Search results for: land cover classification

Authors: Sandeep J. Kumar

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Urbanisation is considered as the primary driver of land use and land cover change that has direct link to population and economic growth. In India, as well as in other developing countries, cities are urbanizing at an alarming rate. This unprecedented and uncontrolled urbanisation can result in urban sprawl. Due to a number of factors, urban sprawl is recognised to be a result of poor planning, inadequate policies, and poor governance. Urban sprawl may be seen as posing a threat to the development of sustainable cities. Hence, it is very essential to manage this. Planning for predicted future growth is critical to avoid the negative effects of urban growth at the local and regional levels. Thiruvananthapuram being the capital city of Kerala is a city of economic success, challenges, and opportunities. Urbanization trends in the city have paved way for Urban Sprawl. This thesis aims to formulate a framework to combat the emerging urban sprawl in the city of Thiruvananthapuram. For that, the first step was to quantify trends of urban growth in Thiruvananthapuram city using Geographical Information System(GIS) and remote sensing techniques. The technique and results obtained in the study are extremely valuable in analysing the land use changes. Secondly, these change in the trends were analysed through some of the critical factors that helped the study to understand the underlying issues of the existing city structure that has resulted in urban sprawl. Anticipating development trends can modify the current order. This can be productively resolved using regional and municipal planning and management strategies. Hence efficient strategies to curb the sprawl in Thiruvananthapuram city have been formulated in this study that can be considered as recommendations for future planning.

Keywords: urbanisation, urban sprawl, geographical information system(GIS), thiruvananthapuram

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Authors: Eugene Y. J. Aw, J. W. Koh, S. H. Chew, K. E. Chua, Lionel L. J. Ang, Algernon C. S. Hong, Danette S. E. Tan, Grace H. B. Foo, K. Q. Hong, L. M. Cheng, M. L. Leong

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This paper presents a novel rapid soil classification technique that combines computer vision with four-probe soil electrical resistivity method and cone penetration test (CPT), to improve the accuracy and productivity of on-site classification of excavated soil. In Singapore, excavated soils from local construction projects are transported to Staging Grounds (SGs) to be reused as fill material for land reclamation. Excavated soils are mainly categorized into two groups (“Good Earth” and “Soft Clay”) based on particle size distribution (PSD) and water content (w) from soil investigation reports and on-site visual survey, such that proper treatment and usage can be exercised. However, this process is time-consuming and labour-intensive. Thus, a rapid classification method is needed at the SGs. Computer vision, four-probe soil electrical resistivity and CPT were combined into an innovative non-destructive and instantaneous classification method for this purpose. The computer vision technique comprises soil image acquisition using industrial grade camera; image processing and analysis via calculation of Grey Level Co-occurrence Matrix (GLCM) textural parameters; and decision-making using an Artificial Neural Network (ANN). Complementing the computer vision technique, the apparent electrical resistivity of soil (ρ) is measured using a set of four probes arranged in Wenner’s array. It was found from the previous study that the ANN model coupled with ρ can classify soils into “Good Earth” and “Soft Clay” in less than a minute, with an accuracy of 85% based on selected representative soil images. To further improve the technique, the soil strength is measured using a modified mini cone penetrometer, and w is measured using a set of time-domain reflectometry (TDR) probes. Laboratory proof-of-concept was conducted through a series of seven tests with three types of soils – “Good Earth”, “Soft Clay” and an even mix of the two. Validation was performed against the PSD and w of each soil type obtained from conventional laboratory tests. The results show that ρ, w and CPT measurements can be collectively analyzed to classify soils into “Good Earth” or “Soft Clay”. It is also found that these parameters can be integrated with the computer vision technique on-site to complete the rapid soil classification in less than three minutes.

Keywords: Computer vision technique, cone penetration test, electrical resistivity, rapid and non-destructive, soil classification

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Authors: Mesfin Nigussie Ibido

