DEFINITION :
GIS is a powerful set of tools for collecting, storing , retrieving at will, transforming and displaying spatial data from the real world for a particular set of purposes
APPLICATION AREAS OF GIS
Agriculture
Business
Electric/Gas utilities
Environment
Forestry
Geology
Hydrology
Land-use planning
Local government
Mapping
11. Military
12. Risk management
13. Site planning
14. Transportation
15. Water / Waste water industry
COMPONENTS OF GIS
DATA INPUT
SPATIAL DATA MODEL
Data Model:
It describes in an abstract way how the data is represented in an information system or in DBMS
Spatial Data Model :
The models or abstractions of reality that are intended to have some similarity with selected aspects of the real world
Creation of analogue and digital spatial data sets involves seven levels of model development and abstraction
SPATIAL DATA MODEL
Conceptual model : A view of reality
Analog model : Human conceptualization leads to analogue abstraction
Spatial data models : Formalization of analogue abstractions without any conventions
Database model : How the data are recorded in the computer
Physical computational model : Particular representation of the data structures in computer memory
Data manipulation model : Accepted axioms and rules for handling the data
SPATIAL DATA MODEL
SPATIAL DATA MODEL
Objects on the earth surface are shown as continuous and discrete objects in spatial data models
Types of data models
Raster data model
vector data models
RASTER DATA MODEL
Basic Elements :
Extent
Rows
Columns
Origin
Orientation
Resolution: pixel = grain = grid cell
Ex: Bit Map Image (BMP),Joint Photographic Expert Group (JPEG), Portable Network Graphics(PNG) etc
RASTER DATA MODEL
VECTOR DATA MODEL
Basic Elements:
Location (x,y) or (x,y,z)
Explicit, i.e. pegged to a coordinate system
Different coordinate system (and precision) require different values
o e.g. UTM as integer (but large)
o Lat, long as two floating point numbers +/-
Points are used to build more complex features
Ex: Auto CAD Drawing File(DWG), Data Interchange(exchange) File(DXF), Vector Product Format (VPF) etc
VECTOR DATA MODEL
RASTER vs VECTORRaster is faster but Vector is corrector
TESSELLATIONS OF CONTINUOUS FIELDS
Triangular Irregular Network: (TIN)
TIN is a vector data structure for representing geographical information that is continuous
Digital elevation model
TIN is generally used to create Digital Elevation Model (DEM)
DIGITAL ELEVATION MODEL
DATA STRUCTURES
Data structure tells about how the data is stored
Data organization in raster data structures
Each cell is referenced directly
Each overlay Is referenced directly
Each mapping unit is referenced directly
Each overlay is separate file with general header
DEFINITION :
GIS is a powerful set of tools for collecting, storing , retrieving at will, transforming and displaying spatial data from the real world for a particular set of purposes
APPLICATION AREAS OF GIS
Agriculture
Business
Electric/Gas utilities
Environment
Forestry
Geology
Hydrology
Land-use planning
Local government
Mapping
11. Military
12. Risk management
13. Site planning
14. Transportation
15. Water / Waste water industry
COMPONENTS OF GIS
DATA INPUT
SPATIAL DATA MODEL
Data Model:
It describes in an abstract way how the data is represented in an information system or in DBMS
Spatial Data Model :
The models or abstractions of reality that are intended to have some similarity with selected aspects of the real world
Creation of analogue and digital spatial data sets involves seven levels of model development and abstraction
SPATIAL DATA MODEL
Conceptual model : A view of reality
Analog model : Human conceptualization leads to analogue abstraction
Spatial data models : Formalization of analogue abstractions without any conventions
Database model : How the data are recorded in the computer
Physical computational model : Particular representation of the data structures in computer memory
Data manipulation model : Accepted axioms and rules for handling the data
SPATIAL DATA MODEL
SPATIAL DATA MODEL
Objects on the earth surface are shown as continuous and discrete objects in spatial data models
Types of data models
Raster data model
vector data models
RASTER DATA MODEL
Basic Elements :
Extent
Rows
Columns
Origin
Orientation
Resolution: pixel = grain = grid cell
Ex: Bit Map Image (BMP),Joint Photographic Expert Group (JPEG), Portable Network Graphics(PNG) etc
RASTER DATA MODEL
VECTOR DATA MODEL
Basic Elements:
Location (x,y) or (x,y,z)
Explicit, i.e. pegged to a coordinate system
Different coordinate system (and precision) require different values
o e.g. UTM as integer (but large)
o Lat, long as two floating point numbers +/-
Points are used to build more complex features
Ex: Auto CAD Drawing File(DWG), Data Interchange(exchange) File(DXF), Vector Product Format (VPF) etc
VECTOR DATA MODEL
RASTER vs VECTORRaster is faster but Vector is corrector
TESSELLATIONS OF CONTINUOUS FIELDS
Triangular Irregular Network: (TIN)
TIN is a vector data structure for representing geographical information that is continuous
Digital elevation model
TIN is generally used to create Digital Elevation Model (DEM)
DIGITAL ELEVATION MODEL
DATA STRUCTURES
Data structure tells about how the data is stored
Data organization in raster data structures
Each cell is referenced directly
Each overlay Is referenced directly
Each mapping unit is referenced directly
Each overlay is separate file with general header
An introduction to GIS Data Types. Strengths and weaknesses of raster and vector data are discussed. Also covered is the importance of topology. Concludes with a discussion of the vector-based format of OpenStreetMap data.
