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
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.
Mapping fire: Can spatially explicit criteria and indicators be developed?CIFOR-ICRAF
Presented by Solichin Manuri, Senior Advisor at Diameter Consulting, Bogor, Indonesia, at "Online Webinar 2: Biophysical Attributes and Peatland Fires", on 14 October 2020
In this session the speaker shared information on mapping fire (extent and occurrences) in tropical peatlands including in Indonesia. This session also shared insights on the existing methods that can be used for fire mapping and comparisons. This session also emphasized that spatial explicit criteria for fire should be developed depending on the method and data used.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Chronological Calibration Methods for Landsat Satellite Images iosrjce
IOSR Journal of Applied Physics (IOSR-JAP) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of physics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in applied physics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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.
Mapping fire: Can spatially explicit criteria and indicators be developed?CIFOR-ICRAF
Presented by Solichin Manuri, Senior Advisor at Diameter Consulting, Bogor, Indonesia, at "Online Webinar 2: Biophysical Attributes and Peatland Fires", on 14 October 2020
In this session the speaker shared information on mapping fire (extent and occurrences) in tropical peatlands including in Indonesia. This session also shared insights on the existing methods that can be used for fire mapping and comparisons. This session also emphasized that spatial explicit criteria for fire should be developed depending on the method and data used.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Chronological Calibration Methods for Landsat Satellite Images iosrjce
IOSR Journal of Applied Physics (IOSR-JAP) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of physics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in applied physics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+,
and EO-1 ALI sensors
Gyanesh Chander a,⁎, Brian L. Markham b, Dennis L. Helder c
a SGT, Inc. 1 contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198-0001, USA
b National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, USA
c South Dakota State University (SDSU), Brookings, SD 57007, USA
Estimation of land surface temperature of dindigul district using landsat 8 dataeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Remote Sensing Methods for operational ET determinations in the NENA region, ...NENAwaterscarcity
Workshop on Operationalizing the Regional Collaborative Platform to Address ‘Water Consumption, Water Productivity and Drought Management’ in Agriculture, 27 - 29 October 2015, Cairo, Egypt
Assessment of Landsat 8 TIRS data capability for the preliminary study of geo...TELKOMNIKA JOURNAL
West Sumatra is one of has big geothermal energy resources potential. Remote sensing technology can have a role in geothermal exploration activity to measure the distribution of land surface temperatures (LST) and predict the geothermal potential area. Main study to obtain the assessment of Landsat 8 TIRS (Landsat`s Thermal Infrared Sensor) data capability for geothermal energy resources estimation. Mono-window algorithms were used to generate the LST maps. Data set was combined with a digital elevation model (DEM) to identify the potential geothermal energy based on the variation in surface temperature. The result that were derived from LST map of West Sumatra shows that ranged from -8.6 C0 to 32.59 C0 and the different temperatures are represented by a graduated pink to brown shading. A calculated result clearly identifies the hot areas in the dataset, which are brown in colour images. Lima Puluh Kota, Tanah Datar, Solok, and South Solok areas showed the high-temperature value (Brown) in the range of 28.1 C0 to 32.59 C0 color in images which means that they possess high potential for generating thermal energy. In contrast, the temperatures were lower (Pink) in the north-eastern areas and the range distribution was from-8.5 C0 to 5 C0.
APPLICATION OF REMOTE SENSING AND GIS IN AGRICULTURELagnajeetRoy
India is a country that depends on agriculture. Today in this era of technological supremacy, agriculture is also using different new technologies like some robotic machinery to remote sensing and Geographical Information System (GIS) for the betterment of agriculture. It is easy to get the information about that area where human cannot check the condition everyday and help in gathering the data with the help of remote sensing. Whereas GIS helps in preparation of map that shows an accurate representation of data we get through remote sensing. From disease estimation to stress factor due to water, from ground water quality index to acreage estimation in various way agriculture is being profited by the application of remote sensing and GIS in agriculture. The applications of those software or techniques are very new to the agriculture domain still much more exploration is needed in this part. New software’s are developing in different parts of the world and remote sensing. Today farmers understand the beneficiaries of these kinds of techniques to the farm field which help in increasing productivity that will help future generation as technology is hype in traditional system of farming.
