Search results for: land cover classification

Authors: A. Elsehemy, M. Abdeen , T. Nazmy

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Since the appearance of the Semantic web, many semantic search techniques and models were proposed to exploit the information in ontology to enhance the traditional keyword-based search. Many advances were made in languages such as English, German, French and Spanish. However, other languages such as Arabic are not fully supported yet. In this paper we present a framework for ontology based information retrieval for Arabic language. Our system consists of four main modules, namely query parser, indexer, search and a ranking module. Our approach includes building a semantic index by linking ontology concepts to documents, including an annotation weight for each link, to be used in ranking the results. We also augmented the framework with an automatic document categorizer, which enhances the overall document ranking. We have built three Arabic domain ontologies: Sports, Economic and Politics as example for the Arabic language. We built a knowledge base that consists of 79 classes and more than 1456 instances. The system is evaluated using the precision and recall metrics. We have done many retrieval operations on a sample of 40,316 documents with a size 320 MB of pure text. The results show that the semantic search enhanced with text classification gives better performance results than the system without classification.

Keywords: Arabic text classification, ontology based retrieval, Arabic semantic web, information retrieval, Arabic ontology

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Authors: Jhovana Silvia Escobar Ortega, Ines Eugenia Garcia De Salamone

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Cover crops are an agri-technological alternative to improve all properties of soils. Cover crops such as oats and rye could be used to reduce erosion and favor system sustainability when they are grown in the same agricultural cycle of the soybean crop. This crop is very profitable but its low contribution of easily decomposable residues, due to its low C/N ratio, leaves the soil exposed to erosive action and raises the need to reduce its monoculture. Furthermore, inoculation with the plant growth promoting rhizobacteria contributes to the implementation, development and production of several cereal crops. However, there is little information on its effects on forage crops which are often used as cover crops to improve soil quality. In order to evaluate the effect of combined inoculation with Azospirillum brasilense and Pseudomonas fluorescens on rhizosphere microbial communities, field experiments were conducted in the west of Buenos Aires province, Argentina, with a split-split plot randomized complete block factorial design with three replicates. The factors were: type of cover crop, inoculation and fertilization. In the main plot two levels of fertilization 0 and 7 40-0-5 (NPKS) were established at sowing. Rye (Secale cereale cultivar Quehué) and oats (Avena sativa var Aurora.) were sown in the subplots. In the sub-subplots two inoculation treatments are applied without and with application of a combined inoculant with A. brasilense and P. fluorescens. Due to the growth of cover crops has to be stopped usually with the herbicide glyphosate, rhizosphere soil of 0-20 and 20-40 cm layers was sampled at three sampling times which were: before glyphosate application (BG), a month after glyphosate application (AG) and at soybean harvest (SH). Community level of physiological profiles (CLPP) and Shannon index of microbial diversity (H) were obtained by multivariate analysis of Principal Components. Also, the most probable number (MPN) of nitrifiers and cellulolytics were determined using selective liquid media for each functional group. The CLPP of rhizosphere microbial communities showed significant differences between sampling times. There was not interaction between sampling times and both, types of cover crops and inoculation. Rhizosphere microbial communities of samples obtained BG had different CLPP with respect to the samples obtained in the sampling times AG and SH. Fertilizer and depth of sampling also caused changes in the CLPP. The H diversity index of rhizosphere microbial communities of rye in the sampling time BG were higher than those associated with oats. The MPN of both microbial functional types was lower in the deeper layer since these microorganisms are mostly aerobic. The MPN of nitrifiers decreased in rhizosphere of both cover crops only AG. At the sampling time BG, the NMP of both microbial types were larger than those obtained for AG and SH. This may mean that the glyphosate application could cause fairly permanent changes in these microbial communities which can be considered bio-indicators of soil quality. Inoculation and fertilizer inputs could be included to improve management of these cover crops because they can have a significant positive effect on the sustainability of the agro-ecosystem.

Keywords: community level of physiological profiles, microbial diversity, plant growth promoting rhizobacteria, rhizosphere microbial communities, soil quality, system sustainability

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Authors: Lufei Mao, Hongwei Chen, Zijiao Chai

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Informal housing is a widespread phenomenon in developing countries. In many newly-emerging cities in China, rapid urbanization leads to an influx of population as well as a shortage of housing. Under this background, container housing, a new form of informal housing, gradually appears on a small scale on urban temporary land in recent years. Container housing, just as its name implies, transforms containers into small houses that allow migrant workers group to live in it. Scholars in other countries have established sound theoretical frameworks for informal housing study, but the research fruits seem rather limited on this small scale housing form. Unlike the cases in developed countries, these houses, which are outside urban planning, bring about various environmental, economic, social and governance issues. Aiming to figure out this new-born housing form, a survey mainly on two container housing settlements in Hangzhou, China was carried out to gather the information of them. Based on this thorough survey, the paper concludes the features and problems of infrastructure, environment and social communication of container housing settlements. The result shows that these containers were lacking of basic facilities and were restricted in a small mess temporary land. Moreover, because of the deficiency in management, the rental rights of these containers might not be guaranteed. Then the paper analyzes the factors affecting the formation and evolution of container housing settlements. It turns out that institutional and policy factors, market factors and social factors were the main three factors that affect the formation. At last, the paper proposes some suggestions for the governance of container housing and the utility pattern of urban temporary land.