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The study is designed to explore the effects of land certification in securing women’s land rights of two rural villages in Robe district at Arsi Zone of Oromia regional state. The land is very critical assets for human life survival and the backbone for rural women livelihood. Equal access and control power to the land have given a chance for rural women to participate in different economic activities and improve their bargaining ability for decision making on their rights. Unfortunately, women were discriminated and marginalized from access and control of land for centuries through customary practices. However, in many countries, legal reform is used as a powerful tool for eliminating discriminatory provisions in property rights. Among other equity and efficiency concerns, the land certification program in Ethiopia attempts to address gender bias concerns of the current land-tenure system. The existed rural land policy was recognizing a women land rights and benefited by strengthened wives awareness of their land rights and contribute to the strong involvement of wives in decision making. However, harmful practices and policy implementation problems still against women do not fully exercise a provision of land rights in a different area of the country. Thus, this study is carried out to examine the effect of land certification in securing women’s land rights by eliminating the discriminatory nature of cultural abuses of study areas. Probability and non-probability sampling types were used, and the sample size was determined by using the sampling distribution of the proportion method. Systematic random sampling method was applied by taking the nth element of the sample frame. Both quantitative and qualitative research methods were applied, and survey respondents of 192 households were conducted and administering questionnaires in the quantitative method. The qualitative method was applied by interviews with focus group discussions with rural women, case stories, Village, and relevant district offices. Triangulation method was applied in data collection, data presentation and in the analysis of findings. Study finding revealed that the existence of land certification is affected by rural women positively by advancing their land rights, but still, some women are challenged by unsolved problems in the study areas. The study forwards recommendation on the existed problems or gaps to ensure women’s equal access to and control over land in the study areas.

Keywords: decision making, effects, land certification, land right, tenure security

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Authors: C. Mayr, J. Periya, A. Kariminezhad

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In this paper, we developed a learning framework for the classification of vulnerable road users (VRU) by their range-Doppler signatures. The frequency-modulated continuous-wave (FMCW) radar raw data is first pre-processed to obtain robust object range-Doppler maps per coherent time interval. The complex-valued range-Doppler maps captured from our outdoor measurements are further fed into a convolutional neural network (CNN) to learn the classification. This CNN has gone through a hyperparameter optimization process for improved learning. By learning VRU range-Doppler signatures, the three classes 'pedestrian', 'dog', and 'noise' are classified with an average accuracy of almost 95%. Interestingly, this classification accuracy holds for a combined longitudinal and lateral object trajectories.

Keywords: machine learning, radar, signal processing, autonomous driving

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Authors: A. Bensaid, T. Mostephaoui, R. Nedjai

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A considerable area of Algerian lands is threatened by the phenomenon of wind erosion. For a long time, wind erosion and its associated harmful effects on the natural environment have posed a serious threat, especially in the arid regions of the country. In recent years, as a result of increases in the irrational exploitation of natural resources (fodder) and extensive land clearing, wind erosion has particularly accentuated. The extent of degradation in the arid region of the Algerian Mecheria department generated a new situation characterized by the reduction of vegetation cover, the decrease of land productivity, as well as sand encroachment on urban development zones. In this study, we attempt to investigate the potential of remote sensing and geographic information systems for detecting the spatial dynamics of the ancient dune cords based on the numerical processing of LANDSAT images (5, 7, and 8) of three scenes 197/37, 198/36 and 198/37 for the year 2020. As a second step, we prospect the use of geospatial techniques to monitor the progression of sand dunes on developed (urban) lands as well as on the formation of sandy accumulations (dune, dunes fields, nebkha, barkhane, etc.). For this purpose, this study made use of the semi-automatic processing method for the dynamic segmentation of images with very high spatial resolution (SENTINEL-2 and Google Earth). This study was able to demonstrate that urban lands under current conditions are located in sand transit zones that are mobilized by the winds from the northwest and southwest directions.

Keywords: land development, GIS, segmentation, remote sensing

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Authors: Anung Style Bukhori, Ani Dijah Rahajoe

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Poverty poses a significant challenge in Indonesia, requiring an effective analytical approach to understand and address this issue. In this research, we applied the Naïve Bayes classification method to examine and classify poverty data in Indonesia. The main focus is on classifying data using RapidMiner, a powerful data analysis platform. The analysis process involves data splitting to train and test the classification model. First, we collected and prepared a poverty dataset that includes various factors such as education, employment, and health..The experimental results indicate that the Naïve Bayes classification model can provide accurate predictions regarding the risk of poverty. The use of RapidMiner in the analysis process offers flexibility and efficiency in evaluating the model's performance. The classification produces several values to serve as the standard for classifying poverty data in Indonesia using Naive Bayes. The accuracy result obtained is 40.26%, with a moderate recall result of 35.94%, a high recall result of 63.16%, and a low recall result of 38.03%. The precision for the moderate class is 58.97%, for the high class is 17.39%, and for the low class is 58.70%. These results can be seen from the graph below.

Keywords: poverty, classification, naïve bayes, Indonesia

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Authors: Hrishi Rakshit, Pooneh Bagheri Zadeh

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This paper investigates the performance of drone classification methods using conventional DCNN with different hyperparameters, when additional drone audio data is embedded in the dataset for training and further classification. In this paper, first a custom dataset is created using different images of drones from University of South California (USC) datasets and Leeds Beckett university datasets with embedded drone audio signal. The three well-known DCNN architectures namely, Resnet50, Darknet53 and Shufflenet are employed over the created dataset tuning their hyperparameters such as, learning rates, maximum epochs, Mini Batch size with different optimizers. Precision-Recall curves and F1 Scores-Threshold curves are used to evaluate the performance of the named classification algorithms. Experimental results show that Resnet50 has the highest efficiency compared to other DCNN methods.