This is most benificial for the First year Engineering students.This presentation consists of videos and many applications of GIS. The processes and the other parts of GIS is also nicely explained.
Also known as geospatial data or geographic information it is the data or information that identifies the geographic location of features and boundaries on Earth, such as natural or constructed features, oceans, and more. Spatial data is usually stored as coordinates and topology, and is data that can be mapped.
Gis Geographical Information System FundamentalsUroosa Samman
Gis, Geographical Information System Fundamentals. This presentation includes a complete detail of GIS and GIS Softwares. It will help students of GIS and Environmental Science.
An introduction to GIS Data Types. Strengths and weaknesses of raster and vector data are discussed. Also covered is the importance of topology. Concludes with a discussion of the vector-based format of OpenStreetMap data.
This is most benificial for the First year Engineering students.This presentation consists of videos and many applications of GIS. The processes and the other parts of GIS is also nicely explained.
Also known as geospatial data or geographic information it is the data or information that identifies the geographic location of features and boundaries on Earth, such as natural or constructed features, oceans, and more. Spatial data is usually stored as coordinates and topology, and is data that can be mapped.
Gis Geographical Information System FundamentalsUroosa Samman
Gis, Geographical Information System Fundamentals. This presentation includes a complete detail of GIS and GIS Softwares. It will help students of GIS and Environmental Science.
H2020 Jupiter Project's webinar, hold on February 23rd 2016.
ISMB researchers present the GNSS systems, the status of the European GNSS and the fundamental role of the “local integrity” concept for the successful deployment of the autonomous driving.
Low cost L1 GPS system suitable for PPK systems and precise navigation for Drones. Geomos and other structural monitoring systems. Safety systems where positioning is critical. Autonomous machine control, surface as well as Underground
Express Maps Presentation - Innovative Mapping Service OnlineSpot Image
Spot Image and Infoterra, subsidiaries of EADS Astrium, are launching the World’s first online service at www.express-maps.com to deliver detailed basemaps covering over three quarters of the Earth’s land surfaces. This unique service creates basemaps at a scale of 1:50 000, which can be delivered electronically to users.
GRASS GIS 7 capabilities: a graphical overviewMarkus Neteler
The Geographic Resources Analysis Support System (http://grass.osgeo.org/), commonly referred to as GRASS GIS, is an Open Source Geographic Information System providing powerful raster, vector and geospatial processing capabilities in a single integrated software suite. GRASS GIS includes tools for spatial modeling, visualization of raster and vector data, management and analysis of geospatial data, and the processing of satellite and aerial imagery. It also provides the capability to produce sophisticated presentation graphics and hardcopy maps. GRASS GIS has been translated into about twenty languages and supports a huge array of data formats. It can be used either as a stand-alone application or as backend for other software packages such as QGIS and R geostatistics. It is distributed freely under the terms of the GNU General Public License (GPL). GRASS GIS is a founding member of the Open Source Geospatial Foundation (OSGeo).
Tracking emerging diseases from space: Geoinformatics for human healthMarkus Neteler
European and other countries are at increasing risk for new or re-emerging vector-borne diseases. Among the top ten vector-borne diseases with greatest potential to affect European citizens are Dengue fever, Chikungunya, Hantavirus, and Crimean-Congo hemorrhagic fever. Despite the risk of disease transmission, many vectors like the Asian tiger mosquito or ticks are also a nuisance in daily life. The examination of disease vector spread and a better understanding of spatio-temporal patterns in disease transmission and diffusion is greatly facilitated by Geoinformatics. New methods including the use of high resolution time series from space in spatial models enable us to predict species invasion and survival, and to assess potential health risks. Geoinformatics is able to address the increasing challenge for human and veterinary public health not only in Europe, but across the globe, assisting decision makers and public health authorities to develop surveillance plans and vector control.