Comparing canopy density measurement from UAV and hemispherical photography: ...IJECEIAES
UAV and hemispherical photography are common methods used in canopy density measurement. These two methods have opposite viewing angles where hemispherical photography measures canopy density upwardly, while UAV captures images downwardly. This study aims to analyze and compare both methods to be used as the input data for canopy density estimation when linked with a lower spatial resolution of remote sensing data i.e. Landsat image. We correlated the field data of canopy density with vegetation indices (NDVI, MSAVI, and AFRI) from Landsat-8. The canopy density values measured from UAV and hemispherical photography displayed a strong relationship with 0.706 coefficient of correlation. Further results showed that both measurements can be used in canopy density estimation using satellite imagery based on their high correlations with Landsat-based vegetation indices. The highest correlation from downward and upward measurement appeared when linked with NDVI with a correlation of 0.962 and 0.652, respectively. Downward measurement using UAV exhibited a higher relationship compared to hemispherical photography. The strong correlation between UAV data and Landsat data is because both are captured from the vertical direction, and 30 m pixel of Landsat is a downscaled image of the aerial photograph. Moreover, field data collection can be easily conducted by deploying drone to cover inaccessible sample plots.
Introduction -Remote means – far away ; Sensing means – believing or observing or acquiring some information.
Remote sensing means acquiring information of things from a distance with sensors. (without touching the things)
Sensors are like simple cameras except that they not only use visible light but also other bands of the electromagnetic spectrum such as infrared, microwaves and ultraviolet regions.
Distance of Remote Sensing, Definition of remote sensing - Remote Sensing is:
“The art and science of obtaining information about an object without being in direct contact with the object” (Jensen 2000).
India’s National Remote Sensing Agency (NRSA) defined as : “Remote sensing is the technique of deriving information about objects on the surface of the earth without physically coming into contact with them.”
Remote Sensing Process, - (A) Energy Source or Illumination.
(B) Radiation and the Atmosphere.
(C) Interaction with the Target.
(D) Recording of Energy by the Sensor.
(E) Transmission, Reception, & Processing.
(F) Interpretation and Analysis.
(G) Application.
Remote sensing platforms , History of Remote Sensing, Applications of remote sensing - In Agriculture, In Geology, Applications of National Priority.
Development of a Java-based application for environmental remote sensing data...IJECEIAES
Air pollution is one of the most serious problems the world faces today. It is highly necessary to monitor pollutants in real-time to anticipate and reduce damages caused in several fields of activities. Likewise, it is necessary to provide decision makers with useful and updated environmental data. As a solution to a part of the above-mentioned necessities, we developed a Java-based application software to collect, process and visualize several environmental and pollution data, acquired from the Mediterranean Dialog earth Observatory (MDEO) platform [1]. This application will amass data of Morocco area from EUMETSAT satellites, and will decompress, filter and classify the received datasets. Then we will use the processed data to build an interactive environmental real-time map of Morocco. This should help finding out potential correlations between pollutants and emitting sources.
Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+,
and EO-1 ALI sensors
Gyanesh Chander a,⁎, Brian L. Markham b, Dennis L. Helder c
a SGT, Inc. 1 contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198-0001, USA
b National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), Greenbelt, MD 20771, USA
c South Dakota State University (SDSU), Brookings, SD 57007, USA
Estimation of land surface temperature of dindigul district using landsat 8 dataeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Remote Sensing Methods for operational ET determinations in the NENA region, ...NENAwaterscarcity
Workshop on Operationalizing the Regional Collaborative Platform to Address ‘Water Consumption, Water Productivity and Drought Management’ in Agriculture, 27 - 29 October 2015, Cairo, Egypt
Assessment of Landsat 8 TIRS data capability for the preliminary study of geo...TELKOMNIKA JOURNAL
West Sumatra is one of has big geothermal energy resources potential. Remote sensing technology can have a role in geothermal exploration activity to measure the distribution of land surface temperatures (LST) and predict the geothermal potential area. Main study to obtain the assessment of Landsat 8 TIRS (Landsat`s Thermal Infrared Sensor) data capability for geothermal energy resources estimation. Mono-window algorithms were used to generate the LST maps. Data set was combined with a digital elevation model (DEM) to identify the potential geothermal energy based on the variation in surface temperature. The result that were derived from LST map of West Sumatra shows that ranged from -8.6 C0 to 32.59 C0 and the different temperatures are represented by a graduated pink to brown shading. A calculated result clearly identifies the hot areas in the dataset, which are brown in colour images. Lima Puluh Kota, Tanah Datar, Solok, and South Solok areas showed the high-temperature value (Brown) in the range of 28.1 C0 to 32.59 C0 color in images which means that they possess high potential for generating thermal energy. In contrast, the temperatures were lower (Pink) in the north-eastern areas and the range distribution was from-8.5 C0 to 5 C0.
APPLICATION OF REMOTE SENSING AND GIS IN AGRICULTURELagnajeetRoy
India is a country that depends on agriculture. Today in this era of technological supremacy, agriculture is also using different new technologies like some robotic machinery to remote sensing and Geographical Information System (GIS) for the betterment of agriculture. It is easy to get the information about that area where human cannot check the condition everyday and help in gathering the data with the help of remote sensing. Whereas GIS helps in preparation of map that shows an accurate representation of data we get through remote sensing. From disease estimation to stress factor due to water, from ground water quality index to acreage estimation in various way agriculture is being profited by the application of remote sensing and GIS in agriculture. The applications of those software or techniques are very new to the agriculture domain still much more exploration is needed in this part. New software’s are developing in different parts of the world and remote sensing. Today farmers understand the beneficiaries of these kinds of techniques to the farm field which help in increasing productivity that will help future generation as technology is hype in traditional system of farming.
Comparing canopy density measurement from UAV and hemispherical photography: ...IJECEIAES
UAV and hemispherical photography are common methods used in canopy density measurement. These two methods have opposite viewing angles where hemispherical photography measures canopy density upwardly, while UAV captures images downwardly. This study aims to analyze and compare both methods to be used as the input data for canopy density estimation when linked with a lower spatial resolution of remote sensing data i.e. Landsat image. We correlated the field data of canopy density with vegetation indices (NDVI, MSAVI, and AFRI) from Landsat-8. The canopy density values measured from UAV and hemispherical photography displayed a strong relationship with 0.706 coefficient of correlation. Further results showed that both measurements can be used in canopy density estimation using satellite imagery based on their high correlations with Landsat-based vegetation indices. The highest correlation from downward and upward measurement appeared when linked with NDVI with a correlation of 0.962 and 0.652, respectively. Downward measurement using UAV exhibited a higher relationship compared to hemispherical photography. The strong correlation between UAV data and Landsat data is because both are captured from the vertical direction, and 30 m pixel of Landsat is a downscaled image of the aerial photograph. Moreover, field data collection can be easily conducted by deploying drone to cover inaccessible sample plots.
Introduction -Remote means – far away ; Sensing means – believing or observing or acquiring some information.
Remote sensing means acquiring information of things from a distance with sensors. (without touching the things)
Sensors are like simple cameras except that they not only use visible light but also other bands of the electromagnetic spectrum such as infrared, microwaves and ultraviolet regions.
Distance of Remote Sensing, Definition of remote sensing - Remote Sensing is:
“The art and science of obtaining information about an object without being in direct contact with the object” (Jensen 2000).
India’s National Remote Sensing Agency (NRSA) defined as : “Remote sensing is the technique of deriving information about objects on the surface of the earth without physically coming into contact with them.”
Remote Sensing Process, - (A) Energy Source or Illumination.
(B) Radiation and the Atmosphere.
(C) Interaction with the Target.
(D) Recording of Energy by the Sensor.
(E) Transmission, Reception, & Processing.
(F) Interpretation and Analysis.
(G) Application.
