Temporal or spatio-temporal sequential pattern discovery is a well-recognized important problem in many domains like seismology, criminology, and finance. The majority of the current approaches are based on candidate generation which necessitates parameter tuning, namely, definition of a neighborhood, an interest measure, and a threshold value to evaluate candidates. However, their performance is limited as the success of these methods relies heavily on parameter settings. In this paper, we propose an algorithm which uses a nonparametric stochastic de-clustering procedure and a multivariate Hawkes model to define triggering relations within and among the event types and employs the estimated model to extract significant triggering patterns of event types. We tested the proposed method with real and synthetic data sets exhibiting different characteristics. The method gives good results that are comparable with the methods based on candidate generation in the literature.

Archaeological studies using computer vision based analysis methods on thermal imageries mainly lack an important stage of pointwise detection of artifact positions, which is needed for the automation of the system in a generic application. In this paper, we propose a pointwise detection method working in the thermal range of hyperspectral band for archaeological artifacts. The proposed method first optimally converts a given 3D hyperspectral image of the searched scene into a 2D brightness-temperature map by minimizing the mean square error (MSE) between the spectral radiance of a pixel and the Planck curves generated at different temperatures. The local maxima and minima are then found on the resulting 2D map as the candidate points. Finally, a score assignment is performed on the candidate points by using their temperature difference with respect to their neighborhood. The results on the thermal images taken from a test scene have indicated a good correlation between the extrema points and artifact positions.

This paper presents a vegetation detection application with semi-supervised target detection using hyperspectral unmixing and segmentation algorithms. The method firstly compares the known target spectral signature from a generic source such as a spectral library with each pixel of hyperspectral data cube employing Spectral Angle Mapper (SAM) algorithm. The pixel(s) with the best match are assumed to be the most likely target vegetation locations. The regions around these potential target locations are further analyzed via hyperspectral unmixing techniques to obtain the real spectra in the image. The abundance fractions are evaluated so as to compare the algorithm performance with those of other methods. As a post processing technique meanshift segmentation algorithm utilized.

The aim of this study is to the evaluate land use and the land cover changes of an abandoned coal mine in Central Anatolia. The mining activity in the region was started at 1987 and after working for 18 years, all the rights of the mine was passed to an another company which after a while the came up with no coal production since its concession was cancelled on February 2008. Unfortunately, during the life of the mine, there were no mine closure and reclamation activities on the field. There was only a limited afforestation work on the dump site performed by provincial special administration. As there were no appropriate closure and reclamation implementations during and after the mining activity, major environmental impacts detected on the mine field. This study investigates the land use land cover change due to mining operations between 1987 and 2000. Landsat ETM+ imagery is selected. Maximum Likelihood estimation based on the Bayesian theorem is used for classification purposes. Change maps are produced to observe the differences in land use and land cover changes in the study area in comparison with the baseline of the region.

The purpose of this study was to investigate the capabilities of different landslide susceptibility methods by comparing their results statistically and spatially to select the best method that portrays the susceptibility zones for the Ulus district of the Bartın province (northern Turkey). Susceptibility maps based on spatial regression (SR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR) method, and artificial neural network method (ANN) were generated, and the effect of each geomorphological parameter was determined. The landslide inventory map digitized from previous studies was used as a base map for landslide occurrence. All of the analyses were implemented with respect to landslides classified as rotational, active, and deeper than 5 m. Three different sets of data were used to produce nine explanatory variables (layers). The study area was divided into grids of 90 m × 90 m, and the ‘seed cell’ technique was applied to obtain statistically balanced population distribution over landslide inventory area. The constructed dataset was divided into two datasets as training and test. The initial assessment consisted of multicollinearity of explanatory variables. Empirical information entropy analysis was implemented to quantify the spatial distribution of the outcomes of these methods. Results of the analyses were validated by using success rate curve (SRC) and prediction rate curve (PRC) methods. Additionally, statistical and spatial comparisons of the results were performed to determine the most suitable susceptibility zonation method in this large-scale study area. In accordance with all these comparisons, it is concluded that ANN was the best method to represent landslide susceptibility throughout the study area with an acceptable processing time.

The traditional approach of soil mapping is time consuming and requires large amount of investments, especially for large scale areas, whereas the reflectance spectrometry for assessing the spectral behavior of soil has been an alternative [13]. This study aims to predict the moisture level in soil and significant wavelengths pointing the moisture existence with the help of satellite imageries and spectroscopic data. For that purpose, spectral profiles obtained from Advanced Spaceborn Thermal Emission and Reflection (ASTER) and Worldview 2 (Wv-2) imageries are utilized in the analysis coupled with the spectroscopic data acquired with ASD spectrometer instrument. The study reveals the fact that ASTER has a superior capability to predict the moisture content over Wv-2, also indicating the capability of Wv-2 having a narrow region in electromagnetic spectrum as a cost & time efficient tool.

The increasing availability of Earth Observation (EO) technologies has provided new opportunities for a wide range of urban applications, such as mapping and monitoring of the urban environment, socio-economic estimations, characterization of urban climate, analysis of regional and global impacts and urban security and emergency preparedness. However, a gap exists between the research-focused results offered by the urban EO community and the application of these data and products by urban planners and decision makers. Hence, the main objective of the GEOURBAN (ExploitinG Earth Observation in sUstainable uRBan plAnning & maNagement) project is to bridge this gap by demonstrating the ability of current and future EO systems to depict parameters of urban structure and urban environmental quality at detailed level. This paper introduces the conceptual approach towards the integration of EO-based geo-information into urban and spatial planning and presents and discusses first results of the indicators developed and evaluated by using remote sensing data.

The term physical accessibility has long been used by geographers, economists, and urban planners and reflects the relative ease of access to/from several urban/rural services by considering the traveling costs. Numerous accessibility measures, ranging from simple to sophisticated, can be observed in the geographical information systems (GIS)-based accessibility modeling literature. However, these measures are generally calculated from a constant catchment boundary (a most likely or average catchment boundary) based on constant deterministic transportation costs. This is one of the fundamental shortcomings of the current GIS-based accessibility modeling and creates uncertainty about the accuracy and reliability of the accessibility measures, especially when highly variable speeds in road segments are considered. The development of a new stochastic approach by using global positioning system (GPS)-based floating car data and Monte Carlo simulation (MCS) technique could enable handling the variations in transportation costs in a probabilistic manner and help to consider all possible catchment boundaries, instead of one average catchment boundary, in accessibility modeling process. Therefore, this article proposes a stochastic methodology for GIS-based accessibility modeling by using GPS-based floating car data and MCS technique. The proposed methodology is illustrated with a case study on medical emergency service accessibility in Eskisehir, Turkey. Moreover, deterministic and stochastic accessibility models are compared to demonstrate the differences between the models. The proposed model could provide better decision support for the decision-makers who are supposed to deal with accessibility, location/allocation, and service/catchment area related issues.

Finding slope units for a given watershed is an important task before analyzing landslide susceptibility. Usually, slope unit generation requires integration of several digital elevation model (DEM)-based outputs obtained from GIS and related hydrological software. Therefore, it is time consuming due to involved steps and compilation of various software outputs. In this paper a DEM-based, scale and resolution invariant, fully automatic method is proposed for creating slope units to be used in landslide susceptibility mapping.