Keywords: drone classifications, deep convolutional neural network, hyperparameters, drone audio signal

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Authors: Jalel Khelil

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In order to improve reliability of Gas Turbine machine especially its generator equipment, a fault diagnosis system based on fuzzy approach is proposed. Three various methods namely K-NN (K-nearest neighbors), F-KNN (Fuzzy K-nearest neighbors) and FNM (Fuzzy nearest mean) are adopted to provide the measurement of relative strength of vibration defaults. Both applications consist of two major steps: Feature extraction and default classification. 09 statistical features are extracted from vibration signals. 03 different classes are used in this study which describes vibrations condition: Normal, unbalance defect, and misalignment defect. The use of the fuzzy approaches and the classification results are discussed. Results show that these approaches yield high successful rates of vibration default classification.

Keywords: fault diagnosis, fuzzy classification k-nearest neighbor, vibration

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Authors: Bing Li, Zhi Li, Yilong Yang

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Web classification can promote the quality of service discovery and management in the service repository. It is widely used to locate developers desired services. Although traditional classification methods based on supervised learning models can achieve classification tasks, developers need to manually mark web services, and the quality of these tags may not be enough to establish an accurate classifier for service classification. With the doubling of the number of web services, the manual tagging method has become unrealistic. In recent years, the attention mechanism has made remarkable progress in the field of deep learning, and its huge potential has been fully demonstrated in various fields. This paper designs a multi-head attention graph convolutional network (MHAGCN) service classification method, which can assign different weights to the neighborhood nodes without complicated matrix operations or relying on understanding the entire graph structure. The framework combines the advantages of the attention mechanism and graph convolutional neural network. It can classify web services through automatic feature extraction. The comprehensive experimental results on a real dataset not only show the superior performance of the proposed model over the existing models but also demonstrate its potentially good interpretability for graph analysis.

Keywords: attention mechanism, graph convolutional network, interpretability, service classification, service discovery

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Authors: Bhalke D. G., Bormane D. S., Kharate G. K.

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This paper presents classification of musical instrument using machine learning techniques. The classification has been carried out using temporal, spectral, cepstral and wavelet features. Detail feature analysis is carried out using separate and combined features. Further, instrument model has been developed using K-Nearest Neighbor and Support Vector Machine (SVM). Benchmarked McGill university database has been used to test the performance of the system. Experimental result shows that SVM performs better as compared to KNN classifier.

Keywords: feature extraction, SVM, KNN, musical instruments

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Authors: Hankyu Lee, Seoung Bum Kim

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The issue with imbalance and overlapping in the class distribution becomes important in various applications of data mining. The imbalanced dataset is a special case in classification problems in which the number of observations of one class (i.e., major class) heavily exceeds the number of observations of the other class (i.e., minor class). Overlapped dataset is the case where many observations are shared together between the two classes. Imbalanced and overlapped data can be frequently found in many real examples including fraud and abuse patients in healthcare, quality prediction in manufacturing, text classification, oil spill detection, remote sensing, and so on. The class imbalance and overlap problem is the challenging issue because this situation degrades the performance of most of the standard classification algorithms. In this study, we propose a classification procedure that can effectively handle imbalanced and overlapped datasets by splitting data space into three parts: nonoverlapping, light overlapping, and severe overlapping and applying the classification algorithm in each part. These three parts were determined based on the Hausdorff distance and the margin of the modified support vector machine. An experiments study was conducted to examine the properties of the proposed method and compared it with other classification algorithms. The results showed that the proposed method outperformed the competitors under various imbalanced and overlapped situations. Moreover, the applicability of the proposed method was demonstrated through the experiment with real data.