Remote sensing platforms , History of Remote Sensing, Applications of remote sensing - In Agriculture, In Geology, Applications of National Priority.
Development of a Java-based application for environmental remote sensing data...IJECEIAES
Air pollution is one of the most serious problems the world faces today. It is highly necessary to monitor pollutants in real-time to anticipate and reduce damages caused in several fields of activities. Likewise, it is necessary to provide decision makers with useful and updated environmental data. As a solution to a part of the above-mentioned necessities, we developed a Java-based application software to collect, process and visualize several environmental and pollution data, acquired from the Mediterranean Dialog earth Observatory (MDEO) platform [1]. This application will amass data of Morocco area from EUMETSAT satellites, and will decompress, filter and classify the received datasets. Then we will use the processed data to build an interactive environmental real-time map of Morocco. This should help finding out potential correlations between pollutants and emitting sources.
ESA SMOS (Soil Moisture and Ocean Salinity) Mission: Principles of Operation ...adrianocamps
SMOS basic principles and description of some products developed at the SMOS Barcelona Expert Center.
Disclaimer: these materials were prepared for Eduacational purposes only.
Big data and remote sensing: A new software of ingestion IJECEIAES
Currently, remote sensing is widely used in environmental monitoring applications, mostly air quality mapping and climate change supervision. However, satellite sensors occur massive volumes of data in near-real-time, stored in multiple formats and are provided with high velocity and variety. Besides, the processing of satellite big data is challenging. Thus, this study aims to approve that satellite data are big data and proposes a new big data architecture for satellite data processing. The developed software is enabling an efficient remote sensing big data ingestion and preprocessing. As a result, the experiment results show that 86 percent of the unnecessary daily files are discarded with a data cleansing of 20 percent of the erroneous and inaccurate plots. The final output is integrated into the Hadoop system, especially the HDFS, HBase, and Hive, for extra calculation and processing.
Building Climate Resilience: Translating Climate Data into Risk Assessments Safe Software
Climate change affects us all. It is an urgent issue that requires practical solutions to mitigate its impacts. Data is at the center of understanding this challenge. In this informative webinar, we will explore how data can be leveraged to translate climate change projections into tangible hazard and risk assessments at the local level.
The webinar will cover a range of topics: including flood, fire, heat, drought, population health, and critical infrastructure, among others. We will also highlight our partner and customer experiences in this field and present key results from our participation in recent OGC pilots on Climate Resilience and Disaster Response. We will also be joined by special guests sharing their experience in the AgriTech sector, where gathering metrics and data from sensors is helping to reduce the demand from farming on precious resources like water for irrigation.
Through live demos, attendees will gain practical knowledge in accessing climate services from USGS & Environment Canada and how to convert climate model NetCDF outputs into more GIS-friendly formats like geodatabase & GeoJSON.
Finally, we will address the significant gaps and challenges that remain in assessing climate-related hazards and risks, and explore how FME can play a critical role in addressing these gaps. Join us for this important discussion on how you can use FME to build resilience and mitigate the impacts of climate change.
DSD-SEA 2023 Global to local multi-hazard forecasting - YanDeltares
Presentation by Kun Yan (Deltares) at the Seminar Models and decision-making in the wake of climate uncertainties, during the Deltares Software Days South-East Asia 2023. Wednesday, 22 February 2023, Singapore.
Summary: The province of Mendoza can administrate water using digital tools that are used for the Science of Earth, and that way to optimize the use of resource, with an intrinsic impact on Economic Science, it is said projections on its productive array. Thus, early development of abilities on this kind of tools that takes part of the so-called Administration 4.0, allows to the professional future of Economic Science and more specifically to the Public Administrators, being more competitive, keeping up online with the new demands that visualize by the digital revolution that are undertaking.
This preview presents a summary of four selected research on remote sensing drought assessment and impacts at both the regional and global levels as part of the course requirement for remote sensing for global environmental change. The papers are presented by Richard MacLean, graduate student in Geographic Information Systems for Development and Environment and Jenkins Macedo, graduate student in Environmental Science and Policy.
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).
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]
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.