Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping.

/pdf/spatial-prediction-models-for-shallow-landslide-hazards-a-49skfsoa8p.pdf

Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

A multimedia approach to the diffusion, communication, and exploitation of Cultural Heritage (CH) is a well-established trend worldwide. Several studies demonstrate that the use of new and combined media enhances how culture is experienced. The benefit is in terms of both number of people who can have access to knowledge and the quality of the diffusion of the knowledge itself. In this regard, CH uses augmented-, virtual-, and mixed-reality technologies for different purposes, including education, exhibition enhancement, exploration, reconstruction, and virtual museums. These technologies enable user-centred presentation and make cultural heritage digitally accessible, especially when physical access is constrained. A number of surveys of these emerging technologies have been conducted; however, they are either not domain specific or lack a holistic perspective in that they do not cover all the aspects of the technology. A review of these technologies from a cultural heritage perspective is therefore warranted. Accordingly, our article surveys the state-of-the-art in augmented-, virtual-, and mixed-reality systems as a whole and from a cultural heritage perspective. In addition, we identify specific application areas in digital cultural heritage and make suggestions as to which technology is most appropriate in each case. Finally, the article predicts future research directions for augmented and virtual reality, with a particular focus on interaction interfaces and explores the implications for the cultural heritage domain.

A Survey of Augmented, Virtual, and Mixed Reality for Cultural Heritage

Statistical learning theory is the basis of support vector machine (SVM) technique. This technique in natural hazard assessment is getting extremely popular these days. It contains a training stage related to the input and desire output values. The main goal of this paper is to assess and evaluate the prediction capability of SVM technique with different kernel functions for spatial prediction of flood occurrence. Kuala Terengganu basin, Malaysia was selected as study area. To begin, a flood inventory map was produced by mapping the flood locations in the Terengganu using documentary sources and field survey. The flood inventory was partitioned into training and testing datasets through random selection. The spatial database was constructed using various flood conditioning factors: altitude, slope, curvature, stream power index (SPI), topographic wetness index (TWI), distance from the river, geology, land use/cover (LULC), soil, and surface runoff. Four SVM kernel types such as linear (LN), polynomial (PL), radial basis function (RBF), and sigmoid (SIG) were utilized to check the robustness of the SVM model. Consequently, four flood susceptibility maps were created. In order to examine the efficiency of the SVM model, a probabilistic based frequency ratio (FR) model was applied and compared with the SVM outcomes. An area under the curve (AUC) method was used to validate the resultant flood susceptibility maps. The validation results demonstrated that the prediction rate curves for flood susceptibility maps generated by the SVM-LN, SVM-PL, SVM-RBF, and SVM-SIG were 84.63%, 83.92%, 84.97%, and 81.88% respectively. On the other hand, the prediction rate achieved by FR showed the lowest accuracy of 61.43%. To evaluate the impacts of conditioning factors on the flood susceptibility mapping, Cohen's kappa index was measured. The result demonstrated that all conditioning factors have reasonably positive influence on the flood analysis in current case study except surface runoff which decreased the accuracy of the final results. The most influential factors were altitude and slope for all kernel types. It can be concluded that SVM technique is an efficient and reliable tool in flood susceptibility assessment. The resultant flood susceptibility maps can be beneficial in flood mitigation strategies.

Flood susceptibility assessment using GIS-based support vector machine model with different kernel types

A new artificial intelligence (AI) model, called Bagging-LMT - a combination of bagging ensemble and Logistic Model Tree (LMT) - is introduced for mapping flood susceptibility. A spatial database was generated for the Haraz watershed, northern Iran, that included a flood inventory map and eleven flood conditioning factors based on the Information Gain Ratio (IGR). The model was evaluated using precision, sensitivity, specificity, accuracy, Root Mean Square Error, Mean Absolute Error, Kappa and area under the receiver operating characteristic curve criteria. The model was also compared with four state-of-the-art benchmark soft computing models, including LMT, logistic regression, Bayesian logistic regression, and random forest. Results revealed that the proposed model outperformed all these models and indicate that the proposed model can be used for sustainable management of flood-prone areas.