Keywords: container housing, informal housing, urban temporary land, urban governance

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Authors: M. Achemo, O. Saito

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Jefoure land use system is one of the traditional landscape human settlement patterns, and it is a cultural design and peculiar art of the people of Gurage in Ethiopia via which houses and trees flank roads left and right. Assessment of the multiple benefits of the traditional road that benefit society and development could enhance the understanding of the land use planners and decision makers to pay attention while planning and managing the land use system. Recent trend shows that the Jefoure land use is on the threshold of change as a result of flourishing road networks, overgrazing, and agricultural expansion. This study aimed to evaluate the multiple ecosystem services provided by the Jefoure land use system after characterization of the socio-ecological landscape. Information was compiled from existing data sources such as ordnance survey maps, aerial photographs, recent high resolution satellite imageries, designated questionnaires and interviews, and local authority contacts. The result generated scientific data on the characteristics, ecosystem services provision, and drivers of changes. The cultural landscape has novel characteristics and providing multiple ecosystem services to the community for long period of time. It is serving as road for humans, livestock and vehicles, habitat for plant species, regulating local temperature, climate, runoff and infiltration, and place for meeting, conducting religious and spiritual activities, holding social events such as marriage and mourning, playing station for children and court for football and other traditional games. As a result of its aesthetic quality and scenic beauty, it is considered as recreational place for improving mental and physical health. The study draws relevant land use planning and management solution in the improvement of socio-ecological resilience in the Jefoure land use system. The study suggests the landscape needs to be registrar as heritage site for recognizing the wisdom of the community and enhancing the conservation mechanisms.

Keywords: cultural landscape, ecosystem services, Gurage, Jefoure

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Authors: Saurav Kumar Suman, P. Karthigayani

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In this paper the height of the tree is estimated and forest types is classified from the multi temporal RISAT-1 Horizontal-Horizontal (HH) and Horizontal-Vertical (HV) Polarised Satellite Aperture Radar (SAR) data. The novelty of the proposed project is combined use of the Back-scattering Coefficients (Sigma Naught) and the Coherence. It uses Water Cloud Model (WCM). The approaches use two main steps. (a) Extraction of the different forest parameter data from the Product.xml, BAND-META file and from Grid-xxx.txt file come with the HH & HV polarized data from the ISRO (Indian Space Research Centre). These file contains the required parameter during height estimation. (b) Calculation of the Vegetation and Ground Backscattering, Coherence and other Forest Parameters. (c) Classification of Forest Types using the ENVI 5.0 Tool and ROI (Region of Interest) calculation.

Keywords: RISAT-1, classification, forest, SAR data

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Authors: M. Baratieri, M. Beccali, S. Corradino, B. Di Pietra, C. La Grassa, F. Monteleone, G. Morosinotto, G. Puglisi

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The energy renovation of existing buildings represents an important opportunity to increase the decarbonization and the sustainability of urban environments. In this context, the work carried out has the objective of demonstrating the technical and economic feasibility of an energy renovate of a public building destined for offices located on the island of Lampedusa in the Mediterranean Sea. By applying the Italian transpositions of European Directives 2010/31/EU and 2009/28/EC, the building has been renovated from the current energy requirements of 111.7 kWh/m² to 16.4 kWh/m². The result achieved classifies the building as nZEB (nearly Zero Energy Building) according to the Italian national definition. The analysis was carried out using in parallel a quasi-stationary software, normally used in the professional field, and a dynamic simulation model often used in the academic world. The proposed interventions cover the components of the building’s envelope, the heating-cooling system and the supply of energy from renewable sources. In these latter points, the analysis has focused more on assessing two aspects that affect the supply of renewable energy. The first concerns the use of advanced logic control systems for air conditioning units in order to increase photovoltaic self-consumption. With these adjustments, a considerable increase in photovoltaic self-consumption and a decrease in the electricity exported to the Island's electricity grid have been obtained. The second point concerned the evaluation of the building's energy classification considering the real efficiency of the heating-cooling plant. Normally the energy plants have lower operational efficiency than the designed one due to multiple reasons; the decrease in the energy classification of the building for this factor has been quantified. This study represents an important example for the evaluation of the best interventions for the energy renovation of buildings in the Mediterranean Climate and a good description of the correct methodology to evaluate the resulting improvements.

Keywords: heat pumps, HVAC systems, nZEB renovation, renewable energy sources

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Authors: Sondos M. Hammad, Sherin M. ElGokhy, Mahmoud M. Fahmy, Elsayed A. Sallam

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In this paper, an ensemble-based Support Vector Machine (SVM) classification approach is proposed. It is used for miRNA prediction. Three problems, commonly associated with previous approaches, are alleviated. These problems arise due to impose assumptions on the secondary structural of premiRNA, imbalance between the numbers of the laboratory checked miRNAs and the pseudo-hairpins, and finally using a training data set that does not consider all the varieties of samples in different species. We aggregate the predicted outputs of three well-known SVM classifiers; namely, Triplet-SVM, Virgo and Mirident, weighted by their variant features without any structural assumptions. An additional SVM layer is used in aggregating the final output. The proposed approach is trained and then tested with balanced data sets. The results of the proposed approach outperform the three base classifiers. Improved values for the metrics of 88.88% f-score, 92.73% accuracy, 90.64% precision, 96.64% specificity, 87.2% sensitivity, and the area under the ROC curve is 0.91 are achieved.

