Object-Based Rule Sets and Its Transferability for Building Extraction from High Resolution Satellite Imagery

With the advent of high spatial resolution satellite imagery, automatic and semiautomatic building extractions have turned into one of the outstanding research topics in the field of remote sensing and machine vision. To this date, various algorithms have been presented for extracting the buildings from satellite images. Such methods lend their bases to diverse criteria such as radiometric, geometric, edge detection, and shadow. In this paper, a novel object based approach has been proposed for automatic and robust detections as well as extraction of the building in high spatial resolution images. To fulfill this, we simultaneously made use of both stable and variable features. While the former can be derived from inherent characteristics of the buildings, the latter is extracted using a feature analysis tool. In addition, a novel perspective has been recommended to boost the automation degree of the segmentation part in the object based analysis of remote sensing imagery. The proposed method was applied to a QuickBird imagery of an urban area in Isfahan city and the results of the quantitative evaluation demonstrated that the proposed method could yield promising results. Moreover, in another section of this study, for assessing the algorithm transferability, the rule set was implemented to a part of the WorldView image of Yazd city, proving that the proposed approach is capable of transferability in different types of case studies.     

Perceived and actual risks of drought: household and expert views from the lower Teesta River Basin of northern Bangladesh

Natural Hazards - Disaster risk perception and risk appraisal are essential in formulating an appropriate disaster risk reduction policy. This study examines the actual vs perceived drought risks...     

Nexus between vulnerability and adaptive capacity of drought-prone rural households in northern Bangladesh

Natural Hazards - Bangladesh is one of the world’s most climate-vulnerable countries. The appraisal of household vulnerability and capacity to adapt under climate change is therefore crucial...     

Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin,Asir Region,Saudi Arabia

The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector Machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artificial Neural Network(ANN),Quadratic Discriminant Analysis(QDA),Linear Discriminant Analysis(LDA),and Naive Bayes(NB),for landslide susceptibility modeling and comparison of their performances.Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue.This study was carried out using GIS and R open source software at Abha Basin,Asir Region,Saudi Arabia.First,a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data sources.All the landslide areas were randomly separated into two groups with a ratio of 70%for training and 30%for validating purposes.Twelve landslide-variables were generated for landslide susceptibility modeling,which include altitude,lithology,distance to faults,normalized difference vegetation index(NDVI),landuse/landcover(LULC),distance to roads,slope angle,distance to streams,profile curvature,plan curvature,slope length(LS),and slope-aspect.The area under curve(AUC-ROC)approach has been applied to evaluate,validate,and compare the MLTs performance.The results indicated that AUC values for seven MLTs range from 89.0%for QDA to 95.1%for RF.Our findings showed that the RF(AUC=95.1%)and LDA(AUC=941.7%)have produced the best performances in comparison to other MLTs.The outcome of this study and the landslide susceptibility maps would be useful for environmental protection.     

A new closed‐form solution for analysis of unlined pressure tunnels under seepage forces

In this study, a simplified analytical closed‐form solution, considering plane strain and axial symmetry conditions, for analysis of a circular pressure tunnel excavated underwater table, is developed. The method accounts for the seepage forces with the steady‐state flow and is based on the generalized effective stress law. To examine the effect of pore pressure variations and also the boundary conditions at the ground surface, the formulations are derived for different directions around the tunnel. The proposed method can be applied for analysis and design of pressure tunnels. Illustrative examples are given to demonstrate the performance of the proposed solution and also to examine the effect of seepage forces on the stability of tunnels. The simplified analytical solution derived in this study is compared with numerical analyses. It is concluded that the classic solutions (Lame's thick‐walled solution), considering the internal pressure as a mechanical load applied to the tunnel surface, are not applicable to pervious media and can result in an unsafe design. Copyright © 2012 John Wiley & Sons, Ltd.     

Optically stimulated luminescence (OSL) dating of quartzite cobbles from the Tapada do Montinho archaeological site (east‐central Portugal)

The burial age of an alluvially deposited cobble pavement at the Tapada do Montinho archaeological site (east‐central Portugal) is investigated using optically stimulated luminescence (OSL) dating. Measurements on the cobbles (quartzite clasts) were carried out on intact slices and large aliquots (～8 mm) of quartz grains (63–300 μm), both recovered from the outer 1.5‐mm surface of the cobbles. The recycling ratio, recuperation and dose‐recovery tests show that the single‐aliquot regenerative‐dose (SAR) protocol is applicable to both rock slices and quartz grains; both have similar luminescence characteristics. The variation in the natural OSL signal with depth below the cobble surface using intact slices from two different cobbles shows that both were bleached to a depth of at least ～2 mm before deposition. A model of the variation of dose with depth fitted to data from one of the cobbles gives a burial age of ～19 ka and also predicts the dose‐depth variation at the time of deposition. Ages based on rock slices suggest that one cobble surface, and the inner parts of two other cobbles experienced a resetting event at ～45 ka, consistent with the age control. However, the surfaces of the other cobbles all record light‐exposure events in the range 26 to 14 ka, suggesting that some of the cobbles were exposed to daylight perhaps more than once in this period. Given the shallow burial depth and unexpectedly young ages of the surrounding and overlying finer‐grained sediment, it is suggested that phases of light exposure following surficial erosion are probably responsible for this underestimate. Nevertheless, it is remarkable that we can identify and quantify four events (two light exposures of different durations and two sequential burial periods) in the dose record contained within a single clast, and this suggests that the luminescence dating of rock surfaces may prove, in the future, to be at least as important as sand/silt sediment dating.     

