The document discusses an automated method for classifying earth surfaces to assess landslide susceptibility using topographic data. It examines different geomorphometric classification approaches and parameters to distinguish landscape types related to landslides. The study tests supervised and unsupervised classification methods, compares results, and develops an integrated method using slope gradient, convexity and texture that identifies terrain features correlated with past landslide events. The automated integrated classification approach provides a useful tool for landslide susceptibility analysis at a territorial scale.
Comparison among Height Observation of GPS, Total Station and Level and their...IRJET Journal
This document compares the accuracy of GPS, total station, and level instruments for measuring elevation in mining works by using GIS technology. Statistical analysis showed the level measurements had the lowest variation while GPS had the highest. Topographic maps were created from observations from each instrument, showing they produced similar overall elevation patterns. The document concludes that while GPS and total station measurements have some error, their accuracy is sufficient for mining works. GIS allows easy analysis and use of elevation data from any of the three instruments.
This document describes a surveying project conducted by a group of students from the Department of Civil and Geomatics Engineering at Tribhuwan University. It provides an overview of the objectives and methodology for conducting topographic surveying, bridge site surveying, road alignment surveying, and an introduction to geographic information systems. The group's project involved topographic mapping, bridge site data collection, horizontal and vertical road alignment design, and cross-section surveying at a field site along the Kali Khola river.
Tarımsal Toprak Haritalama'da Jeofizik MühendisliğiAli Osman Öncel
1) The document discusses a study evaluating the reliability and reproducibility of electromagnetic induction (EMI) data collection.
2) The study compared data from two identical EMI instruments, the calibration methods of different individuals, and variations in calibration height.
3) The results showed significant differences between instruments, calibrations, and heights. This demonstrates the need for standardization of EMI data collection procedures to ensure reliable and reproducible data.
Performances evaluation of surface water areas extraction techniques using l...Abdelazim Negm
This presentation was presented at:
9th International Conference Interdisciplinarity in Engineering, INTER-ENG 2015, 8-9 October 2015, Tirgu-Mures, Romania
The complete paper will be published in Procedia Technology Journal soon.
Remote Exploration Technique. Dr. V. GalkineVadim Galkine
Evaluation of the accumulated permeability field of the uppermost crust using analogue modeling and lineament analysis combination.
Method results in building a series of Exploration Target Maps for further ground exploration. Unique on the market. Low cost, fast, effective. Base Metals, Gold, Silver, Uranium, Oil and gas, Kimberlites.
Spatial interpolation techniques are used to estimate values at unsampled locations based on known sample points. There are two main types of interpolation methods: global methods which apply a single mathematical function to all points, and local methods which apply functions to subsets of points. Specific interpolation methods covered include Thiessen polygons, triangulated irregular networks, spatial moving average, trend surfaces, inverse distance weighting, and Kriging. Kriging is similar to inverse distance weighting but uses minimum variance to calculate weights.
Triangulation, trilateration, traverse, leveling, and radiation are the five most common land survey techniques. Traverse surveying involves measuring distances and lengths to connect points and determine locations, making it suitable for preliminary surveys and navigating around obstacles. Given the existing buildings on the site, traverse surveying would allow connecting points to survey the site while working around structures.
This document describes a new robust fixed rank kriging (R-FRK) method for improving the spatial completeness and accuracy of satellite sea surface temperature (SST) products. The R-FRK method addresses two key issues: 1) it allows for dimension reduction kriging to be applied to satellite SST data over irregular regions, and 2) it incorporates a data-driven bias correction model to address systematic biases in the satellite SST measurements. The method is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) SST data from 2003 and 2010. Validation using drifting buoy observations shows the method produces spatially complete SST fields with high accuracy.
Assessing 50 years of tropical Peruvian glacier volume change from multitempo...InfoAndina CONDESAN
This study assessed 50 years of glacier volume change in four glaciers in the Cordillera Blanca mountain range in Peru using digital elevation models from 1962 aerial photos and 2008 LiDAR data. The key findings were:
1) Surface area of the four glaciers decreased significantly between 1962 and 2008, with losses ranging from 31.2% to 85.7%.
2) Glacier volume also decreased substantially over this period. Total volume losses for the four glaciers ranged from 0.013 km3 to 0.137 km3.