Keywords: MiRNAs, SVM classification, ensemble algorithm, assumption problem, imbalance data

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Authors: N. Hadj Mohamed, A. Boutaleb

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The Beni Snouss area is characterized by horst and graben structures and it comprises deformed Palaeozoic sedimentary and magmatic rocks overlapping by Mesozoic sediments. Two structural units are distinguished: a Palaeozoic basement and a Mesozoic cover. The study area is densely faulted and major faults strike N110° to N140° and dip vertically The mineralized fault zones are readily distinguishable by their argillic wall rock alteration. The fault zones that are filled with mineralizations, aplites, microgranites and quartz run roughly parallel to each other and are apparently in the same fault system. The Palaeozoic basement rocks contain mineralization occurring as veins, veinlets and disseminations. The Liassic carbonate platform sequence contains Ba (Pb-Zn) sulphide deposits occurring mainly as strata bound, and open space filling.

Keywords: Algeria, basement, Beni Snouss, cover

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Authors: Tsedekech Gebremeskel Weldmichael, Tamas Szegi, Lubangakene Denish, Ravi Kumar Gangwar, Erika Micheli, Barbara Simon

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Following the 1992 Earth Summit in Rio de Janeiro, soil biodiversity has been recognized globally as a crucial player in guaranteeing the functioning of soil and a provider of several ecosystem services essential for human well-being. The microbial fraction of the soil is a vital component of soil fertility as soil microbes play key roles in soil aggregate formation, nutrient cycling, humification, and degradation of pollutants. Soil fauna, such as earthworms, have huge impacts on soil organic matter dynamics, nutrient cycling, and infiltration and distribution of water in the soil. Currently, land-use change has been a global concern as evidence accumulates that it adversely affects soil biodiversity and the associated ecosystem goods and services. In this study, we examined the patterns of soil microbial respiration (SMR) and earthworm (abundance, biomass, and species richness) across three land-use types (grassland, arable land, and forest) in Hungary. The objectives were i) to investigate whether there is a significant difference in SMR and earthworm (abundance, biomass, and species richness) among land-use types. ii) to determine the key soil properties that best predict the variation in SMR and earthworm communities. Soil samples, to a depth of 25 cm, were collected from the surrounding areas of seven soil profiles. For physicochemical parameters, soil organic matter (SOM), pH, CaCO₃, E₄/E₆, available nitrogen (NH₄⁺-N and NO₃⁻-N), potassium (K₂O), phosphorus (P₂O₅), exchangeable Ca²⁺, Mg²⁺, soil moisture content (MC) and bulk density were measured. The analysis of SMR was determined by basal respiration method, and the extraction of earthworms was carried out by hand sorting method as described by ISO guideline. The results showed that there was no statistically significant difference among land-use types in SMR (p > 0.05). However, the highest SMR was observed in grassland soils (11.77 mgCO₂ 50g⁻¹ soil 10 days⁻¹) and lowest in forest soils (8.61 mgCO₂ 50g⁻¹ soil 10 days⁻¹). SMR had strong positive correlations with exchangeable Ca²⁺ (r = 0.80), MC (r = 0.72), and exchangeable Mg²⁺(r = 0.69). We found a pronounced variation in SMR among soil texture classes (p < 0.001), where the highest value in silty clay loam soils and the lowest in sandy soils. This study provides evidence that agricultural activities can negatively influence earthworm communities, in which the arable land had significantly lower earthworm communities compared to forest and grassland respectively. Overall, in our study, land use type had minimal effects on SMR whereas, earthworm communities were profoundly influenced by land-use type particularly agricultural activities related to tillage. Exchangeable Ca²⁺, MC, and texture were found to be the key drivers of the variation in SMR.

Keywords: earthworm community, land use, soil biodiversity, soil microbial respiration, soil property

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Authors: Cuneyt Yucelbas, Seral Ozsen, Sule Yucelbas, Gulay Tezel

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Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems.

Keywords: artificial immune system, breast cancer diagnosis, Euclidean function, Gaussian function

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Authors: Vistorina Amputu, Katja Tielboerger, Nichola Knox

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Primary productivity in dry savannahs is constraint by moisture availability and under increasing anthropogenic pressure. Thus, considering climate change and the unprecedented pace and scale of rangeland deterioration, methods for assessing the status of such rangelands should be easy to apply, yield reliable and repeatable results that can be applied over large spatial scales. Global and local scale monitoring of rangelands through satellite data and labor-intensive field measurements respectively, are limited in accurately assessing the spatiotemporal heterogeneity of vegetation dynamics to provide crucial information that detects degradation in its early stages. Fortunately, newly emerging techniques such as unmanned aerial vehicles (UAVs), associated miniaturized sensors and improving digital photogrammetric software provide an opportunity to transcend these limitations. Yet, they have not been extensively calibrated in natural systems to encompass their complexities if they are to be integrated for long-term monitoring. Limited research using drone technology has been conducted in arid savannas, for example to assess the health status of this dynamic two-layer vegetation ecosystem. In our study, we fill this gap by testing the relationship between UAV-estimated cover of rangeland functional attributes and field data collected in discrete sample plots in a Namibian dryland savannah along a degradation gradient. The first results are based on a supervised classification performed on the ultra-high resolution multispectral imagery to distinguish between rangeland functional attributes (bare, non-woody, and woody), with a relatively good match to the field observations. Integrating UAV-based observations to improve rangeland monitoring could greatly assist in climate-adapted rangeland management.