A novel hybrid artificial intelligence approach for flood susceptibility assessment

An easy to use ArcMap based texture analysis program for extraction of flooded areas from TerraSAR-X satellite image

Cartography from Pole to Pole

Variability in snow cover strongly influences mass budgets of glaciers, permafrost distribution, and seasonal discharge of rivers. In times of a changing climate, the spatio-temporal patterns of snow cover are of high interest. In this study, snow cover time series for the Aksu catchment in Central Tien Shan have been generated from optical remote sensing imagery. The analyses span a period between 1986 and 2013 and imbed Advanced Very High Resolution Radiometer (AVHRR) level 1b scenes, which were classified using a dichotomous decision scheme, as well as the preprocessed Moderate Resolution Imaging Spectrometer (MODIS) snow cover product. High congruence of the results could be achieved in spite of different sensors involved. However, a small bias appears especially at high elevations. The results from 2000 to 2013 reveal that snow accumulation begins in October and melting starts in March. Above an elevation of around 5200 m a.s.l., permanent snow cover can be expected, which is mirrored by a zonal mean of more than 85% of snow for the whole period 1986–2013. Anomalies are very indicative and reveal a high interannual variability of snow cover in terms of quantity and spatial distribution. Change detection of snow cover probability (SCP) shows a slight decrease in lower altitudes up to 4000 m a.s.l. and an opposite trend above. However, the negative trends are not significant. Significant gradients have been found only at high elevations where the two data sources could not perfectly be harmonized. Comparisons with climatic variables show a similar temporal behavior of SCP and temperatures.

Snow Cover Distribution in the Aksu Catchment (Central Tien Shan) 1986–2013 Based on AVHRR and MODIS Data

Two current projects of the authors try to approach cultural heritage landscapes from both cultural sciences and geography through a combination of customised geo-information (GIS) and visualisation/presentation technology. In excess of a mere academic use, easyto- handle virtual 3D web presentations may contribute to knowledge, esteem, commemoration and preservation. The examples relate to pre-historic Scythian burial sites in the South-Siberian Altay Mountains ("Uch Enmek") as well as to a "virtual memorial" of contemporary history ("GEPAM"), a chapter of Jewish prosecution in the "Third Reich", which historically connects the town of Dresden with the Czech Terezin (Theresienstadt). It is common knowledge that a profound understanding of (pre-)historic artefacts and places may reflect a larger environment as well as an individual geographic setting. Coming from this background, the presented projects try to find technical solutions. They start from GIS models and aim at customised interactive presentations of 3D models. In using the latter a widely-spanned public is invited to a land- or townscape of specific cultural importance. The geographic space is thought to work as a door to a repository of educational exhibits under the umbrella of a web application. Within this concept a landscape/townscape also accounts for the time dimension in different scales (time of construction/operation versus actual state, and in sense of a season and time of the day as a principal modulator of visual perception of space).

/pdf/presenting-cultural-heritage-landscapes-from-gis-via-3d-2bjtqqhfhl.pdf

Presenting cultural heritage landscapes – from gis via 3d models to interactive presentation frameworks

3D scenes within all media indicate a societal preference shift toward 3D presentations. In spite of wide data availability and successful standardization efforts in 3D modeling, it is not a standard practice to offer large-scale topographic references to the end user in the form of 3D models. This motivates to propose automated strategies for the generation of closed 3D representations of a complete urban landscape, which at the same time account for capabilities of consumer-class devices. The campus of Dresden University of Technology served as a test case. The final appearance of the 3D model will be steered directly by the original geographic information system (GIS) data source. Such a “schematic model” displays source classes and attributes by nonphotorealistic rendering. A tested generic workflow can be presented, which programmatically integrates attributed 2D GIS entities and digital elevation model data, checks for compliancy with consistency rules and generates a slim geometric model. Only deta...

On strategies and automation in upgrading 2D to 3D landscape representations

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