Keywords: classification, imbalanced data with class overlap, split data space, support vector machine

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Authors: Harsha B. R., K. S. Anil Kumar

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Land characterization, classification, and soil suitability evaluation of grapes-growing pedons were assessed at fifteen taluks covering four agro climatic zones of Karnataka. Study on problems and potentials of grapes cultivation in selected agro-climatic zones was carried out along with the plant sample analysis. Twenty soil profiles were excavated as study site based on the dominance of area falling under grapes production and existing spatial variability of soils. The detailed information of profiles and horizon wise soil samples were collected to study the morphological, physical, chemical, and fertility characteristics. Climatic analysis and water retention characteristics of soils of major grapes-growing areas were also done. Based on the characterisation and classification study, it was revealed that soils of Doddaballapur (Bangalore Blue and Wine grapes), Bangalore North (GKVK Farm, Rajankunte, and IIHR Farm), Devanahalli, Magadi, Hoskote, Chikkaballapur (Dilkush and Red globe), Yelaburga, Hagari Bommanahalli, Bagalkot (UHS farm) and Indi fall under the soil order Alfisol. Vijaypur pedon of northern dry zone was keyed out as Vertisols whereas, Jamkhandi and Athani as Inceptisols. Properties of Aridisols were observed in B. Bagewadi (Manikchaman and Thompson Seedless) and Afzalpur. Soil fertility status and its mapping using GIS technique revealed that all the nutrients were found to be in adequate range except nitrogen, potassium, zinc, iron, and boron, which indicated the need for application along with organic matter to improve the SOC status. Varieties differed among themselves in yield and plant nutrient composition depending on their age, climatic, soil, and management requirements. Bangalore North (GKVK farm) and Jamkhandi are having medium soil organic carbon stocks of 6.21 and 6.55 kg m⁻³, respectively. Soils of Bangalore North (Rajankunte) were highly suitable (S1) for grapes cultivation. Under northern Karnataka, Vijayapura, B. Bagewadi, Indi, and Afzalpur vineyards were good performers despite the limitations of fertility and free lime content.

Keywords: land characterization, suitability, soil orders, soil organic carbon stock

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Authors: Isabela Romanha de Alcantara, Jose Eustaquio Ribeiro Vieira Filho, Jose Garcia Gasques

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Innovation and technology can be determinant factors to ensure agricultural and sustainable growth, as well as productivity gains. Technical change has contributed considerably to supply agricultural expansion in Brazil. This agricultural growth could be achieved by incorporating more land or capital. If capital is the main source of agricultural growth, it is possible to increase production per unit of land. The objective of this paper is to estimate: 1) total factor productivity (TFP), which is measured in terms of the rate of output per unit of input; and 2) the land-saving effect (LSE) that is the amount of land required in the case that yield rate is constant over time. According to this study, from 1990 to 2019, it appears that 87 percent of Brazilian agriculture product growth comes from the gains of productivity; the rest of 13 percent comes from input growth. In the same period, the total LSE was roughly 400 Mha, which corresponds to 47 percent of the national territory. These effects reflect the greater efficiency of using productive factors, whose technical change has allowed an increase in agricultural production based on productivity gains.

Keywords: agriculture, land-saving effect, livestock, productivity

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Authors: V. R. Venugopal

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Human being evolved 200,000 years ago in an area which is now the Sahara desert and lived all along in the northern part of Africa. It was around 10,000 to15,00 years that he moved out of Africa. Various ancient civilizations – mainly the Egyptian, Mesopotamian, Indus valley and the Chinese yellow river valley civilizations - developed and perished till the beginning of the Christian era. Strangely the regions where all these civilizations flourished are no deserts. After the ancient civilizations the two major religions of the world the Christianity and Islam evolved. These too evolved in the regions of Jerusalem and Mecca which are now in the deserts of the present Israel and Saudi Arabia. Human activity since ancient age right from his origin was in areas which are now deserts. This is only because wherever Man lived in large numbers he has turned them into deserts. Unfortunately, this is not the case with the ancient days alone. Over the last 500 years the forest cover on the earth is reduced by 80 percent. Even more currently Just over the last forty decades human population has doubled but the number of bugs, beetles, worms and butterflies (micro fauna) have declined by 45%. Deforestation and defaunation are the first signs of desertification and Desertification is a process parallel to the extinction of life. There is every possibility that soon most of the earth will be in deserts. This writer has been involved in the process of forestation and increase of fauna as a profession since twenty years and this is a report of his efforts made in the process, the results obtained and concept generated to revert the ongoing desertification of this earth. This paper highlights how desertification can be reverted by applying these basic principles. 1) Man is not owner of this earth and has no right destroy vegetation and micro fauna. 2) Land owner shall not have the freedom to do anything that he wishes with the land. 3) The land that is under agriculture shall be reduced at least by a half. 4) Irrigation and modern technology shall be used for the forest growth also. 5) Farms shall have substantial permanent vegetation and the practice of all in all out shall stop.