Keywords: arid savannah, degradation gradient, field observations, narrow-band sensor, supervised classification

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Authors: Rayed Alyousef, Tim Topper, Adil Al-Mayah

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Many of the reinforced concrete structures subject to cyclic load constructed before the modern bond and fatigue design code. One of the main issue face on exists structure is the bond strength of the longitudinal steel bar and the surrounding concrete. A lap splice is a common connection method to transfer the force between the steel rebar in a reinforced concrete member. Usually, the lap splice is the weak connection on the bond strength. Fatigue flexural loading imposes severe demands on the strength and ductility of the lap splice region in reinforced concrete structures and can lead to a brittle and sudden failure of the member. This paper investigates the effect of different concrete covers on the fatigue bond strength of reinforcing concrete beams containing a lap splice under a fatigue loads. It includes tests of thirty-seven beams divided into three groups. Each group has beams with 30 mm and 50 mm clear side and bottom concrete covers. The variables that were addressed where the concrete cover, the presence or absence of CFRP or GFRP sheet wrapping, the type of loading (monotonic or fatigue) and the fatigue load ranges. The test results showed that an increase in the concrete cover led to an increase in the bond strength under both monotonic and fatigue loading for both the unwrapped and wrapped beams. Also, the FRP sheets increased both the fatigue strength and the ductility for both the 30 mm and the 50 mm concrete covers.

Keywords: bond strength, fatigue, Lap splice, FRp wrapping

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Authors: Etienne Herrick

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As an economically and ecologically feasible method for farmers to introduce greater diversity into their crop rotations, cover cropping presents a valuable opportunity for improving the sustainability of food production. Planted in-between cash crop growing seasons, cover crops serve to enhance agroecosystem functioning, rather than being destined for sale or consumption. In fact, cover crops may hold the capacity to deliver multiple ecosystem functions or services simultaneously (multifunctionality). Building upon this line of research will not only benefit society at present, but also support its continued survival through its potential for restoring depleted soils and reducing the need for energy-intensive and harmful external inputs like fertilizers and pesticides. This study utilizes a trait-based approach to explore the influence of inter- and intra-specific interactions in summer cover crop mixtures and monocultures on functional trait expression and ecosystem services. Functional traits that enhance ecosystem services related to agricultural production include height, specific leaf area (SLA), root, shoot ratio, leaf C and N concentrations, and flowering phenology. Ecosystem services include biomass production, weed suppression, reduced N leaching, N recycling, and support of pollinators. Employing a trait-based approach may allow for the elucidation of mechanistic links between plant structure and resulting ecosystem service delivery. While relationships between some functional traits and the delivery of particular ecosystem services may be readily apparent through existing ecological knowledge (e.g. height positively correlating with weed suppression), this study will begin to quantify those relationships so as to gain further understanding of whether and how measurable variation in functional trait expression across cover crop mixtures and monocultures can serve as a reliable predictor of variation in the types and abundances of ecosystem services delivered. Six cover crop species, including legume, grass, and broadleaf functional types, were selected for growth in six mixtures and their component monocultures based upon the principle of trait complementarity. The tricultures (three-way mixtures) are comprised of a legume, grass, and broadleaf species, and include cowpea/sudex/buckwheat, sunnhemp/sudex/buckwheat, and chickling vetch/oat/buckwheat combinations; the dicultures contain the same legume and grass combinations as above, without the buckwheat broadleaf. By combining species with expectedly complimentary traits (for example, legumes are N suppliers and grasses are N acquirers, creating a nutrient cycling loop) the cover crop mixtures may elicit a broader range of ecosystem services than that provided by a monoculture, though trade-offs could exist. Collecting functional trait data will enable the investigation of the types of interactions driving these ecosystem service outcomes. It also allows for generalizability across a broader range of species than just those selected for this study, which may aid in informing further research efforts exploring species and ecosystem functioning, as well as on-farm management decisions.

Keywords: agroecology, cover crops, functional traits, multifunctionality, trait complementarity

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Authors: Rosa L. Figueroa, Christopher A. Flores, Qing Zeng-Treitler

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A regular expression consists of sequence characters which allow describing a text path. Usually, in clinical research, regular expressions are manually created by programmers together with domain experts. Lately, there have been several efforts to investigate how to generate them automatically. This article presents a text classification algorithm based on regexes. The algorithm named REX was designed, and then, implemented as a simplified method to create regexes to classify Spanish text automatically. In order to classify ambiguous cases, such as, when multiple labels are assigned to a testing example, REX includes an information gain method Two sets of data were used to evaluate the algorithm’s effectiveness in clinical text classification tasks. The results indicate that the regular expression based classifier proposed in this work performs statically better regarding accuracy and F-measure than Support Vector Machine and Naïve Bayes for both datasets.

Keywords: information gain, regular expressions, smith-waterman algorithm, text classification

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Authors: Omer Altaf, Liam Wotherspoon, Rolando Orense

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The Wairau Plains are located in the northeast of the South Island of New Zealand, with alluvial deposits of fine-grained silts and sands combined with low-lying topography suggesting the presence of liquefiable deposits over significant portions of the region. Liquefaction manifestations were observed in past earthquakes, including the 1848 Marlborough and 1855 Wairarapa earthquakes, and more recently during the 2013 Lake Grassmere and 2016 Kaikōura earthquakes. Therefore, a good understanding of the deposits that may be susceptible to liquefaction is important for land use planning in the region and to allow developers and asset owners to appropriately address their risk. For this purpose, multiple approaches have been employed to develop regional-scale maps showing the liquefaction vulnerability categories for the region. After applying semi-qualitative criteria linked to geologic age and deposit type, the higher resolution surface mapping of geomorphologic characteristics encompassing the Wairau River and the Opaoa River was used for screening. A detailed basin geologic model developed for groundwater modelling was analysed to provide a higher level of resolution than the surface-geology based classification. This is used to identify the thickness of near-surface gravel deposits, providing an improved understanding of the presence or lack of potentially non-liquefiable crust deposits. This paper describes the methodology adopted for this project and focuses on the influence of geomorphic characteristics and analysis of the detailed geologic basin model on the liquefaction classification of the Lower Wairau Plains.