3) To account for inaccuracies between the different elevation data sources, the 2008 LiDAR DEM was compared to the 1962 aerial photos over non-glacierized terrain
This document provides an overview of geographic information system (GIS) analysis functions. It discusses several types of analysis that GIS is used for, including selection and measurement, overlay analysis, neighbourhood operations, and connectivity analysis. Overlay analysis allows for spatially interrelating multiple data layers and is one of the most important GIS functions. Neighbourhood operations consider characteristics of surrounding areas, such as through buffering or interpolation. Overall, the document outlines the key spatial analysis techniques that GIS provides for examining geographic data patterns and relationships.
A Simplified and Robust Surface Reflectance Estimation Method (SREM) for Use ...Muhammad Bilal
The document describes a new Simplified and Robust Surface Reflectance Estimation Method (SREM) for estimating surface reflectance from multi-sensor remote sensing data. SREM is based on equations from the widely-used 6S radiative transfer model but does not require inputs on aerosols and atmospheric gases. The method is tested using Landsat 5, 7, and 8 data from 2000-2018. Results show SREM produces surface reflectance with high correlation and low errors compared to in situ measurements and other Landsat surface reflectance products. SREM also performs well when applied to Sentinel-2A and MODIS data, suggesting it can be used with other satellite sensors.
1. The document presents a study that proposes a modified analytical method for calculating Sky View Factor (SVF) to better represent urban canyon geometry. [2. The study measures elevation angles at 12 sites using a theodolite and considers the instrument height, taking two additional angles perpendicular to the street. This modifies Oke's original 2-angle method. [3. Results found significantly different SVF values between the original and modified methods, especially at sites with irregular streets, indicating the modified approach more accurately represents complex urban environments.
The document outlines topics related to horizontal positioning in engineering surveys, including introduction to horizontal positioning, datum defects, and methods to provide horizontal control such as triangulation, trilateration, intersection, and resection. It discusses these topics over several pages with examples and explanations. The focus is on establishing accurate horizontal positioning for engineering projects.
Гоман, Загайнов, Храмцовский (1997) - Использование бифуркационных методов дл...Project KRIT
This document discusses the application of bifurcation methods to analyze nonlinear problems in flight dynamics, such as stall, spin, and roll-coupling. It provides an overview of how bifurcation analysis and continuation techniques have been used to study these problems for various aircraft, including the F-4, F-14, and F-15. The document outlines the methodology for applying bifurcation analysis, which involves using continuation methods to find equilibrium solutions and closed orbits, analyzing their stability, and predicting behavior as parameters vary. This allows researchers to better understand nonlinear aircraft dynamics at high angles of attack and critical flight regimes.
The document discusses formulas for calculating the gravitational effects of topographic-isostatic masses on airborne and satellite gravity gradiometry measurements. It derives integral formulas in ellipsoidal approximation for computing the gravitational potential, gradients, and tensor due to various topographic-isostatic models. The formulas separate the computations into spherical and ellipsoidal components. They are applied to calculate the gravitational tensor at GOCE satellite altitude using a 5-arcminute digital elevation model. The approach uses mass-lines to approximate ellipsoidal volume elements for numerical evaluation.
Decision analysis applied to rock tunnel exploration 1978 baecherJunaida Wally
This document discusses applying decision analysis to planning rock tunnel exploration. Decision analysis provides a framework for making decisions under uncertainty. It involves defining alternatives, outcomes, variables, and relationships between them. Probabilities are assigned to uncertain variables. Expected costs are calculated for different exploration and construction strategies to determine the optimal approach. A decision tree is used to relate variables like geology, exploration method/cost, construction method/cost, and outcomes like total expected cost. Sensitivity analysis varies the variables to evaluate how robust the optimal strategies are. The framework provides a systematic way to determine if and where exploration is beneficial for reducing uncertainty and expected construction costs for a rock tunnel project.
IJRET-V1I1P3 - Remotely Sensed Images in using Automatic Road Map CompilationISAR Publications
High Resolution satellite Imagery is an important source for road network extraction for
roads database creation, refinement and updating. Various sources of imagery are known for their
differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for
different purposes of vegetation mapping. A number of shape descriptors are computed to reduce
the misclassification between road and other spectrally similar objects. The detected road segments
are further refined using morphological operations to form final road network, which is then
evaluated for its completeness, correctness and quality. The proposed methodology has been tested
on updating on road extraction from remotely-sensed imagery.