Keywords: desertification, extinction, micro fauna, reverting

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Authors: Vo Ngoc Phu, Phan Thi Tuoi

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We have explored different methods of improving the accuracy of sentiment classification. The sentiment orientation of a document can be positive (+), negative (-), or neutral (0). We combine five dictionaries from [2, 3, 4, 5, 6] into the new one with 21137 entries. The new dictionary has many verbs, adverbs, phrases and idioms, that are not in five ones before. The paper shows that our proposed method based on the combination of Term-Counting method and Enhanced Contextual Valence Shifters method has improved the accuracy of sentiment classification. The combined method has accuracy 68.984% on the testing dataset, and 69.224% on the training dataset. All of these methods are implemented to classify the reviews based on our new dictionary and the Internet Movie data set.

Keywords: sentiment classification, sentiment orientation, valence shifters, contextual, valence shifters, term counting

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Authors: Sri E. Wati, D. Roswidyatmoko, N. Maslahatun, Gunawan, Andhika B. Taji

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Natural resource is the main sources to fulfill human needs. Its utilization must consider not only human prosperity but also sustainability. Balance of natural resources is a tool to manage natural wealth and to control land use change. This tool is needed to organize land use planning as stated on spatial plan in a certain region. Balance of natural resources can be calculated by comparing two-series of natural resource data obtained at different year. In this case, four years data period of land and forest were used (2010 and 2014). Land use data were acquired through satellite image interpretation and field checking. By means of GIS analysis, its result was then assessed with land use plan. It is intended to evaluate whether existing land use is suitable with land use plan. If it is improper, what kind of efforts and policies must be done to overcome the situation. Subosukawonosraten is rapid developed areas in Central Java Province. This region consists of seven regencies/cities which are Sukoharjo Regency, Boyolali Regency, Surakarta City, Karanganyar Regency, Wonogiri Regency, Sragen Regency, and Klaten Regency. This region is regarding to several former areas under Karasidenan Surakarta and their location is adjacent to Surakarta. Balance of forest resources show that width of forest area is not significantly changed. Some land uses within the area are slightly changed. Some rice field areas are converted into settlement (0.03%) whereas water bodies become vacant areas (0.09%). On the other hand, balance of land resources state that there are many land use changes in this region. Width area of rice field decreases 428 hectares and more than 50% of them have been transformed into settlement area and 11.21% is converted into buildings such as factories, hotels, and other infrastructures. It occurs mostly in Sragen, Sukoharjo, and Karanganyar Regency. The results illustrate that land use change in this region is mostly influenced by increasing of population number. Some agricultural lands have been converted into built-up area since demand of settlement, industrial area, and other infrastructures also increases. Unfortunately, recent utilization of more than a half of total area is not appropriate with land use plan declared in spatial planning document. It means, local government shall develop a strict regulation and law enforcement related to any violation in land use management.

Keywords: balance, forest, land, spatial plan

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Authors: E. Farg, M. M. El-Sharkawy, M. S. Mostafa, S. M. Arafat

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Mapping water stress provides important baseline data for sustainable agriculture. Recent developments in the new Sentinel-1 data which allow the acquisition of high resolution images and varied polarization capabilities. This study was conducted to detect and quantify vegetation water content from canopy backscatter for extracting spatial information to encourage drought mapping activities throughout new reclaimed sandy soils in western Nile delta, Egypt. The performance of radar imagery in agriculture strongly depends on the sensor polarization capability. The dual mode capabilities of Sentinel-1 improve the ability to detect water stress and the backscatter from the structure components improves the identification and separation of vegetation types with various canopy structures from other features. The fieldwork data allowed identifying of water stress zones based on land cover structure; those classes were used for producing harmonious water stress map. The used analysis techniques and results show high capability of active sensors data in water stress mapping and monitoring especially when integrated with multi-spectral medium resolution images. Also sub soil drip irrigation systems cropped areas have lower drought and water stress than center pivot sprinkler irrigation systems. That refers to high level of evaporation from soil surface in initial growth stages. Results show that high relationship between vegetation indices such as Normalized Difference Vegetation Index NDVI the observed radar backscattering. In addition to observational evidence showed that the radar backscatter is highly sensitive to vegetation water stress, and essentially potential to monitor and detect vegetative cover drought.

