Abstract: The aim of this work was to test a methodology able to generate spatial-temporal maps that can synthesize simultaneously the trends of distinct hydrochemical indicators in an old radium-uranium tailings dam deposit. Multidimensionality reduction derived from principal component analysis and subsequent data aggregation derived from clustering analysis allow to identify distinct hydrochemical behavioral profiles and generate synthetic evolutionary hydrochemical maps.
Abstract: Recent development of AI and edge computing plays a critical role to capture meaningful events such as detection of an unattended bag. One of the core problems is re-identification across multiple CCTVs. Immediately following the detection of a meaningful event is to track and trace the objects related to the event. In an extensive environment, the challenge becomes severe when the number of CCTVs increases substantially, imposing difficulties in achieving high accuracy while maintaining real-time performance. The algorithm that re-identifies cross-boundary objects for extensive tracking is referred to Extensive Re-Identification, which emphasizes the issues related to the complexity behind a great number of CCTVs. The Spatial-Temporal Awareness approach challenges the conventional thinking and concept of operations which is labor intensive and time consuming. The ability to perform Extensive Re-Identification through a multi-sensory network provides the next-level insights – creating value beyond traditional risk management.
Abstract: Metropolitan areas need urban transformation and urban renewal in terms of their internal Dynamics. Since 1980, the İstanbul Metropolitan area has been started to urban growth, while the population was increasing and it has brought together masses that have different lifestyles and cultures. Commercial and residential areas' spatial needs and decisions are affected by these different lifestyles. As the terms shopping mall and commercial Street became widespread, consumption trends had changed depending on the socio-economic characteristics of the people. Increase in demand for these areas, the number of shopping centers has increased, while the shopping streets started to be as effective as the shopping centers and have been pedestrianized. In this article, the change in commercial area site selection by the dynamics of the population will be examined in cities that diverged from spatial-temporal limitations. In the study, the analysis of multilayered data using geographic information systems (GIS) will be used as a method. With this method, a more synthesistic approach will be introduced with the collection editing, querying, and analysis of geographical data in computer-based systems. While conducting this analysis, Alemdağ Street in the Ümraniye district of İstanbul, where a pedestrian decision was made, will be based on and the change in the commercial and residential functions before and after the pedestrianization decision will be evaluated.
Abstract: In conjunction with the problem of the target selection on a clutter background, the analysis of the scanning rate influence on the spatial-temporal signal structure, the generalized multivariate correlation function and the quality of the resolution with the increase pulse repetition frequency is made. The possibility of the object space-distance resolution, which is conditioned by the range-to-angle conversion with an increased scanning rate, is substantiated. The calculations for the real cylindrical array at high scanning rate are presented. The high scanning rate let to get the signal to noise improvement of the order of 10 dB for the space-time signal processing.
Abstract: Spatial, Temporal, and Spectral Resolution (STSR) are three key characteristics of Earth observation satellite sensors; however, any single satellite sensor cannot provide Earth observations with high STSR simultaneously because of the hardware technology limitations of satellite sensors. On the other hand, a conflicting circumstance is that the demand for high STSR has been growing with the remote sensing application development. Although image fusion technology provides a feasible means to overcome the limitations of the current Earth observation data, the current fusion technologies cannot enhance all STSR simultaneously and provide high enough resolution improvement level. This study proposes a Hybrid Spatial-Temporal-Spectral image Fusion Model (HSTSFM) to generate synthetic satellite data with high STSR simultaneously, which blends the high spatial resolution from the panchromatic image of Landsat-8 Operational Land Imager (OLI), the high temporal resolution from the multi-spectral image of Moderate Resolution Imaging Spectroradiometer (MODIS), and the high spectral resolution from the hyper-spectral image of Hyperion to produce high STSR images. The proposed HSTSFM contains three fusion modules: (1) spatial-spectral image fusion; (2) spatial-temporal image fusion; (3) temporal-spectral image fusion. A set of test data with both phenological and land cover type changes in Beijing suburb area, China is adopted to demonstrate the performance of the proposed method. The experimental results indicate that HSTSFM can produce fused image that has good spatial and spectral fidelity to the reference image, which means it has the potential to generate synthetic data to support the studies that require high STSR satellite imagery.
Abstract: Historically, wetlands in the United States have been lost due to agriculture, anthropogenic activities, and rapid urbanization along the coast. Such losses of wetlands have resulted in high flooding risk for coastal communities over the period of time. In addition, alteration of wetlands via the Section 404 Clean Water Act permits can increase the flooding risk to future hurricane events, as the cumulative impact of this program is poorly understood and under-accounted. Further, recovery after hurricane events is acting as an encouragement for new development and reconstruction activities by converting wetlands under the wetland alteration permitting program. This study investigates the degree to which hurricane recovery activities in coastal communities are undermining the ability of these places to absorb the impacts of future storm events. Specifically, this work explores how and to what extent wetlands are being affected by the federal permitting program post-Hurricane Ike in 2008. Wetland alteration patterns are examined across three counties (Harris, Galveston, and Chambers County) along the Texas Gulf Coast over a 10-year time period, from 2004-2013 (five years before and after Hurricane Ike) by conducting descriptive spatial analyses. Results indicate that after Hurricane Ike, the number of permits substantially increased in Harris and Chambers County. The vast majority of individual and nationwide type permits were issued within the 100-year floodplain, storm surge zones, and areas damaged by Ike flooding, suggesting that recovery after the hurricane is compromising the ecological resiliency on which coastal communities depend. The authors expect that the findings of this study can increase awareness to policy makers and hazard mitigation planners regarding how to manage wetlands during a long-term recovery process to maintain their natural functions for future flood mitigation.