Deriving environmental indicators from massive spatial time series using open...Markus Neteler
Geospatial Analytics Forum at North Carolina State University, 4 Sept 2014 - http://geospatial.ncsu.edu/about/geoforum/
See also: http://opensource.com/education/14/9/back-school-grass-gis
GRASS GIS 7: your reliable geospatial number cruncherMarkus Neteler
GRASS GIS (Geographic Resources Analysis Support System) looks back to the longest development history in the FOSS4G community. Having been available for 30 years, a lot of innovation has been put into the new GRASS GIS 7 release. After six years of development it offers a lot of new functionality, e.g. enhanced vector network analysis, voxel processing, a completely new engine for massive time series management, an animation tool for raster and vector map time series, a new graphic image classification tool, a "map swiper" for interactive maps comparison, and major improvements for massive data analysis (see also http://grass.osgeo.org/grass7/). The development was driven by the rapidly increasing demand for robust and modern free analysis tools, especially in terms of massive spatial data processing and processing on high-performance computing systems. With respect to GRASS GIS 6.4 more than 10,000 source code changes have since been made.
Vom Laptop zum Großrechner: Neues in GRASS GIS 7Markus Neteler
GRASS GIS 7 bietet neue Module zur Vektornetzwerk-, Voxelanalyse, Zeitreihenspeicherung und -management, dazu ein Animationstool für Raster-und Vektorkartenzeitreihen, ein graphisches Bildklassifikationtool, "Map Swiper" zum interaktiven Kartenvergleich nebst verbesserter massiver Datenanalyse.
GRASS GIS (Geographic Ressourcen Analysis Support System) blickt mit nun 30 Jahren auf die längste Entwicklungsgeschichte in der FOSSGIS Community zurück. Die stark ansteigende Nachfrage nach robusten und modernen freien Analysewerkzeugen, v.a. im Hinblick auf die heutzutage enormen räumlichen Datenmengen führte 2008 zum Beginn der GRASS GIS 7 Entwicklung. In Bezug auf GRASS GIS 6.4 wurden inzwischen mehr als 10.000 Verbesserungen vorgenommen.
Die Entwicklercommunity hat eine Reihe von neuen Modulen für Vektornetzwerkanalyse, Bildverarbeitung, Voxelanalyse, Zeitreihenspeicherung (Raster, Vektor, Voxel) und eine verbesserte grafische Benutzeroberfläche integriert (http://trac.osgeo.org/grass/wiki/Grass7/NewFeatures). GRASS GIS 7 bietet eine neue Python Schnittstelle, die auf einfache Weise ermöglicht, neue Anwendungen zu erstellen, die leistungsfähig und effizient sind. In der Benutzeroberfläche gibt es nun ein neues Werkzeug für die Animation von Raster-und Vektorkartenzeitreihen, einen verbesserten Georektifier, ein neues Werkzeug zur überwachten Bildklassifikation, einen "map swiper" zum interaktiven Vergleich zweier Karten (z.B. für Katastrophen) und ein visuelles Zeitreihenmanagement.
Darüber hinaus wurde insbesondere die topologische Vektorbibliothek in Bezug auf die Unterstützung von großen Dateien verbessert. Des weiteren gibt es eine Reihe von neuen Analysefunktionen und auch im Raster-/Bildbereich die Unterstützung für massive Datenanalyse. Auch werden nun Projektionen andere Planeten unterstützt. Viele Module wurden in Bezug auf Geschwindigkeit signifikant optimiert. Der Vortrag illustriert die interessantesten Neuerungen und zeigt, wie Benutzer auf einfache Weise auf die kommende GRASS GIS 7 Version migrieren können. Testversionen stehen für alle üblichen Betriebssysteme zur Verfügung (http://grass.osgeo.org/download/software/).
News in GRASS GIS7. Plenary talk at FOSS4G-CEE 2013, RomaniaMarkus Neteler
GRASS GIS, commonly referred to as GRASS (Geographic Resources Analysis Support System), is the free Geographic Information System (GIS) software with the longest record of development as FOSS4G community project. The increasing demand for a robust and modern analytical free GIS led to the start of GRASS GIS 7 development in April 2008. Since GRASS 6 more than 10,000 changes have been implemented with a series of new modules for vector network analysis, image processing, voxel analysis, time series management and improved graphical user interface (http://trac.osgeo.org/grass/wiki/Grass7/NewFeatures). The core system offers a new Python API and large file support for massive data analysis. Many modules have been undergone major optimization also in terms of speed. The presentation will highlight the advantages for users to migrate to the upcoming GRASS GIS 7 release.