Remote sensing and GIS can be applied in civil engineering for spatial analysis and to answer geographic queries. Spatial analysis examines how the locations of objects impact analysis results and can reveal patterns. GIS uses methods like overlay, proximity, density, and network analysis to study spatial relationships. Common analyses include measuring distances, areas and shapes, transforming datasets, descriptive summaries of data, and optimizing locations.
The document summarizes research using remote sensing data and quantitative analysis to identify and characterize alluvial fans. Satellite imagery was used to calculate surface roughness as a proxy for distinguishing alluvial areas. Digital elevation models from SRTM data were analyzed to delineate geometric parameters of landforms. A fuzzy logic model populated with roughness, elevation, and curvature data was able to classify terrain into categories corresponding to different parts of alluvial fans. The method provided initial identification and spatial extent of alluvial fans while also assigning fuzzy membership values.
John McGaughey, CEO/President of Mira Geoscience offers his thoughts and the practices of integrated geophysical interpretation at the 3D Interest Group
Risk Assessment in Geotechnical Engineeringsoumaya Addou
A presentation Soumaya Addou a Master student in Tohoku University made about Risk Assessment in Geotechnical Engineering during meeting of Risk commission, that is part of the Japanese Geotechnical Society - Tohoku branch.
Digital soil mapping uses statistical methods and environmental data to predict soil properties across continuous landscapes. It involves preparing soil data and predictor variables like climate, vegetation and remote sensing data. Predictor data is harmonized using techniques like principal components analysis. Soil data is also harmonized by estimating mean values at standard depth intervals. Regression models are selected to relate soil properties to predictors and create continuous prediction maps. Maps are validated and uncertainty is estimated using confidence intervals or bootstrapping. The process is implemented using the R programming language and specialized soil mapping packages.
This document discusses a study that tested the use of infrared thermography (IRT) for landslide mapping and characterization. IRT uses infrared cameras to detect differences in surface temperature that can indicate features associated with landslide activity such as fractures, moisture zones, and loose debris. The study acquired IRT data from terrestrial and aerial platforms for several landslides in Italy. Thermograms revealed thermal anomalies corresponding to instability features and were geo-referenced for analysis in GIS. IRT provides a remote method for detailed landslide mapping and monitoring of hazardous areas.
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Google Earth Web Service as a Support for GIS Mapping in Geospatial Research ...Universität Salzburg
The geospatial work has been performed using combination of the Google Earth imagery, Landsat TM images and Erdas Imagine GIS software. The advantage of utilizing Google Earth scenes with Landsat TM satellite imagery, along with GIS techniques and methods, for inventorying land cover types has been demonstrated for landscape studies. Combination of land cover type characteristics and landscape changes enabled to analyse landscape dynamics, as well as applicability of Google Earth service for thematic mapping. The used data included Landsat TM and ETM+ multi-band imagery covering area in Izmir, western Turkey. The image processing was per- formed using supervised classification in Erdas Imagine software. The Google Earth web service technologies were applied to test the accuracy of mapping via the available module of Erdas Imagine «Linking with Google Earth».
This document presents a landslide susceptibility map created for Sri Lanka using a bivariate statistical method. Six factors that influence landslide susceptibility are identified: lithology, soil type, landuse, slope, aspect, and curvature. GIS software is used to generate weighted maps for each factor based on statistical analysis of landslide occurrences. These weighted maps are then combined through map algebra to create a final landslide hazard susceptibility map for Sri Lanka. The workflow involves data preparation steps like rasterization of vector data, reclassification of aspect, slope and curvature, followed by zonal statistics analysis and calculation of weight values to produce individual weighted maps for each factor, which are then summed to obtain the final susceptibility map.
This document discusses landslide hazard mapping using GIS. It describes different methodological approaches for landslide hazard mapping including heuristic, statistical, and deterministic approaches. The statistical approaches include bivariate and multivariate analysis. Case studies applying bivariate analysis and logistic regression are presented. Key inputs for landslide hazard mapping include landslide inventory maps, lithology, slope, and other thematic maps. The document concludes different statistical methods can be used but validation and replication of results is challenging.
Geographic information system(GIS) and its applications in agricultureKiranmai nalla
This document presents a seminar on geographic information systems (GIS) given by Nalla Anthony Kiranmai. The seminar discusses the principles, components, functions, applications and advantages of GIS. It covers topics such as the linkage between remote sensing and GIS, vector vs raster data representation, spatial data analysis functions including overlays and buffers, and applications of GIS in fields like agriculture, land suitability analysis, and groundwater assessment. The seminar aims to provide an introduction to GIS concepts and demonstrate how GIS can be used as an integrated technology for spatial analysis and decision support.
