
1) Minimum distance
2) Maximum Likelihood
3) Spectral Angle Mapping
https://fromgistors.blogspot.com/p/user-manual.html
Select classification method