Abstract: The aim of the paper is to analyze the characteristics of winter fog in terms of its trend and spatial-temporal variability over Indo-Gangetic plains. The study reveals that during last four and half decades (1971-2015), an alarming increasing trend in fog frequency has been observed during the winter months of December and January over the study area. The frequency of fog has increased by 118.4% during the peak winter months of December and January. It has also been observed that on an average central part of IGP has 66.29% fog days followed by west IGP with 41.94% fog days. Further, Empirical Orthogonal Function (EOF) decomposition and Mann-Kendall variation analysis are used to analyze the spatial and temporal patterns of winter fog. The findings have significant implications for the further research of fog over IGP and formulate robust strategies to adapt the fog variability and mitigate its effects. The decision by Delhi Government to implement odd-even scheme to restrict the use of private vehicles in order to reduce pollution and improve quality of air may result in increasing the alarming increasing trend of fog over Delhi and its surrounding areas regions of IGP.
Abstract: In this paper, we present the human action recognition method using the variational Bayesian HMM with the Dirichlet process mixture (DPM) of the Gaussian-Wishart emission model (GWEM). First, we define the Bayesian HMM based on the Dirichlet process, which allows an infinite number of Gaussian-Wishart components to support continuous emission observations. Second, we have considered an efficient variational Bayesian inference method that can be applied to drive the posterior distribution of hidden variables and model parameters for the proposed model based on training data. And then we have derived the predictive distribution that may be used to classify new action. Third, the paper proposes a process of extracting appropriate spatial-temporal feature vectors that can be used to recognize a wide range of human behaviors from input video image. Finally, we have conducted experiments that can evaluate the performance of the proposed method. The experimental results show that the method presented is more efficient with human action recognition than existing methods.
Abstract: Dengue outbreaks are affected by biological,
ecological, socio-economic and demographic factors that vary over
time and space. These factors have been examined separately and still
require systematic clarification. The present study aimed to investigate
the spatial-temporal clustering relationships between these factors and
dengue outbreaks in the northern region of Sri Lanka. Remote sensing
(RS) data gathered from a plurality of satellites were used to develop
an index comprising rainfall, humidity and temperature data. RS data
gathered by ALOS/AVNIR-2 were used to detect urbanization, and a
digital land cover map was used to extract land cover information.
Other data on relevant factors and dengue outbreaks were collected
through institutions and extant databases. The analyzed RS data and
databases were integrated into geographic information systems,
enabling temporal analysis, spatial statistical analysis and space-time
clustering analysis. Our present results showed that increases in the
number of the combination of ecological factor and socio-economic
and demographic factors with above the average or the presence
contribute to significantly high rates of space-time dengue clusters.
Abstract: The proliferation of user-generated content (UGC) results in huge opportunities to explore event patterns. However, existing event recommendation systems primarily focus on advanced information technology users. Little work has been done to address novice and low-literacy users. The next billion users providing and consuming UGC are likely to include communities from developing countries who are ready to use affordable technologies for subsistence goals. Therefore, we propose a design framework for providing event recommendations to address the needs of such users. Grounded in information integration theory (IIT), our framework advocates that effective event recommendation is supported by systems capable of (1) reliable information gathering through structured user input, (2) accurate sense making through spatial-temporal analytics, and (3) intuitive information dissemination through interactive visualization techniques. A mobile pest management application is developed as an instantiation of the design framework. Our preliminary study suggests a set of design principles for novice and low-literacy users.
Abstract: In this paper we present a noise reduction filter for video processing. It is based on the recently proposed two dimensional steering kernel, extended to three dimensions and further augmented to suit the spatial-temporal domain of video processing. Two alternative filters are proposed - the time symmetric kernel and the time asymmetric kernel. The first reduces the noise on single sequences, but to handle the problems at scene shift the asymmetric kernel is introduced. The performance of both are tested on simulated data and on a real video sequence together with the existing steering kernel. The proposed kernels improves the Rooted Mean Squared Error (RMSE) compared to the original steering kernel method on video material.
Abstract: PARIS (Personal Archiving and Retrieving Image
System) is an experiment personal photograph library, which includes
more than 80,000 of consumer photographs accumulated within a
duration of approximately five years, metadata based on our proposed
MPEG-7 annotation architecture, Dozen Dimensional Digital Content
(DDDC), and a relational database structure. The DDDC architecture
is specially designed for facilitating the managing, browsing and
retrieving of personal digital photograph collections. In annotating
process, we also utilize a proposed Spatial and Temporal Ontology
(STO) designed based on the general characteristic of personal
photograph collections. This paper explains PRAIS system.
Abstract: In this study spatial-temporal speckle correlation techniques have been applied for the quality evaluation of three different Indian fruits namely apple, pear and tomato for the first time. The method is based on the analysis of variations of laser light scattered from biological samples. The results showed that crosscorrelation coefficients of biospeckle patterns change subject to their freshness and the storage conditions. The biospeckle activity was determined by means of the cross-correlation functions of the intensity fluctuations. Significant changes in biospeckle activity were observed during their shelf lives. From the study, it is found that the biospeckle activity decreases with the shelf-life storage time. Further it has been shown that biospeckle activity changes according to their respiration rates.