2010 rock slope risk assesment based on geostructural annaMasagus Azizi
The document describes a study on assessing rock slope stability along a highway in North Malaysia. Laser scanning and traditional surveying techniques were used to characterize discontinuities in eight rock slopes. Discontinuity orientations and positions were derived from laser scanning point clouds. Stability analyses using key block analysis identified potential failure mechanisms. A relative hazard index was developed based on slope geometry, stability, water presence, and protections to assess hazard levels and inform mitigation recommendations. The study provides a methodology for integrating advanced scanning with traditional surveys to evaluate rock slope stability.
Multi-Criteria Decision Making in Hotel Site Selection inventionjournals
In the Multi Criteria Decision-Making (MCDM) context, the selection is facilitated by evaluating each choice on the set of criteria. The criteria must be measurable and their outcomes must be measured for every decision alternative. In This Paper the decision making process frame work was developed to provide Hotel site suitability map. Road, river , built up areas n and the Available area were prepared as layers in ArcGIS 10.2 to create suitability model for development area. The results of this analysis indicated that 41% of the study area is considered as the most suitable place for hotel site selection, 33% of the area as moderately suitable and 21% percent as marginally suitable. A portion of 5% was found to be not suitable areas for hotel site selection
Wolfgang | Bikeability Workshop December 2010Morten Meyer
Various techniques can be used to incorporate spatial variables into choice experiments. Spatial representation, using maps or profiles, can provide valuable context and reduce preference variability compared to non-spatial methods. Both rigorous and artistic approaches were presented, with the rigorous using GIS and the artistic providing more freedom. Lessons indicated that while reality imposes constraints, more freedom may be needed to represent reality, and spatial context adds complexity not possible without spatial tools.
A MODEL FOR EARTHQUAKE CRISIS MANAGEMENT IN OLD URBANS (CASE STUDY NAJAFABAD...Julie Davis
This document presents a study analyzing the seismic risk in the old city of Najafabad, Iran using a multi-criteria decision making model. The study identifies 5 criteria and 18 sub-criteria to evaluate the vulnerability factors. These include physical exposure, accessibility, social, and relief management indicators. GIS analysis was used to classify the city into different risk zones based on the vulnerability assessment. The results found about 33% of the city has a high seismic risk, covering the central area and parts of Saleh Abad district, home to around 40,000 people. The Yazdanshahr district in the southwest also has medium rising risk due to its high population density.
Geomatics is the discipline of gathering, storing, processing, and delivering geographic information or spatially referenced information. It involves topics such as geodesy, topography, land surveying, cartography, photogrammetry, remote sensing, GPS, laser scanning, GIS, decision support systems, expert systems, and webGIS. Geomatics uses techniques from geography, computer science, and ontology to systematically collect, integrate, analyze, and distribute geospatial data for applications such as climate change monitoring, resource management, urban planning, and more.
The document summarizes a research paper on using CNN and LSTM models for land use and land cover change detection from remote sensing images. It presents the methodology which involves preprocessing two QuickBird satellite images from Russia from 2005 and 2010. Features are extracted from the segmented images using Gabor, Zernike moments and local features. A CNN model is used for classification and identifies changes such as increased buildings and conversion from forest to land. Performance is evaluated using accuracy, sensitivity, specificity and precision metrics which are over 95% according to the results. The paper concludes the CNN model effectively identifies temporal changes in land use from the satellite images.
This document discusses and compares various spatial interpolation techniques used in GIS, including inverse distance weighting (IDW), spline interpolation, kriging, and radial basis functions. It analyzes the results of applying IDW, spline, and kriging methods to weather station data from several US states. Ordinary and universal kriging techniques produced the smoothest surfaces that best modeled trends in the elevation data.
The document discusses improving the accuracy of digital terrain models (DTMs). It compares different algorithms for generating DTMs from point data, including inverse distance weighting, spline interpolation, Voronoi diagrams, Delaunay triangulation, and kriging. The author tests these algorithms on real elevation data from Oradea, Romania. Results show Delaunay triangulation produces the most accurate surface, followed by kriging and Shepard interpolation. Higher point density leads to greater DTM accuracy than the interpolation method used. The quality of DTMs can be improved by using Delaunay triangulation with a dense point distribution.