Keywords: liquefaction, earthquake, cone penetration test, mapping, liquefaction-induced damage

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Authors: Abdullah Bal

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This paper presents a one-class classification (OCC) technique based on Fukunaga-Koontz Transform (FKT) for binary classification problems. The FKT is originally a powerful tool to feature selection and ordering for two-class problems. To utilize the standard FKT for data domain description problem (i.e., one-class classification), in this paper, a set of non-class samples which exist outside of positive class (target class) describing boundary formed with limited training data has been constructed synthetically. The tunnel-like decision boundary around upper and lower border of target class samples has been designed using statistical properties of feature vectors belonging to the training data. To capture higher order of statistics of data and increase discrimination ability, the proposed method, termed one-class FKT (OC-FKT), has been extended to its nonlinear version via kernel machines and referred as OC-KFKT for short. Multiple kernel learning (MKL) is a favorable family of machine learning such that tries to find an optimal combination of a set of sub-kernels to achieve a better result. However, the discriminative ability of some of the base kernels may be low and the OC-KFKT designed by this type of kernels leads to unsatisfactory classification performance. To address this problem, the quality of sub-kernels should be evaluated, and the weak kernels must be discarded before the final decision making process. MKL/OC-FKT and selective MKL/OC-FKT frameworks have been designed stimulated by ensemble learning (EL) to weight and then select the sub-classifiers using the discriminability and diversities measured by eigenvalue ratios. The eigenvalue ratios have been assessed based on their regions on the FKT subspaces. The comparative experiments, performed on various low and high dimensional data, against state-of-the-art algorithms confirm the effectiveness of our techniques, especially in case of small sample size (SSS) conditions.

Keywords: ensemble methods, fukunaga-koontz transform, kernel-based methods, multiple kernel learning, one-class classification

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Authors: Muhammad Yahaya Ubale

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Substantial rate of urbanization as well as economic growth is the tasks and prospects of sustainability. Objectives of the paper are: to ascertain the fate of sustainability in urbanized cities and; to identify the challenges of land use array in urbanized cities. Methodology engaged in this paper employed the use of secondary data where articles, conference proceedings, seminar papers and literature materials were effectively used. The paper established the fact that while one thinks globally, it is reciprocal to act locally if at all sustainability should be achieved. The speed and scale of urbanization must be equal to natural and cost-effective deliberations. It also discovered a podium that allows a city to work together as an ideal conglomerate, engaging all city departments as a source of services, engaging residents, businesses, and contractors. It also revealed that city should act as a leader and partner within an urban region, engaging senior government officials, utilities, rural settlements, private sector stakeholders, NGOs, and academia. Cities should assimilate infrastructure system design and management to enhance efficiency of resource flows in an urban area. They should also coordinate spatial development; integrate urban forms and urban flows, combine land use, urban design, urban density, and other spatial attributes with infrastructural development. Finally, by 2050, urbanized cities alone could be consuming 140 billion tons of minerals, ores, fossil fuels and biomass annually (three times its current rate of consumption), sustainability can be accomplished through land use control, limited access to finite resources, facilities, utilities and services as well as property right and user charge.

Keywords: sustainability, land use array, urbanized cities, fate of sustainability and perseverance

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Authors: Steffy Maria Joseph, Abdu Rahiman V, Abdul Hameed K. M.

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Online handwritten character recognition is a challenging field in Artificial Intelligence. The classification success rate of current techniques decreases when the dataset involves similarity and complexity in stroke styles, number of strokes and stroke characteristics variations. Malayalam is a complex south indian language spoken by about 35 million people especially in Kerala and Lakshadweep islands. In this paper, we consider the significant feature extraction for the similar stroke styles of Malayalam. This extracted feature set are suitable for the recognition of other handwritten south indian languages like Tamil, Telugu and Kannada. A classification scheme based on support vector machines (SVM) is proposed to improve the accuracy in classification and recognition of online malayalam handwritten characters. SVM Classifiers are the best for real world applications. The contribution of various features towards the accuracy in recognition is analysed. Performance for different kernels of SVM are also studied. A graphical user interface has developed for reading and displaying the character. Different writing styles are taken for each of the 44 alphabets. Various features are extracted and used for classification after the preprocessing of input data samples. Highest recognition accuracy of 97% is obtained experimentally at the best feature combination with polynomial kernel in SVM.