From a niche to a global user community: Open Source GIS and OSGeoMarkus Neteler
OGRS 2009: International Opensource Geospatial Research Symposium
www.ogrs2009.org
From a niche to a global user community: Open Source GIS and OSGeo
Markus Neteler
IASMA Research and Innovation Centre
Fondazione Edmund Mach
Environment and Natural Resources Area
GIS and Remote Sensing Unit, Trento, Italy
Web: http://gis.fem-environment.eu/
Email: markus.neteler . iasma.it
Geographical Information Systems (GIS) have evolved from a highly specialized niche to a technology that affects nearly every aspect of our lives, from finding driving directions to managing natural disasters. The masses have discovered geospatial data and technologies through the availability of popular globes; wiki-fied street mapping which was started by a few individuals has grown to weekly mapping parties around the globe. Today almost everybody can create customized maps or overlay GIS data. Current GIS technology covers viewing maps and images on the web, simple and complex spatial analysis, modeling and simulations.
In our presentation we'll present highlights of the last 20 years of Open Source GIS developments. Many projects are born as initiative of individuals when the lack of available software for a specific application is solved by own development and the result is then made available to the public on the Internet for further collaborative development. In the early 80's, the first Open Source GIS (MOSS and GRASS GIS) reached production status followed by the PROJ4 library project, a first crucial library for many Open Source GIS applications. In 1995 the UMN MapServer project was started to implement OGC standard. The second cross-project library GDAL/OGR was born in 1998. While these projects became mature, new applications were started with partially extraordinary success (OpenEV, OSSIM, MapBuilder, PostGIS, Geoserver, Quantum GIS, uDIG, MapGuide Open Source, MapBender, gvSIG, Geonetwork and OpenLayers).
The wealth of available but partially unconnected projects suggested to establish an umbrella foundation to foster source code and knowledge sharing. Hence, in February 2006, the Open Source Geospatial Foundation (OSGeo, www.osgeo.org) has been created to support and promote worldwide use and collaborative development of Open Source geospatial technologies and data. The foundation supports outreach and advocacy activities to promote Open Source concepts. It also builds shared infrastructure for improved cross-project collaboration. OSGeo has been a stimulating force for cooperative developments of sister projects, leveraging each other efforts by developing shared architecture components and expanding interoperability.
To become an OSGeo member, the software project needs to undergo a rigorous review of its source code, development structure and community health. In these community-developed projects a whole “ecosystem” of users, translators, developers, and provides quick support and tested solutions, both for beginners and professionals.
In our opinion, Open Source GIS is an appropriate choice for scientific computing as it is developed in a peer review process. We will show some case studies for GRASS GIS usage in research which illustrates its academic roots especially in environmental applications. This covers analysis of spatio-temporal data sets such as multi-temporal Lidar and remote sensing data including processing of large amounts of geospatial data on a cluster.
GRASS and OSGeo: a framework for archeologyMarkus Neteler
Use of GIS and geospatial data in archeology. Contribution to:
Quarto Workshop Italiano "Open Source, Free Software e Open Format nei processi di ricerca archeologica", Roma, 27 e 28 aprile 2009. Sede centrale del Consiglio Nazionale delle Ricerche (CNR)
http://www.archeo-foss.org/
Abstract:
With the widespread availability of desktop GIS, archaeologists have gained the tools to comprehensively analyze the important spatial component of their data. Initial archaeological use of GIS was (and still is in many instances) for making maps of archaeological sites. Rather quickly GIS became used for predictive modeling of site locations. More recently, viewshed analysis has seen increasing use, in efforts to understand prehistoric perceptions of the landscape.
In the last years, Open Source GIS software evolved to a powerful set of software products which support both scientific as well as common GIS users. In particular, the integration of GIS with image processing capabilities, geospatial data analysis, database management system and Web mapping software enables archaeologists to perform their tasks in a completely free environment. Since 2006, the Open Source Geospatial Foundation (OSGeo) operates as umbrella foundation for Web Mapping, Desktop GIS Applications, Geospatial Libraries, Metadata Catalog as well as the Public Geospatial Data project and the Education and Curriculum project.