- Spatial autocorrelation measures the correlation of a variable with itself through space and can be positive or negative. It quantifies the degree of spatial clustering or dispersion of values across locations.
- Global measures identify overall patterns of clustering, while local measures identify specific clusters. Spatial weights defining neighbor relationships are required.
- Contiguity-based weights define neighbors based on shared boundaries, while distance-based weights use a threshold distance. Higher order weights incorporate indirect neighbors.
- Spatially lagged variables are weighted averages of neighboring values and are important for spatial autocorrelation tests and regression models.
Analyzing and assessing ecological transition in building sustainable citiesBeniamino Murgante
"Analyzing and assessing ecological transition in building sustainable cities" Keynote presentation at "International Conference on Sustainable Environment and Technologies" 23 September 2022, Nicolas Tesla University Union, Belgrade, Serbia
Smart Cities: New Science for the Cities
Beniamino Murgante
School of Engineering, University of Basilicata
Lecture at the Department of Community and Regional Planning
Smart Cities course - Professor Alenka Poplin
The evolution of spatial analysis and modeling in decision processesBeniamino Murgante
This document discusses the evolution of spatial analysis and modeling in decision processes. It describes how spatial analysis has progressed from early tools like spatial autocorrelation statistics to modern techniques using big data and volunteered geographic information. Examples are provided of using local spatial statistics, satellite imagery, and crowdsourced maps to analyze urban growth and quantify impacts of natural disasters. Overall, the document outlines the increasing role of spatial analysis and modeling in decision-making as tools and data availability have advanced.
Keynote at the 24th International Conference on Urban Planning and Regional Development in the Information Society
GeoMultimedia 2019, 2-4 April 2019
Karlsruhe Institute of Technology, Germany
Involving citizens in smart energy approaches: the experience of an energy pa...Beniamino Murgante
Involving citizens in smart energy approaches: the experience of an energy park in Calvello municipality
4th International Conference on Urban e-Planning, University of Lisbon, 23-24 April 2019
Programmazione per la governance territoriale in tema di tutela della biodive...Beniamino Murgante
Programmazione per la governance territoriale in tema di tutela della biodiversità - Sabrina Lai - Regione Sardegna, Direzione generale della difesa dell’ambiente slai@regione.sardegna.it
Università degli Studi di Cagliari, DICAAR, sabrinalai@unica.it
Presentation of ICCSA 2019 at the University of Saint petersburg Beniamino Murgante
The document discusses plans to hold the 2019 International Conference on Computational Science and Its Applications (ICCSA 2019) in Saint Petersburg, Russia from June 24-27, 2019. Saint Petersburg is a historic city that was formerly the capital of the Russian Empire and is now a cultural capital of Russia, with many beautiful suburbs and metro stations located in a UNESCO World Heritage Site downtown area.
RISCHIO TERRITORIALE NEL GOVERNO DEL TERRITORIO: Ricerca e formazione nelle s...Beniamino Murgante
RISCHIO TERRITORIALE NEL GOVERNO DEL TERRITORIO: Ricerca e formazione nelle scuole di ingegneria
Giuseppe Las Casas, Beniamino Murgante, Francesco Scorza
UrbIng 2016
Presentation of ICCSA 2017 at the University of triesteBeniamino Murgante
The document discusses the 17th International Conference on Computational Science and Its Applications (ICCSA 2017) that will take place from July 3-6, 2017 in Trieste, Italy. It provides background information on the University of Trieste, including its research centers, rankings, international partnerships, and spin-offs. The conference will bring together researchers in computational science and related fields and showcase the work happening at the University of Trieste and surrounding institutions in the Trieste area.
GEOGRAPHIC INFORMATION – NEED TO KNOW (GI-N2K) Towards a more demand-driven g...Beniamino Murgante
GEOGRAPHIC INFORMATION – NEED TO KNOW (GI-N2K) Towards a more demand-driven geospatial workforce education/training system
Mauro Salvemini, Giuliana Vitiello, Monica Sebillo, Sergio Farruggia. Beniamino Murgante
Focussing Energy Consumers’ Behaviour Change towards Energy Efficiency and Lo...Beniamino Murgante
This document discusses a project aimed at improving policy instruments to increase energy efficiency in buildings. The project will focus on three pillars: supplementary services from authorities, innovative cooperation models like public-private partnerships, and smart technologies. It will involve partners from several regions developing action plans over three years to test new policies and share results. The goal is to reduce energy consumption and emissions from buildings, which account for 40% of energy usage and 36% of emissions in the EU.