A non‐linear coupled finite element–boundary element model for the prediction of vibrations due to vibratory and impact pile driving

This paper presents a non‐linear coupled finite element–boundary element approach for the prediction of free field vibrations due to vibratory and impact pile driving. Both the non‐linear constitutive behavior of the soil in the vicinity of the pile and the dynamic interaction between the pile and the soil are accounted for. A subdomain approach is used, defining a generalized structure consisting of the pile and a bounded region of soil around the pile, and an unbounded exterior linear soil domain. The soil around the pile may exhibit non‐linear constitutive behavior and is modelled with a time‐domain finite element method. The dynamic stiffness matrix of the exterior unbounded soil domain is calculated using a boundary element formulation in the frequency domain based on a limited number of modes defined on the interface between the generalized structure and the unbounded soil. The soil–structure interaction forces are evaluated as a convolution of the displacement history and the soil flexibility matrices, which are obtained by an inverse Fourier transformation from the frequency to the time domain. This results in a hybrid frequency–time domain formulation of the non‐linear dynamic soil–structure interaction problem, which is solved in the time domain using Newmark's time integration method; the interaction force time history is evaluated using the θ‐scheme in order to obtain stable solutions. The proposed hybrid formulation is validated for linear problems of vibratory and impact pile driving, showing very good agreement with the results obtained with a frequency‐domain solution. Linear predictions, however, overestimate the free field peak particle velocities as observed in reported field experiments during vibratory and impact pile driving at comparable levels of the transferred energy. This is mainly due to energy dissipation related to plastic deformations in the soil around the pile. Ground vibrations due to vibratory and impact pile driving are, therefore, also computed with a non‐linear model where the soil is modelled as an isotropic elastic, perfectly plastic solid, which yields according to the Drucker–Prager failure criterion. This results in lower predicted free field vibrations with respect to linear predictions, which are also in much better agreement with experimental results recorded during vibratory and impact pile driving. Copyright © 2008 John Wiley & Sons, Ltd.     

Dust acoustic solitary and shock waves in coupled dusty plasmas with variable dust charge and vortex-like ion distribution

The modified Kodomtsev-Petviashvili-Burger (mKP-Burger) and Kodomtsev-Petviashvili-Burger equations are derived in strongly coupled dusty plasmas containing iso-nonthermal ions; Boltzmann distributed electrons and variable dust charge. We use reductive perturbation method and discuss on solitary waves and shock waves solutions of these equations.     

Conventional vs. modified pseudo-dynamic seismic analyses in the shallow strip footing bearing capacity problem

The conventional pseudo-dynamic(CPD) and modified pseudo-dynamic(MPD) methods are invoked to obtain the seismic bearing capacity of strip foundations using the limit equilibrium method, with a two-wedge failure mechanism.A spectral version of the conventional pseudo-dynamic method(SPD) is also invoked by considering the ground motion amplification factor, to be a function of the non-dimensional frequency λ/B and soil damping. Numeric analyses show that bearing capacity results obtained by the MPD and SPD methods are generally consistent. Both experience the same general reduction in bearing capacity with the increase of λ/B, with successive ups and downs corresponding to soil′s natural frequencies. For 5λ/B10, SPD and MPD results fluctuated between falling above and below CPD results. For λ/B2.5, SPD and MPD results were consistent with attenuation of the shear wave, while for 10λ/B, amplification was exhibited. Results obtained by the CPD method monotonically decrease, due to the fact that CPD fails to inherently consider site effects and damping, and instead and relies on a single factor to consider the ground motion amplification.     

Annual precipitation forecast for west, southwest, and south provinces of Iran using artificial neural networks

Rainfed agriculture plays an important role in the agricultural production of the southern and western provinces of Iran. In rainfed agriculture, the adequacy of annual precipitation is considered as an important factor for dryland field and supplemental irrigation management. Different methods can be used for predicting the annual precipitation based on climatic and non-climatic inputs. Among which artificial neural networks (ANN) is one of these methods. The purpose of this research was to predict the annual precipitation amount (millimeters) in the west, southwest, and south of Islamic Republic of Iran with the total area of 394,259?km², by applying non-climatic inputs according to the long-time average precipitation in each station (millimeters), 47.5?mm precipitation since the first of autumn (day), t _47.5, and other effective parameters like coordinate and altitude of the stations, by using the artificial neural networks. In order to intelligently estimate the annual amount of precipitation in the study regions (ten provinces), feedforward backpropagation artificial neural network model has been used (method I). To predict the annual precipitation amount more accurately, the region under study was divided into three sub-regions, according to the precipitation mapping, and for each sub-region, the neural networks were developed using t _47.5 and long-time average annual precipitation in each station (method II). It is concluded that neural networks did not significantly increase the prediction accuracy in the study area compared with multiple regression model proposed by other investigators. However, in case of ANN, it is better to use a structure of 2–6–6–10–1 and Levenberg–Marquardt learning algorithm and sigmoid logistic activation function for prediction of annual precipitation.