In our presentation, we focus on GRASS GIS (http://grass.osgeo.org/) for spatial data analysis and visualization. GRASS is the largest Open Source GIS program currently available. The new version GRASS 6.4.0 is interoperable as it supports all common vector and raster GIS formats. Its capabilities cover raster and volume spatial analysis and modeling, time-series and landscape analysis, image processing, and visualization of 2D and 3D (voxel) raster data. Vector data can be digitized, extracted, extruded to 3D, and vector networks analyzed. Vector data are handled topologically. Vector attributes are stored in internal or externally connected databases. All general GIS tasks like map reprojection, georeferencing, and transformations are available for raster and vector data. The data storage concept of GRASS permits for single as well as multi-user access set up via network file system.
GRASS 6.4.0, the new stable release after more than one year of development and testing, brings a number of exciting enhancements to the GIS. Besides the hundreds of new module features, supported data formats, and language translations. The 6.4.0 release also runs in MS-Windows, a new installer is provided. A new graphical user interface with integrated location wizard and new vector digitizer is also included.
The presentation concludes with a series of applications relevant to archaeology including image processing, Lidar data analysis, fast viewshed analysis and more.
The need of Interoperability in Office and GIS formatsMarkus Neteler
Free GIS and Interoperability: The need of Interoperability in Office and GIS formats
GIS Open Source, interoperabilità e cultura del dato nei SIAT della Pubblica Amministrazione
[GIS Open Source, interoperability and the 'culture of data' in the spatial data warehouses of the Public Administration]
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
The GRASS GIS software (with QGIS) - GIS Seminar
1. The GRASS GIS software GIS Seminar Politecnico di Milano Polo Regionale di Como M. Neteler neteler at osgeo.org http://grass.itc.it ITC-irst, Povo (Trento), Italy (Document revised November 2006)
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4. GRASS GIS Brief Introduction Developed since 1984, always Open Source , since 1999 under GNU GPL Written in C programming language, portable code (multi-OS, 32/64bit) International development team , since 2001 coordinated at ITC-irst GRASS master Web site: http://grass.itc.it GNU/Linux MacOSX MS-Windows iPAQ
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6. Spatial Data Types Supported Spatial Data Types 2D Raster data incl. image processing 3D Voxel data for volumetric data 2D/3D Vector data with topology Multidimensional points data http://grass.itc.it Orthophoto Distances Vector TIN 3D Vector buildings Voxel
7. Raster data model Raster geometry cell matrix with coordinates resolution: cell width / height (can be in kilometers, meters, degree etc.) y resolution x resolution
8. Vector data model Vector geometry types Point Centroid Line Boundary Area (boundary + centroid) face (3D area) [kernel (3D centroid)] [volumes (faces + kernel)] Geometry is true 3D: x, y, z Line Faces not in all GIS! Node Node Vertex Vertex Segment Segment Segment Node Boundary Vertex Vertex Vertex Vertex Centroid Area
9. OGC Simple Features versus Vector Topology Simple Features ... - points, lines, polygons - replicated boundaries for adjacent areas Advantage: - faster computations Disadvantage: - extra work for data maintenance - in this example the duplicated boundaries are causing troubles Switzerland slivers Map generalized with Douglas-Peucker algorithm in non-topological GIS gaps (Latitude-longitude)
10. OGC Simple Features versus Vector Topology ... versus Vector Topology - points, centroids, lines, boundaries - in topology centroid and boundary form an area - single boundaries for adjacent areas Advantage: - less maintenance, high quality Disadvantage: - slower computations Switzerland Original Pruned each boundary is a single line, divided by two polygons (UTM32N projection) Map generalized with v.clean “prune” algorithm in topological GIS GRASS
11. Italy: Gauss-Boaga Coordinate System Gauss-Boaga Transverse Mercator projection 2 zones ( fuso Ovest, Est ) with a width of 6º30' longitude Each zone is an own projection! False easting: Fuso Ovest: 1500000m (1500km) Fuso Est: 2520000m (2520km) False northing: 0m Scale along meridian: 0.9996 – secante case, not tangent case Ellipsoid: international (Hayford 1909, also called International 1924) Geodetic datum : Rome 1940 (3 national datums; local datums to buy from IGM). National datum values available at: http://crs.bkg.bund.de/crs-eu/
17. WebGIS: Integration of data sources GRASS in the Web Real-time monitoring of Earthquakes (provided in Web by USGS) with GRASS/PHP: http://grass.itc.it/spearfish/php_grass_earthquakes.php
23. Spearfish Sample Dataset Spearfish (SD) sample data location Maps: raster, vector and point data covering two 1:24000 topographic maps (quadrangles Spearfish and Deadwood North) UTM zone 13N, transverse mercator projection, Clarke66 ellipsoid, NAD27 datum, metric units, boundary coordinates: 4928000N, 4914000S, 590000W, 609000E DATA download: http://mpa.itc.it/markus/osg05/ SD Spearfish
24. Practical GIS Usage Start a “terminal” to enter commands Start GRASS 6 within the terminal: grass61 -help grass61 -gui 1. 2. 3.