Socio-Economic Planning profiles: Sciences VS Daily activities in public sector Beniamino Murgante
This document outlines a training program on geographic information systems (GIS) and spatial statistics. The program consists of 11 modules that cover fundamentals of GIS, basics of statistics, socio-economic indicators, demographic indicators, introduction to geostatistics, spatial statistics, and spatial regression. Each module includes multiple lectures on related topics, techniques, and software. The overall aim is to provide education and training to develop a more demand-driven geospatial workforce.
GEOGRAPHIC INFORMATION – NEED TO KNOW (GI-N2K) Towards a more demand-driven g...Beniamino Murgante
GEOGRAPHIC INFORMATION – NEED TO KNOW (GI-N2K) Towards a more demand-driven geospatial workforce education/training system
Mauro Salvemini, Francesco Di Massa, Monica Sebillo, Sergio Farruggia. Beniamino Murgante
Garden in motion. An experience of citizens involvement in public space regen...Beniamino Murgante
Garden in motion. An experience of citizens involvement in public space regeneration.
Sara Lorusso, Gerardo Sassano, Michele Scioscia, Antonio Graziadei, Pasquale Passannante, Sara Bellarosa, Francesco Scaringi, Beniamino Murgante
1) Beniamino Murgante presented at the "Limits of Formal Planning in Managing the Urban Development" conference in Lodz, Poland on April 10-12, 2014.
2) The presentation discussed the challenges of formal planning and the need for "smartness" in urban development given rapid urbanization and new technologies.
3) Murgante argued that smart cities require connections between sensors, open data, and governance to effectively manage and plan urban development.
GeoSDI: una piattaforma social di dati geografici basata sui principi di INSP...Beniamino Murgante
This document summarizes the activities of the geoSDI laboratory. It discusses how geoSDI started in 2007 as a center of competence for spatial data infrastructures within the Italian government. It has since developed open source geospatial web platforms and provided support for emergency response around the world. Key projects include developing Geo-Platform, an open source framework for building INSPIRE-compliant SDIs, and providing geospatial support during disasters in Italy, Haiti, Chile and elsewhere. GeoSDI continues to develop new widgets and functionality for Geo-Platform while also implementing SDI systems for various government and international organization clients.
The document summarizes a presentation given by Beniamino Murgante at the COST ACTION TU1104 - Smart Energy Regions conference held on November 18-19, 2013 in Rome. The presentation was titled "Cities and Smartness: a critical analysis of opportunities and risks" and discussed the concept of smart cities, including definitions, technologies used such as sensors and open data, and both opportunities and risks of making cities smarter.
Fino alla fine degli anni '80 un urbanista che cercava di supportare dei ragionamenti di piano con l'informatica riusciva ad ottenere, nel migliore dei casi, qualche dato statistico sulla popolazione. Con il trascorrere degli anni si è assistito ad un incremento dell'utilizzo delle tecnologie per la costruzione dei quadri conoscitivi a supporto del processo di piano, fino a raggiungere l'attuale Information Explosion Era.
Il contenuto dell'intervento si baserà su aspetti teorici ed applicativi a partire dall'esperienza di Ian McHarg fino all'ultima "moda" delle Smart Cities.
Introduzione
Andreina Maahsen-Milan
Università di Bologna
Tecnologie, Territorio, Smartness
Beniamino Murgante
Università della Basilicata
Facoltà Ingegneria Edile di Ravenna - Università di Bologna
Via Tombesi dall'Ova 55, 48121 Ravenna
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‘Not afraid to die’ Kenya tax protests inspire broader demand for change.pdfJohn Leonardo
This presentation includes a video of an interview conducted by Al Jazeera reporter, Malcolm Webb, with Nyawanga Owuor, a Nairobi student who has covered the protests on Instagram. Nyawanga points out that “Blatant corruption and misspending by President William Ruto’s government had pushed young people to the limit and so they rejected the finance bill”. Nyawanga noted “It’s not new anger. It’s not specifically for the finance Bill. It’s a long pent-up anger failure from a period of time of our representatives not adequately representing us”.