Keywords: SVM, matlab, malayalam, South Indian scripts, onlinehandwritten character recognition

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Authors: Thi Thanh Nga Pham, Damien Philippon, Alexis Drogoul, Thi Thu Thuy Nguyen, Tien Cong Nguyen

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Mekong River Delta region of Vietnam is recognized as one of the most vulnerable to climate change due to flooding and seawater rise and therefore an increased burden of climate change-related diseases. Changes in temperature and precipitation are likely to alter the incidence and distribution of vector-borne diseases such as dengue fever. In this region, the peak of the dengue epidemic period is around July to September during the rainy season. It is believed that climate is an important factor for dengue transmission. This study aims to enhance the capacity of dengue prediction by the relationship of dengue incidences with climate and environmental variables for Mekong River Delta of Vietnam during 2005-2015. Mathematical models for vector-host infectious disease, including larva, mosquito, and human being were used to calculate the impacts of climate to the dengue transmission with incorporating geospatial data for model input. Monthly dengue incidence data were collected at provincial level. Precipitation data were extracted from satellite observations of GSMaP (Global Satellite Mapping of Precipitation), land surface temperature and land cover data were from MODIS. The value of seasonal reproduction number was estimated to evaluate the potential, severity and persistence of dengue infection, while the final infected number was derived to check the outbreak of dengue. The result shows that the dengue infection depends on the seasonal variation of climate variables with the peak during the rainy season and predicted dengue incidence follows well with this dynamic for the whole studied region. However, the highest outbreak of 2007 dengue was not captured by the model reflecting nonlinear dependences of transmission on climate. Other possible effects will be discussed to address the limitation of the model. This suggested the need of considering of both climate variables and another variability across temporal and spatial scales.

Keywords: infectious disease, dengue, geospatial data, climate

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Authors: Yarra Sulina, Nunna Tagore Sai Priya, Ankhi Banerjee

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With the rapid growth in tourist arrivals, there has been a huge demand for the growth of infrastructure in the destinations. With the increasing preference of tourists to stay near attractions, there has been a considerable change in the land use around tourist sites. However, with the inclusion of certain regulations and guidelines provided by the authorities based on the nature of tourism activity and geographical constraints, the pattern of growth of built form is different for various tourist sites. Therefore, this study explores the patterns of growth of built-up for a decade from 2009 to 2019 through two-dimensional and three-dimensional analysis. Land use maps are created through supervised classification of satellite images obtained from LANDSAT 4-5 and LANDSAT 8 for 2009 and 2019, respectively. The overall expansion of the built-up area in the region is analyzed in relation to the distance from the city's geographical center and the tourism-related growth regions are identified which are influenced by the proximity of tourist attractions. The primary tourist sites of various destinations with different geographical characteristics and tourism activities, that have undergone a significant increase in built-up area and are occupied with tourism-related infrastructure are selected for further study. Proximity analysis of the tourism-related growth sites is carried out to delineate the influence zone of the tourist site in a destination. Further, a temporal analysis of volumetric growth of built form is carried out to understand the morphology of the tourist precincts over time. The Digital Surface Model (DSM) and Digital Terrain Model (DTM) are used to extract the building footprints along with building height. Factors such as building height, and building density are evaluated to understand the patterns of three-dimensional growth of the built area in the region. The study also explores the underlying reasons for such changes in built form around various tourist sites and predicts the impact of such growth patterns in the region. The building height and building density around tourist site creates a huge impact on the appeal of the destination. The surroundings that are incompatible with the theme of the tourist site have a negative impact on the attractiveness of the destination that leads to negative feedback by the tourists, which is not a sustainable form of development. Therefore, proper spatial measures are necessary in terms of area and volume of the built environment for a healthy and sustainable environment around the tourist sites in the destination.

Keywords: sustainable tourism, growth patterns, land-use changes, 3-dimensional analysis of built-up area

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Authors: Shelby Simpson, William Stanley, Namir Naba, Xiaodi Wang

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Both t-SNE and UMAP are brand new state of art tools to predominantly preserve the local structure that is to group neighboring data points together, which indeed provides a very informative visualization of heterogeneity in our data. In this research, we develop a t-SNE and UMAP base neural network image classification algorithm to embed the original dataset to a corresponding low dimensional dataset as a preprocessing step, then use this embedded database as input to our specially designed neural network classifier for image classification. We use the fashion MNIST data set, which is a labeled data set of images of clothing objects in our experiments. t-SNE and UMAP are used for dimensionality reduction of the data set and thus produce low dimensional embeddings. Furthermore, we use the embeddings from t-SNE and UMAP to feed into two neural networks. The accuracy of the models from the two neural networks is then compared to a dense neural network that does not use embedding as an input to show which model can classify the images of clothing objects more accurately.

Keywords: t-SNE, UMAP, fashion MNIST, neural networks

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Authors: O. Onoriode Emoyan, E. Eyitemi Akporhonor, Charles Otobrise

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Polycyclic Aromatic Hydrocarbons (PAHs) are formed mainly as a result of incomplete combustion of organic materials during industrial, domestic activities or natural occurrence. Their toxicity and contamination of terrestrial and aquatic ecosystem have been established. Though with limited validity index, previous research has focused on PAHs isomer pair ratios of variable physicochemical properties in source identification. The objective of this investigation was to determine the empirical validity of Pearson correlation coefficient (PCC) and cluster analysis (CA) in PAHs source identification along soil samples of different land uses. Therefore, 16 PAHs grouped as endocrine disruption substances (EDSs) were determined in 10 sample stations in top and sub soils seasonally. PAHs was determined the use of Varian 300 gas chromatograph interfaced with flame ionization detector. Instruments and reagents used are of standard and chromatographic grades respectively. PCC and CA results showed that the classification of PAHs along kinetically and thermodyanamically-favoured and those derived directly from plants product through biologically mediated processes used in source signature is about the predominance PAHs are likely to be. Therefore the observed PAHs in the studied stations have trace quantities of the vast majority of the sixteen un-substituted PAHs which may ultimately inhabit the actual source signature authentication. Type and extent of bacterial metabolism, transformation products/substrates, and environmental factors such as: salinity, pH, oxygen concentration, nutrients, light intensity, temperature, co-substrates and environmental medium are hereby recommended as factors to be considered when evaluating possible sources of PAHs.