25. GRASS user interface: QGIS Start QGIS within GRASS terminal: qgis http://qgis.org GRASS Toolbar
26. QGIS: further key functionality Creating a paper map GRASS toolbox GRASS raster maps GRASS vector maps GRASS vector digitizer
27. New GRASS user interface: QGIS Excercise: Please reproduce this map view! Raster: - elevation.dem - aspect Vector: - roads - fields
28. QGIS map composer: prepare map with layout Creating a paper map for printing or saving into a file (SVG, PNG, Postscript) Transfer map view into map composer (printer symbol)
31. QGIS-GRASS Exercises: Noise impact 1/4 1) Simple noise impact map: Extract interstate (highway) from roads vector map into new map and buffer interstate for 3km in each direction GRASS commands: a) first look at the table to get column name and ID of interstate: v.db.select roads b) we extract only 'interstate' (cat = 1, cat is the GRASS standard column name for ID): v.extract in=roads out=interstate where=”cat = 1” c) we buffer the interstate (give buffer in map units which is meters here): v.buffer interstate out=interstate_buf3000 buffer=3000
32. QGIS-GRASS Exercises: Noise impact 2/4 2) Verify affected areas: Look at landcover.30m raster map, overlay extracted interstate and overlay buffered interstate_buf3000 (use transparency to make it nice)
33. # set current region to landcover map, '-p' prints the settings: g.region rast= landcover.30m -p Info: Command line versus graphical user interface On the next slide we either use the following command line: or these settings in the graphical user interface:
34. QGIS-GRASS Exercises: Noise impact 3/4 How to get statistics on influenced landcover-landuse units? -> needs generalization of original landcover.30m map (originates from satellite map) Approach 1: Raster based generalization : “mode” operator in moving window # set current region to landcover map, '-p' prints the settings: g.region rast= landcover.30m -p r.neighbors in= landcover.30m out= landcover.smooth method= mode size= 3 3x3 moving window
35. QGIS-GRASS Exercises: Noise impact 4/4 ... Generalization cont'ed: Approach 2: Vector based generalization : “rmarea” tool: merges small areas into bigger a. # zoom to map: g.region rast= landcover.30m -p # raster to vector conversion: r.to.vect in= landcover.30m out= landcover_30m f=area # filter perimeter of 3x3 pixels ( threshold=(30 * 3)^2 = 8100) v.clean in= landcover_30m out= landcover_30m_gen tool= rmarea thresh= 8100
38. GRASS: Geographic Resources Analysis Support System Example for Location and Mapsets /home/user/grassdata /europa /hannover /world hist dbln coor sidx topo Mapset Location /PERMANENT GRASS Database /prov_trentino /PERMANENT /trento Geometry and attribute data streets parks lakes poi streets.dbf parks.dbf poi.dbf lakes.dbf fcell hist colr cell_misc cellhd cell cats vector dbf /silvia
39. Raster map analysis DEM analysis Raster map algebra Geocoding of scanned map Volume data processing
40. GRASS Command Classes d.* display graphical output (screen) r.* raster raster data processing r3.* raster3D raster voxel 3D data processing i.* imagery image processing v.* vector vector data processing g.* general general file operations (copy, rename of maps, ...) m.* misc miscellaneous commands ps.* postscript map creation in Postscript format Prefix Class Functionality
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42. Raster data analysis: Geomorphology DEM: r.param.scale # set region/resolution to the input map: g.region rast=elevation.10m -p # generalize with size parameter r.param.scale elevation.10m out=morph param=feature size=25 # with legend d.rast.leg morph # view with aspect/shade map (or QGIS) d.his h=morph i=aspect.10m Spearfish DEM: 10m Moving window size: 25x25 nviz elev=elevation.10m col=morph
43. Raster data analysis: Water flows - Contributing area Topographic Index: ln(a/tan(beta)) g.region rast=elevation.10m -p r.topidx in=elevation.10m out=ln_a_tanB d.rast ln_a_tanB d.vect streams col=yellow # ... the old vector stream map nicely deviates from the newer USGS DEM nviz elevation.10m col=ln_a_tanB