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1. “ Automated Unsupervised Geomorphometric Classification of Earth Surface for Landslide Susceptibility Assessment ” Alessandro Paregiani and Maria Ioannilli International Conference on Computational Science and Its Applications ICCSA 2008 June 30th - July 3rd, 2008 Perugia, Italy "Geographical Analysis, Urban Modeling, Spatial Statistics" University of Rome “Tor Vergata”
2. Outline 4. Experimented Classification Methods 1. Landslide Hazard vs. Landslide Susceptibility 2. Purpose of the Work 3. Approaches to Landslide Susceptibility Analysis 6. Integrated Classification Method 5. Comparison of Intermediate Results 7. Correlation Analysis between Geomorphometric Classes and Types of Landslide 8. Conclusions
3. Landslides constitute one of the major hazards that cause losses in lives and property. To assess landslide occurrences is a complex analysis, involving multitude of factors and need to be studied systematically in order to evaluate the hazard. There are no universally accepted forecasting methods of "natural hazard" and in particular of landslide hazard. Landslide Hazard R = P x ( V x E ) (UNESCO)
4. Purpose of the work The definition of an automated method of terrain morphological classification in order to establish the correlation degree between topographic forms of the territory and landslide phenomena, by using a Landslide Inventory and a DEM as input A Landslide Inventory and a DEM Input Data Specific Objectives Identification of the most suitable measures to describe terrain topographic forms and to distinguish among geomorphically different landscapes (geometric signatures) Identification of a classification method, in order to obtain the best segmentation of terrain surface related to landslide phenomena Moving from the current literature: Building up the morphological parameters Classification by using different methods Evaluation of the goodness of each classification, by considering as factors the physical meaning of classes and the statistical correlation degree between classes and landslide phenomena Experiment end evaluation of a new integrated classification method Technical approach
5. Approaches to Landslide Susceptibility Analysis Indirect methodologies Geomorphometric Approach high degree of subjectivity this method doesn’t consider the relationships between instability factors Limits Algorithm Heuristic Approach Statistical Approach Method
6. The “science of quantitative land-surface analysis” It draws upon mathematical, statistical and image-processing techniques to quantify the shape of the earth at various spatial scales The quantitative analysis of a territory, and in particular of its shape, eliminates the limitations of the qualitative topographic information It stems from the need to establish a reliable numerical model in order to describe the earth shape. The quantitative characterization of topographical shape is a multidisciplinary technique applicable at any scale of analysis The geometric signature is an analytic tool of numerical land-surface classification The signature was defined as “a set of measurements sufficient to identify unambiguously an object or a set of objects” [Enzmann, 1966] Natural surface processes create different forms. The geometric signature abstracts those forms and expresses them numerically. Geomorphometric Approach Approaches to Landslide Susceptibility Analysis It considers combinations of instability factors, by introducing the “geometric signature” It eliminates the subjectivity of heuristic approach
7. Experimented Classification Methods Applying State-of-the-Art Classification Methods Supervised Classification: types of topography are recognized starting from selected “training samples” Unsupervised Classification: unconstrained by pre-set conditions, and allow the input data to determine “optimal” categories Parameters Authors Parameters - Single – Cell Topological Parameters “ Context” Parameters (extended neighborhood) Evans (1981) Pike – Iwahashi (2006) Pike (1971) Nested - Means Divided Parameters Clustering • Mean • S.D. • Variation coefficient • Symmetry • Slope gradient • Texture • Convexity - - • Slope gradient • Aspect • Plan curvature • Profile curvature - Method • Mean • S.D. • Variation Coefficient • Symmetry • Slope Gradient • Texture • Convexity • Slope Gradient • Aspect • Plan Curvature • Profile Curvature Types of
8. It depends on the input data type the scale of analysis the desired output data quality and spatial resolution the availability of analytic and information tools Preliminary Processing The choice of a terrain-unit of analysis Input data analysis and preparation 30x30m DEM, computed by interpolating the altitude-points extracted from contour lines (10m interval) of the Technical Regional Cartography of Lazio Landslide Inventory of Tevere River Basin Authority (PAI), differentiating seven types of phenomena; the number of events totally registered is 351 with a total area of 19.35 square kilometres as a portion of land surface which contains a set of ground conditions which differ from the adjacent units across definable boundaries