Keywords: canopy backscatter, drought, polarization, NDVI

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Authors: Valerie Triggs, Michele Sorensen

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As two university professors in postsecondary faculties of social work and education, we have observed that students often recognize the importance of learning facts about colonization but have difficulty grappling with how they themselves might be implicated in reconciliation or how they might respond to these facts in meaningful ways. The detachment observed between students and factual information results in the initiation of a research study centered around an approach to teaching the course. This involved transitioning its pedagogical format to embrace a/r/tographic methods of teaching, learning, and inquiry. By taking seriously the arguments of various Indigenous scholars for learning from the land and by working alongside traditional Indigenous knowledge, we chose to engage a speculative approach to course design and teaching, which actually used the land as one of the course texts. We incorporated art practices that involved connecting bodies with land as well as using land materials in various creative and aesthetic projects while being informed by Medicine Keepers, Indigenous and settler artists, and knowledge-keeper helpers. In this study, we share some of the unanticipated themes that arose when students began to allow land and artmaking, both aesthetically and intuitively, through both joy and sorrow, to affect a reimagining and repositioning of selves and relations. We found that time and engagement with land and art began to build more empathic understanding and foster personal and professional practices grounded in respect, relevance, reciprocity, and responsibility.

Keywords: reconciliation, decolonization, artmaking, respect

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Authors: Faisal Alshuwaier, Ali Areshey

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Text mining techniques are generally applied for classifying the text, finding fuzzy relations and structures in data sets. This research provides plenty text mining capabilities. One common application is text classification and event extraction, which encompass deducing specific knowledge concerning incidents referred to in texts. The main contribution of this paper is the clarification of a concept graph generation mechanism, which is based on a text classification and optimal fuzzy relationship extraction. Furthermore, the work presented in this paper explains the application of fuzzy relationship extraction and branch and bound method to simplify the texts.

Keywords: extraction, max-prod, fuzzy relations, text mining, memberships, classification, memberships, classification

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Authors: Rui Liu, Klaus Greve

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The navigation information is a kind of dynamic geographic information, and the navigation information system is a kind of special geographic information system. At present, there are many researches on the application of centralized management and cross-integration application of basic geographic information. However, the idea of information integration and sharing is not deeply applied into the research of navigation information service. And the imperfection of navigation target coordination and navigation information sharing mechanism under certain navigation tasks has greatly affected the reliability and scientificity of navigation service such as path planning. Considering this, the project intends to study the multi-source information fusion and multi-objective united navigation information interaction mechanism: first of all, investigate the actual needs of navigation users in different areas, and establish the preliminary navigation information classification and importance level model; and then analyze the characteristics of the remote sensing and GIS vector data, and design the fusion algorithm from the aspect of improving the positioning accuracy and extracting the navigation environment data. At last, the project intends to analyze the feature of navigation information of the land, sea and air navigation targets, and design the united navigation data standard and navigation information sharing model under certain navigation tasks, and establish a test navigation system for united navigation simulation experiment. The aim of this study is to explore the theory of united navigation service and optimize the navigation information service model, which will lay the theory and technology foundation for the united navigation of land, sea and air targets.

Keywords: information fusion, united navigation, dynamic path planning, navigation information visualization

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Authors: Mosses E. Lufuke

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This paper attempted to investigate whether there is Granger-causality running from trade to environment as evidenced in the changing climatic condition and land degradation. Using Tanzania as the reference, VAR-Granger-causality test was employed to rationalize the conundrum of causal-effect relationship between trade and environment. The changing climatic condition, as the proxy of both nitrous oxide emissions (in thousand metric tons of CO₂ equivalent) and land degradation measured by the size of arable land were tested against trade using both exports and imports variables. The result indicated that neither of the trade variables Granger-cause the variability on gas emissions and arable land size. This suggests the possibility that all trade concerns in relation to environment to have been internalized in domestic policies to offset any likely negative consequence.

Keywords: environment, growth, impact, trade

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Authors: Bouzidi Yassine

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This experimental work was aimed to characterize the porosity of the concrete cover zone using the capillary absorption test, and establish the links between open porosity characterized by the initial absorption, the compressive strength and carbonation depth. Eight formulations of workability similar made from ordinary Portland cement (CEM I 42.5) and a compound cement (CEM II/B 42.5) four of each type are studied. The results allow us to highlight the effect of the cement type. Indeed, concretes-based cement CEM II/B 42.5 carbonatent approximately faster than concretes-based cement CEM I 42.5. This effect is attributed in part to the lower content of portlandite Ca(OH)2 of concretes-based cement CEM II/B 42.5, but also the impact of the cement type on the open porosity of the cover concrete. The open porosity of concretes-based cement CEM I 42.5 is lower than that of concretes-based cement CEM II/B 42.5. The carbonation depth is a decreasing function of the compressive strength at 28 days and increases with the initial absorption. Through the results obtained, correlations between the quantity of water absorbed in 1 h, the carbonation depth at 180 days and the compressive strength at 28 days were performed in an acceptable manner.