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47. Working with vector data Vector map import Attribute management Buffering Extractions, selections, clipping, unions, intersections Conversion raster-vector and vice verse Digitizing in GRASS and QGIS Working with vector geometry
48. GRASS 6 Vector data Vector geometry types Point Centroid Line Boundary Area (boundary + centroid) face (3D area) [kernel (3D centroid)] [volumes (faces + kernel)] Geometry is true 3D: x, y, z Line Faces Node Node Vertex Vertex Segment Segment Segment Node Boundary Vertex Vertex Vertex Vertex Centroid Area
49. Raster-Vector conversion – extraction 1/2 Extraction of residential areas from raster landuse map # set current region to map; look at the landuse/landcover map with legend: g.region rast=landcover.30m -p d.erase d.rast.leg -n landcover.30m # Automated vectorization of the landuse/landcover map: r.to.vect -s landcover.30m out=landcover30m feature=area # see attribute table ('-p' prints the current connection between vector # geometry and attribute table – note that GRASS can link to various DBMS): v.db.connect -p landcover30m # ... will tell you that it is a DBF table v.db.select landcover30m
50. Raster-Vector conversion – extraction 2/2 Extraction of residential areas from raster landuse map # generate list of unique landuse/landcover types from text legend output: v.db.select landcover30m | sort -t '|' -k2 -n -u #display selected categories: d.erase d.vect landcover30m where="value=21 or value=22" fcol=orange # Extract residential area into a new vector map: v.extract landcover30m out=residential where="value=21 or value=22" d.frame -e d.vect residential fcol=orange type=area d.vect roads d.barscale -mt This pipe '|' character is a nice way of combining Unix commands. The output of the first command is sent into the second and so forth... sort is here sorting by second column on numbers (-n) and extracts unique (-u) rows only
56. Vector network analysis methods Vector network with one way roads Generic vector directions One attribute column for each direction Value -1 closes direction (for one way streets) drawn in ps.map Street direction open closed
57. Vector networking Shortest path with d.path d.vect roads d.path roads # or: # v.net.path Further vector network exercises: http://mpa.itc.it/corso_dit2004/grass04_4_vector_network_neteler.pdf
58. Working with own data - Import/Export/Creating Locations Import of LANDSAT-7 data Creating a new location external data files Creating from EPSG code/interactively a new location http://mpa.itc.it/markus/mum3/
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62. Image processing Image classification Image fusion with Brovey transform Natural color composites Calculating a degree Celsius map from the LANDSAT thermal channel
63. Import of LANDSAT-7 Erdas/Img Unsupervised & Supervised Image Classification classification methods in GRASS: all image data must be first listed in a group ( i.group ) See handout for unsupervised classification example Image Classification radiometric, radiometric, supervised radio- and geometric unsupervised supervised Preprocessing i.cluster i.class (monitor) i.gensig (maps) i.gensigset (maps) Computation i.maxlik i.maxlik i.maxlik i.smap
65. Image fusion: Brovey transform We use the earlier imported LANDSAT-7 scene to perform image fusion of the channels 2 (red), 4 (NIR), and 5 (MIR): g.region -dp i.fusion.brovey -l ms1=tm.2 ms2=tm.4 ms3=tm.5 pan=pan out=brovey # zoom to fused channel g.region -p rast=brovey.red # color composite: r.composite r=brovey.red g=brovey.green b=brovey.blue n out=tm.brovey d.rast tm.brovey nviz elevation.10m col=tm.brovey # Increase visual resolution in NVIZ # with Panel -> Surface # -> Polygon resolution # (lower! the value)
66. Natural color composites: LANDSAT-7 RGB The i.landsat.rgb script performs a histogram-area based color optimization: http://plantsci.sdstate.edu/woodardh/Soils_and_Ag/ Black_Hills/Soil_Characteristics_Profiles/landscape_pine.htm Photo: H.J. Woodard, SD Stae Univ. Standard RGB Enhanced RGB