Keywords: comparative correlation, kinetically and thermodynamically-favored PAHs, pearson correlation coefficient, cluster analysis, sources evaluation

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Authors: Akanksha Gupta, Vishal Dubey

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In the Neoliberal era, there has been a much-talked discourse about urban issues that arise from a narrow approach of the single rationality of market-driven planning in Indian cities. More to this, India's urban planning is already jeopardized by the captious shortage of infrastructure, a cluster of incoherent governing bodies and implementation mechanism, leading cities to lie in the plethora of urban challenges. In this context, Allahabad (now known as Prayagraj) a city in North India is not an exception. Once known as the most planned splendid Colonial city of the British regime in India collapsed phenomenally because of the incompetent approach of planning machinery, straightforward market-driven accession and lack of attention on urban equity and sustainability. Particularly Civil Lines a Colonial neighbourhood, reached to the zenith of the glorified legacy of the Colonial era, transformed into filthy and congested urban form. Contextually this study contemplates and assesses the chronological episodes of major changes in land management reforms and policies under the ad hoc approach of political economy and land use planning which radically degraded the living environment in the present context. This study would empirically showcase the selected sample area detailing some of the major consequences in terms of gradual change in urban morphology, land use, and function. Here the method of study is primarily a qualitative study implying oral history and other historical methods to exhibit the idiom of planning conundrum. This subsequently reflects the repercussions translated into major issues like unclear land titles, encroachment, and unauthorized development and mushrooming of informal and squatter settlements. In nutshell, the study seeks to distinct out the limitations of the land reform and land management policies, which impacted the general degradation to the beautiful setting of Colonial neighbourhood. The Colonial legacy of Civil Lines now exists in the traces of history- memories of people, who once took pride in its serenity have now witnessed the transformation bit by bit till neo-liberal market forces completely swallow it.

Keywords: civil lines, land reforms, policies, urban challenges

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Authors: C. S. Subhashini, H. L. Premaratne

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Landslides are the most recurrent and prominent disaster in Sri Lanka. Sri Lanka has been subjected to a number of extreme landslide disasters that resulted in a significant loss of life, material damage, and distress. It is required to explore a solution towards preparedness and mitigation to reduce recurrent losses associated with landslides. Artificial Neural Networks (ANNs) and Hidden Markov Model (HMMs) are now widely used in many computer applications spanning multiple domains. This research examines the effectiveness of using Artificial Neural Networks and Hidden Markov Model in landslides predictions and the possibility of applying the modern technology to predict landslides in a prominent geographical area in Sri Lanka. A thorough survey was conducted with the participation of resource persons from several national universities in Sri Lanka to identify and rank the influencing factors for landslides. A landslide database was created using existing topographic; soil, drainage, land cover maps and historical data. The landslide related factors which include external factors (Rainfall and Number of Previous Occurrences) and internal factors (Soil Material, Geology, Land Use, Curvature, Soil Texture, Slope, Aspect, Soil Drainage, and Soil Effective Thickness) are extracted from the landslide database. These factors are used to recognize the possibility to occur landslides by using an ANN and HMM. The model acquires the relationship between the factors of landslide and its hazard index during the training session. These models with landslide related factors as the inputs will be trained to predict three classes namely, ‘landslide occurs’, ‘landslide does not occur’ and ‘landslide likely to occur’. Once trained, the models will be able to predict the most likely class for the prevailing data. Finally compared two models with regards to prediction accuracy, False Acceptance Rates and False Rejection rates and This research indicates that the Artificial Neural Network could be used as a strong decision support system to predict landslides efficiently and effectively than Hidden Markov Model.

Keywords: landslides, influencing factors, neural network model, hidden markov model

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Authors: Tempe S. F. Adams, Michael J. Chase, Tracey L. Rogers, Keith E. A. Leggett

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Transfrontier wildlife corridors can be successful conservation tools, connecting protected areas and reducing the impact of habitat fragmentation on mobile species. Urban wildlife corridors have been proposed as a potential mitigation tool to facilitate the passage of elephants through towns without causing conflict with urban communities. However, because such corridors are typically narrow and close to human development, wildlife (particularly large mammals) may be less likely to use them. We used remote-sensor camera traps and global positioning system collars to identify the movement patterns of African elephants Loxondonta africana through narrow, urban corridors in Botswana. The corridors were in three types of human-dominated land-use designations with varying levels of human activity: agricultural, industrial and open space recreational land. We found that elephants used the corridors within all three land-use designations and we identified, using a model selection approach, that season, time of day and rainfall were important factors in determining the presence of elephants in the corridors. Elephants moved more slowly through the narrow corridors compared with their movement patterns through broader, wide-ranging corridors. Our results indicate that urban wildlife corridors are useful for facilitating elephants to pass through urban areas.