9. Considered Parameters Z = A x2y2 + B x2y + C xy2 + D x2 + E y2 + F xy + G x + H y + I A, B, C ecc. are calculated using this polynomial and 9 elevation values as input data, as shown: (the reference system has its origin-point in the central cell): A = [(Z1 + Z3 + Z7 + Z9) /4 - (Z2 + Z4 + Z6 + Z8) /2 + Z5] /L4 B = [(Z1 + Z3 - Z7 - Z9) /4 - (Z2 - Z8) /2] /L3 C = [(-Z1 + Z3 - Z7 + Z9) /4 + (Z4 - Z6)] /2] /L3 D = [(Z4 + Z6) /2 - Z5] /L2 E = [(Z2 + Z8) /2 - Z5] /L2 F = (-Z1 + Z3 + Z7 - Z9) /4L2 G = (-Z4 +Z6) /2L H = (Z2 - Z8) /2LI = Z5 Curvature = -2 (D + E) * 100 (dz/dx) = [(a + 2d + g) - (c + 2f + i)] / (8 * L) (dz/dy) = [(a + 2b + c) - (g + 2h + i)] / (8 * L) Computational procedure to calculate “curvature”: Computational procedure to calculate “local convexity”: Focalmean(DEM) Computational procedure to calculate “texture”: Focalmedian(DEM) DEM – Focalmedian(DEM) “ slope gradient” “ section curvature” “ plan curvature” “ aspect” “ local convexity” “ texture” Computational procedure to calculate “slope gradient” in “e” cell i h g f e d c b a
10. Considered Parameters: Slope Gradient (first threshold) Local Convexity (second threshold) Surface Texture (third threshold) Classification of earth topography from DEMs by a nested-means algorithm and a three-part geometric signature Experiment 1: Nested-Means Multivariate Analysis (Pike – Iwahashi) TOPOGRAPHY: Continuous random surface Independent of any spatial orderliness imposed by geomorphic processes
11. Nested-Means Multivariate Analysis (Pike - Iwahashi) The classification underline a remarkable distinction among mountainside surfaces in four different classes characterized by increasing values of elevation and slope gradient
12. Statistical Multivariate Analysis Cluster method Maximum internal homogeneity and minimum external homogeneity Statistical Mean of parameter distributions and Covariance among parameter distributions
13. Considered Parameters: Slope gradient (SIMG) Local Convexity (CONVEX) Surface Texture (PITPEAK) Experiment 2: Statistical Multivariate Analysis (Pike - Iwahashi)
14. Experiment 3: Statistical Multivariate Analysis (Evans) Considered Parameters: Slope gradient Aspect Plan Curvature Profile Curvature normalized as follow: A remarkable distinction among terrain elements originated by hydrological and wind erosive activities, such as torrential (Class 1) and fluvial (Class 6) riverbeds and ridges (Class 8), with a topological continuity between Classes 1 and 6
15. Experiment 4: Statistical Multivariate Analysis (Pike) Considered Parameters (statistically derived): Mean Standard Deviation Variation Coefficient Symmetry Not-derived Parameters: Elevation Slope Gradient Curvature
17. Integrated Classification Method grid-cell based analysis homogeneity between input data (30x30m cells) selection of relevant classes by a conditional function
18. Integrated Classification Method A particular of the classification discriminating mountainside surfaces Overlapping of the three classes representing ridges, fluvial and torrential riverbeds A particular of the new integrated classification that considers both mountainside surfaces and hydrological factors
19. Correlation Analysis Geomorphometric Classes/Landslides Integrated Classification Method K: type of landslide J: geomorphometric class M: number of k-type landslides in class j N: total number of k-type landslides
20. Conclusions Identification of the most suitable parameters to describe terrain topographic forms related to landslide susceptibility Slope gradient constitutes the main parameter in discriminating different classes with a clear physical meaning related to landslide susceptibility analysis Single – Cell Topological Parameters discriminate local physical terrain features “ Context” Parameters discriminate global physical terrain features Identification of a new classification method, in order to obtain the best segmentation of terrain surface related to the landslide phenomena The method working by the nested-means algorithm allows to identify global features Local features, such as fluvial and torrential riverbeds, have been identified by using the statistical multivariate method The goodness of each classification has been evaluated by considering as factors the physical meaning of classes and the statistical correlation degree between classes and landslide phenomena The results of this evaluation show that the integration of both classification methods allows to correctly classify the territory and to establish correlation degrees between geomorphometric classes and landslide phenomena This method could represent a useful tool in territorial-scale landslide susceptibility analysis. In fact, the application of this repeatable and reliable procedure may return the best results in a short time and with low economic resources.