67. TM61: Conversion of temperature first to Kelvin, then to degree Celsius g.region rast=tm6.1 -p #DN: digital numbers (coded temperatures) r.info -r tm6.1 min=131 max=175 # Conversion of DN to spectral radiances: r.mapcalc "tm61rad=((17.04 - 0.)/(255. - 1.))*(tm6.1 - 1.) + 0." r.info -r tm61rad min=8.721260 max=11.673071 # Conversion of spectral radiances to absolute temperatures (Kelvin): # T = K2/ln(K1/L_l + 1)) r.mapcalc "temp_kelvin=1260.56/(log (607.76/tm61rad + 1.0))" r.info -r temp_kelvin min=296.026722 max=317.399879 Recalibrating the LANDSAT-7 thermal channel 1/2
68. TM61: ... conversion to degree Celsius # We currently have the land surface temperature map in Kelvin. # Conversion to degree Celsius: r.mapcalc "temp_celsius=temp_kelvin - 273.15" r.info -r temp_celsius min=22.876722 max=44.249879 # New color table: r.colors temp_celsius col=rules << EOF -10 blue 15 green 25 yellow 35 red 50 brown EOF d.rast.leg temp_celsius g.region rast=elevation.dem -p nviz elevation.dem col=temp_celsius Recalibrating the LANDSAT-7 thermal channel 2/2
70. R-stats is a powerful statistical language Spatial extentions available for all kinds of geostatistics, spatial pattern analysis, time series etc Interface to exchange raster and point data between GRASS and R-stats Rdbi: connects R-stats to PostgreSQL PostgreSQL Spatial data Tables Geostatistics Predictive Models http://www.r-project.org http://grass.itc.it/statsgrass/ GRASS/R-stats interface - R-stats/PostgreSQL interface
74. GRASS: User map Who is using GRASS? AMTI/NASA Ames Research Center USA Austrian Institute for Avalanche and Torrent Research Bank of America Bombardier Aerospace Canada Brenner Railway Austria BR-NetProduction (Bavarian Television) Germany Canadian Forest Service CEA Monte Bondone Census USA CERN Switzerland CICESE Mexico CNR Italia Colorado State University Comune di Prato, Italy Comune Milano, Italy Comune Modena, Italy Comune di Torino, Italy Cornell University USA CSIRO Australia Deutsche Bank Germany DLR Germany Dubai Municipality DuPont Spain EDF France Ericsson Sweden ETH Zuerich Switzerland FED USA Finnish Meteorological Institute Forschungszentrum Juelich Germany Forschungszentrum Karlsruhe Germany GFZ Potsdam Germany Global Environmental Technology Nigeria Limited Graz Technical University Austria Harvard University Hokkaido University HPCC NECTEC Bangkok Thailand Iceland Forest Service Iceland Inst.of Earthquake Engineering & Seismology (ITSAK) Greece ISMAA - Centro Agrometeorologico, Istituto Agrario San Michele JPL NASA JSC NASA Purdue University Qualcomm USA Regione Toscana Rutgers University Sevilla University Spain South African Weather Bureau (METSYS) Stockholm Environment Institute-Boston Teledetection France Telefónica Spain TU Berlin TU Muenchen UC Davis UFRGS Brasilia University of Costa Rica University of Sydney University of Toronto Canada University of Trento, Italy US Army US Bureau of Reclamation US Dep. of Agriculture VA Linux Systems USA Landesmuseum Linz Austria La Poste France Lawrence Laboratories USA Lockheed Martin Space USA Los Alamos National Laboratory Meteo Poland MIT Lincoln Laboratory Nanjing University National Botanic Garden of Belgium National Museum Japan National Radio Astronomy Observatory USA National Research Center of Soils USA NCSA Illinois USA NCSU USA NIMA USA NOAA USA (GLOBE DEM generated with GRASS) NRSA USA Onera France (running SPOT etc.) Politecnico di Milano Politecnico di Torino Princeton University Procergs Brasilia
75. New OSGeo Foundation: Proposed founding projects Founded 4 th February 2006, Chicago http://www.osgeo.org GRASS GIS
77. Capacity building Communities growing together... GRASS GIS Spatial Computing http:// grass.itc.it GDAL - Geospatial Data Abstraction Library http://www.gdal.org ... AND MANY OTHERS! http://www.osgeo.org (General) statistical computing environment: http://www.r-project.org / Rgeo: spatial data analysis in R, unified classes and interfaces (e.g, RGRASS) http://r-spatial.sourceforge.net/ QGIS: user friendly Open Source GIS http://www.qgis.org Spatially-enabled Internet applications http:// mapserver.gis.umn.edu / PostGIS : support for geographic objects to the PostgreSQL object-relational database http://postgis.refractions.net PostgreSQL Most advanced open source relational database http://www.postgresql.org/
78. Closure of the Seminar Thanks for your interest and your attention! M. Neteler neteler at osgeo.org http://grass.itc.it ITC-irst, Povo (Trento), Italy