Keywords: camera traps, conservation, human-elephant coexistence, land management, urban wildlife corridors

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Authors: R. Vidhya, M. Navamuniyammal M. Sivakumar, S. Reeta

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The unplanned urbanization affects the environment due to pollution, conditions of the atmosphere, decreased vegetation and the pervious and impervious soil surface. Considered to be a cumulative effect of all these impacts is the Urban Heat Island. In this paper, the urban heat island effect is studied for the Chennai city, TamilNadu, South India using satellite remote sensing data. LANDSAT 8 OLI and TIRS DATA acquired on 9th September 2014 were used to Land Surface Temperature (LST) map, vegetation fraction map, Impervious surface fraction, Normalized Difference Water Index (NDWI), Normalized Difference Building Index (NDBI) and Normalized Difference Vegetation Index (NDVI) map. The relationship among LST, Vegetation fraction, NDBI, NDWI, and NDVI was calculated. The Chennai city’s Urban Heat Island effect is significant, and the results indicate LST has strong negative correlation with the vegetation present and positive correlation with NDBI. The vegetation is the main factor to control urban heat island effect issues in urban area like Chennai City. This study will help in developing measures to land use planning to reduce the heat effects in urban area based on remote sensing derivatives.

Keywords: land surface temperature, brightness temperature, emissivity, vegetation index

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Authors: Ali J. Ghandour, Houssam A. Krayem, Abedelkarim A. Jezzini

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High-resolution satellite images and remote sensing can provide global information in a fast way compared to traditional methods of data collection. Under such high resolution, a road is not a thin line anymore. Objects such as cars and trees are easily identifiable. Automatic vehicles enumeration can be considered one of the most important applications in traffic management. In this paper, autonomous vehicle detection and classification approach in highway environment is proposed. This approach consists mainly of three stages: (i) first, a set of preprocessing operations are applied including soil, vegetation, water suppression. (ii) Then, road networks detection and delineation is implemented using built-up area index, followed by several morphological operations. This step plays an important role in increasing the overall detection accuracy since vehicles candidates are objects contained within the road networks only. (iii) Multi-level Otsu segmentation is implemented in the last stage, resulting in vehicle detection and classification, where detected vehicles are classified into cars and trucks. Accuracy assessment analysis is conducted over different study areas to show the great efficiency of the proposed method, especially in highway environment.

Keywords: remote sensing, object identification, vehicle and road extraction, vehicle and road features-based classification

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Authors: Majid Habib Khan, Jinwei Zhao, Xinhong Hei, Liu Jiedong, Rana Shahzad Noor, Muhammad Imran

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Deep learning is increasingly employed in image classification, yet the scarcity and high cost of labeled data for training remain a challenge. Limited samples often lead to overfitting due to biased sample distribution. This paper introduces a dynamic distribution calibration method for few-shot learning. Initially, base and new class samples undergo normalization to mitigate disparate feature magnitudes. A pre-trained model then extracts feature vectors from both classes. The method dynamically selects distribution characteristics from base classes (both adjacent and remote) in the embedding space, using a threshold value approach for new class samples. Given the propensity of similar classes to share feature distributions like mean and variance, this research assumes a Gaussian distribution for feature vectors. Subsequently, distributional features of new class samples are calibrated using a corrected hyperparameter, derived from the distribution features of both adjacent and distant base classes. This calibration augments the new class sample set. The technique demonstrates significant improvements, with up to 4% accuracy gains in few-shot classification challenges, as evidenced by tests on miniImagenet and CUB datasets.

Keywords: deep learning, computer vision, image classification, few-shot learning, threshold

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Authors: Smriti Bhatnagar

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This study aims to find if the Transit-oriented planning and development approach benefit the quality of air in neighborhoods of New Delhi. Two methodologies, namely the land use regression analysis and the Transit-oriented development index analysis, are being used to explore this relationship. Land Use Regression Analysis makes use of urban form characteristics as obtained for 33 neighborhoods in Delhi. These comprise road lengths, land use areas, population and household densities, number of amenities and distance between amenities. Regressions are run to establish the relationship between urban form variables and air quality parameters (dependent variables). For the Transit-oriented development index analysis, the Transit-oriented Development index is developed as a composite index comprising 29 urban form indicators. This index is developed by assigning weights to each of the 29 urban form data points. Regressions are run to establish the relationship between the Transit-oriented development index and air quality parameters. The thesis finds that elements of Transit-oriented development if incorporated in planning approach, have a positive effect on air quality. Roads suited for non-motorized transport, well connected civic amenities in neighbourhoods, for instance, have a directly proportional relationship with air quality. Transit-oriented development index, however, is not found to have a consistent relationship with air quality parameters. The reason could this, however, be in the way that the index has been constructed.

Keywords: air quality, land use regression, mixed-use planning, transit-oriented development index, New Delhi

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Authors: Mekamı Hayet, Bounoua Nacer, Benabderrahmane Sidahmed, Taleb Ahmed

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Pose estimation is an important task in computer vision. Though the majority of the existing solutions provide good accuracy results, they are often overly complex and computationally expensive. In this perspective, we propose the use of dimensionality reduction techniques to address the problem of facial pose estimation. Firstly, a face image is converted into one-dimensional time series using Hilbert space filling curve, then the approach converts these time series data to a symbolic representation. Furthermore, a distance matrix is calculated between symbolic series of an input learning dataset of images, to generate classifiers of frontal vs. profile face pose. The proposed method is evaluated with three public datasets. Experimental results have shown that our approach is able to achieve a correct classification rate exceeding 97% with K-NN algorithm.

Keywords: machine learning, pattern recognition, facial pose classification, time series

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