Keywords: initial absorption, cover concrete, compressive strength, carbonation depth

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Authors: Yingxiao Ye, Christopher Doehring, Angelos Georghiou, Hugh Robinson, Phebe Vayanos

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Human development is a threat to biodiversity, and conservation organizations (COs) are purchasing land to protect areas for biodiversity preservation. However, COs have limited budgets and thus face hard prioritization decisions that are confounded by uncertainty in future human land use. This research proposes a data-driven sequential planning model to help COs choose land parcels that minimize the uncertain human impact on biodiversity. The proposed model is robust to uncertain development, and the sequential decision-making process is adaptive, allowing land purchase decisions to adapt to human land use as it unfolds. The cellular automata model is leveraged to simulate land use development based on climate data, land characteristics, and development threat index from NASA Socioeconomic Data and Applications Center. This simulation is used to model uncertainty in the problem. This research leverages state-of-the-art techniques in the robust optimization literature to propose a computationally tractable reformulation of the model, which can be solved routinely by off-the-shelf solvers like Gurobi or CPLEX. Numerical results based on real data from the Jaguar in Central and South America show that the proposed method reduces conservation loss by 19.46% on average compared to standard approaches such as MARXAN used in practice for biodiversity conservation. Our method may better help guide the decision process in land acquisition and thereby allow conservation organizations to maximize the impact of limited resources.

Keywords: data-driven robust optimization, biodiversity conservation, uncertainty simulation, adaptive sequential planning

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Authors: Ishrath, Tapas Kumar Chatterjee

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The present study aims developing interpretation and evaluation to integrate various data types for management of existing water resources for sustainable use. Proper study should be followed based on the geomorphology of the area. Thematic maps such as lithology, base map, land use/land cover, geomorphology, drainage and lineaments maps are prepared to study the area by using area toposheet, IRS P6 and LISIII Satellite imagery. These thematic layers are finally integrated by using Arc GIS, Arc View, and software to prepare a ground water potential zones map of the study area. In this study, an integrated approach involving remote sensing and GIS techniques has successfully been used in identifying groundwater potential zones in the study area to classify them as good, moderate and poor. It has been observed that Pediplain shallow (PPS) has good recharge, Pediplain moderate (PPM) has moderately good recharge, Pediment Inselberg complex (PIC) has poor recharge and Inselberg (I) has no recharge. The study has concluded that remote sensing and GIS techniques are very efficient and useful for identifying ground water potential zones.

Keywords: satellite remote sensing, GIS, ground water potential zones, Miyapur

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Authors: Sisi Xia, Dezhuan Tao, Junyan Yang, Weiting Xiong

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Under the background of Chinese urbanization changing from incremental development to stock development, the completion of urban public service facilities is essential to urban spatial quality. As public services facilities is a huge and complicated system, clarifying the various types of internal rules associated with the land market price is key to optimizing spatial layout. This paper takes Wuxi City as a representative sample location and establishes the digital analysis platform using urban price and several high-precision big data acquisition methods. On this basis, it analyzes the coupling relationship between different public service categories and land price, summarizing the coupling patterns of urban public facilities distribution and urban land price fluctuations. Finally, the internal mechanism within each of the two elements is explored, providing the reference of the optimum layout of urban planning and public service facilities.

Keywords: public service facilities, land price, urban spatial morphology, big data

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Authors: Rajkumar Kolangarakandy

Abstract:

Feature extraction and selection is the primary part of any mammogram classification algorithms. The choice of feature, attribute or measurements have an important influence in any classification system. Discrete Wavelet Transformation (DWT) coefficients are one of the prominent features for representing images in frequency domain. The features obtained after the decomposition of the mammogram images using wavelet transformations have higher dimension. Even though the features are higher in dimension, they were highly correlated and redundant in nature. The dimensionality reduction techniques play an important role in selecting the optimum number of features from the higher dimension data, which are highly correlated. PCA is a mathematical tool that reduces the dimensionality of the data while retaining most of the variation in the dataset. In this paper, a multilevel classification of mammogram images using reduced discrete wavelet transformation coefficients and lazy classifiers is proposed. The classification is accomplished in two different levels. In the first level, mammogram ROIs extracted from the dataset is classified as normal and abnormal types. In the second level, all the abnormal mammogram ROIs is classified into benign and malignant too. A further classification is also accomplished based on the variation in structure and intensity distribution of the images in the dataset. The Lazy classifiers called Kstar, IBL and LWL are used for classification. The classification results obtained with the reduced feature set is highly promising and the result is also compared with the performance obtained without dimension reduction.

Keywords: PCA, wavelet transformation, lazy classifiers, Kstar, IBL, LWL

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Authors: Sunday Nathaniel Obasi, Chiamak Chinasa Obasi

Abstract:

Soil characterization and classification provide the basic information necessary to create a functional evaluation and soil classification schemes. Fertility capability classification (FCC) on the other hand is a technical system that groups the soils according to kinds of problems they present for management of soil physical and chemical properties. This research was carried out in Ebonyi state, southeastern Nigeria, which is an agrarian state and a leading rice producing part of southeastern Nigeria. In order to maximize the soil and enhance the productivity of rice in Ebonyi soils, soil classification, and fertility classification information need to be supplied. The state was grouped into three locations according to their agricultural zones namely; Ebonyi north, Ebonyi central and Ebonyi south representing Abakaliki, Ikwo and Ivo locations respectively. Major rice growing areas of the soils were located and two profile pits were sunk in each of the studied zones from which soils were characterized, classified and fertility capability classification (FCC) developed. Soil classification was done using United State Department of Agriculture (USDA) Soil Taxonomy and correlated with World Reference Base for soil resources. Results obtained classified Abakaliki 1 and Abakaliki 2 as Typic Fluvaquents (Ochric Fluvisols). Ikwo 1 was classified as Vertic Eutrudepts (Eutric Vertisols) while Ikwo 2 was classified as Typic Eutrudepts (Eutric Cambisols). Ivo 1 and Ivo 2 were both classified as Aquic Eutrudepts (Gleyic Leptosols). Fertility capability classification (FCC) revealed that all studied soils had mostly loamy topsoils and subsoils except Ikwo 1 with clayey topsoil. Limitations encountered in the studied soils include; dryness (d), low ECEC (e), low nutrient capital reserve (k) and water logging/ anaerobic condition (gley). Thus, FCC classifications were Ldek for Abakaliki 1 and 2, Ckv for Ikwo 1, LCk for Ikwo 2 while Ivo 1 and 2 were Legk and Lgk respectively.

Keywords: soil classification, soil fertility, limitations, modifiers, Southeastern Nigeria

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Authors: Bryan Crompton, Daniel Giger, Tanay Mehta, Apurva Mody

Abstract:

The task of classifying radio frequency (RF) signals has seen recent success in employing deep neural network models. In this work, we present a combined signal processing and machine learning approach to signal classification for cochannel anomalous signals. The power spectral density and cyclostationary signal processing features of a captured signal are computed and fed into a neural net to produce a classification decision. Our combined signal preprocessing and machine learning approach allows for simpler neural networks with fast training times and small computational resource requirements for inference with longer preprocessing time.

Keywords: signal processing, machine learning, cyclostationary signal processing, signal classification

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Authors: J. K. Alhassan, S. A. Lawal

Abstract:

This work is on decision tree-based classification for the disbursement of scholarship. Tree-based data mining classification technique is used in other to determine the generic rule to be used to disburse the scholarship. The system based on the defined rules from the tree is able to determine the class (status) to which an applicant shall belong whether Granted or Not Granted. The applicants that fall to the class of granted denote a successful acquirement of scholarship while those in not granted class are unsuccessful in the scheme. An algorithm that can be used to classify the applicants based on the rules from tree-based classification was also developed. The tree-based classification is adopted because of its efficiency, effectiveness, and easy to comprehend features. The system was tested with the data of National Information Technology Development Agency (NITDA) Abuja, a Parastatal of Federal Ministry of Communication Technology that is mandated to develop and regulate information technology in Nigeria. The system was found working according to the specification. It is therefore recommended for all scholarship disbursement organizations.

Keywords: classification, data mining, decision tree, scholarship

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Authors: Wanhyun Cho, Soonja Kang, Sangkyoon Kim, Soonyoung Park

Abstract:

In this paper, we propose a novel inference algorithm for the multi-class Gaussian process classification model that can be used in the field of human behavior recognition. This algorithm can drive simultaneously both a posterior distribution of a latent function and estimators of hyper-parameters in a Gaussian process classification model with multi-class. Our algorithm is based on the Laplace approximation (LA) technique and variational EM framework. This is performed in two steps: called expectation and maximization steps. First, in the expectation step, using the Bayesian formula and LA technique, we derive approximately the posterior distribution of the latent function indicating the possibility that each observation belongs to a certain class in the Gaussian process classification model. Second, in the maximization step, using a derived posterior distribution of latent function, we compute the maximum likelihood estimator for hyper-parameters of a covariance matrix necessary to define prior distribution for latent function. These two steps iteratively repeat until a convergence condition satisfies. Moreover, we apply the proposed algorithm with human action classification problem using a public database, namely, the KTH human action data set. Experimental results reveal that the proposed algorithm shows good performance on this data set.

Keywords: bayesian rule, gaussian process classification model with multiclass, gaussian process prior, human action classification, laplace approximation, variational EM algorithm

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