Deep Learning-Based Spatio-Temporal Data Mining Using Multi-Source Geospatial Data

With the rapid development of various geospatial technologies including remote sensing, mobile devices, and Global Position System (GPS), spatio-temporal data are abundantly available nowadays. Extracting valuable knowledge from spatio-temporal data is of crucial importance for many real-world applications such as intelligent transportation, social services, and intelligent distribution. With the fast increase of the amount and resolution of spatio-temporal data, traditional data mining methods are becoming obsolete. In recent years, deep learning models such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) have made promising achievements in many fields based on the strong ability in automated feature extraction and have been broadly used in different spatio-temporal data mining tasks. Many methods have been developed, and more diverse data were collected in recent decades, however, the existing methods have faced challenges from multi-source geospatial data. This thesis investigates four efficient techniques in different scenarios for spatio-temporal data mining that take advantage of multi-source geospatial data to overcome the limitations of traditional data mining methods. This study investigates spatio-temporal data mining from four different perspectives. Firstly, a multi-elemental geolocation inference method is proposed to predict the location of tweets without geo-tags. Secondly, an optimization model is proposed to detect multiple Areas-of-Interest (AOIs) simultaneously and solve the multi-AOIs detection problem. Thirdly, a multi-task Res-U-Net model with attention mechanism is developed for the extraction of the building roofs and the whole building shapes from remote sensing images, then an offset vector method is used to detect the footprints of the high-rise buildings based on the boundaries of the corresponding building roofs and shapes. Lastly, a novel decoder fusion model is introduced to extract interior road network from remote sensing images and GPS trajectory data. And this method is effective for multi-source data mining. The proposed four methods use different techniques for spatio-temporal data mining to improve the detection performance. Numerous experiments show that the techniques developed in this thesis can detect ground features efficiently and effectively and overcome the limitations of conventional algorithms. The studies demonstrate that exploiting spatial information from multi-source geospatial data can improve the detection accuracy in comparison with single-source geospatial data

Spatio-Temporal Partitioning and Location Prediction in GPS Trajectory Data

© 2019 Morteza Mousavi BarroudiIn recent years, a large volume of GPS trajectory data of the mobile objects is collected by many individuals, companies, and organizations. Various applications are being developed to extract useful information from the collected trajectory data through data analysis. Having adequate information about the position and the geometry of the Points of Interest (POIs) of the mobile objects is a key requirement in a wide range of such applications. The information about the location and the geometries of the POIs is not publicly available in various environments due to the nature of the environments (e.g. animals migration monitoring), or because of military and security issues. The main research problem of this thesis is to estimate the geometries of the POIs of the mobile objects by merely using the data extracted from GPS trajectories. In this dissertation, the geometry of a POI is called a Geometry of Interest (GOI). The methodology of estimating the GOIs is composed of five phases: (i) Stay region extraction, (ii) Clustering the stay regions aiming to estimation of geometries of the destination regions, (iii) Data aggregation, (iv) Outlier detection, and (v) Partitioning the trajectory area into a grid with inhomogeneous cells containing the GOIs. In each of the phases of the spatio-temporal partitioning process, new methods and algorithms have been proposed and evaluated based on the field data. The evaluation is performed based on the geometric similarity comparison between the geometries of the real-world POIs (ground truth) and the estimated GOIs. The experimental results prove that the proposed multi-step framework is able to estimate the GOIs with a high geometric quality. Moreover, to be able to evaluate the quality of the estimated GOIs when the ground truth is not available, two novel GOI quality measurement metrics are proposed and evaluated. The experiments show that the proposed metrics can effectively analyse the quality of the GOIs without having access to the ground truth. In this thesis, we also evaluate the effect of the quality of the estimated GOIs on the accuracy of spatio-temporal location prediction applications. Moreover, to tackle with the uncertainty of the estimated GOIs which have a negative impact on the accuracy of the spatio-temporal location prediction, a novel method is proposed which performs approximate GOI matching instead of exact GOIs matching in the sequential pattern mining of the spatio-temporal location prediction process. Evaluation results show that the proposed approximate spatio-temporal location prediction method outperforms the competitor methods in terms of the rate of true prediction and reducing the temporal prediction error

Integrated Watershed Management in Rainfed Agriculture

This book provides a comprehensive presentation of the realization of improved rainfed agriculture yield in semi-arid and dry land areas. The incentive of watershed programs is to increase the return on investment with over 20% for 65% of the projects that are currently underperforming. Besides techniques to improve the livelihood of the many small-scale farmers in developing countries, it includes examples and case studies for further support. The methods discussed have recently shown to be successful and economically remunerative in India and in various African countries. Intended for professionals (investors, policy makers), researchers and (post) graduate students working on dry land and sustainable agriculture and water and natural resources management. Suited for courses in dry land agriculture, soil and water management and watershed development

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Planetary Science Vision 2050 Workshop : February 27–28 and March 1, 2017, Washington, DC

This workshop is meant to provide NASA’s Planetary Science Division with a very long-range vision of what planetary science may look like in the future.Organizer, Lunar and Planetary Institute ; Conveners, James Green, NASA Planetary Science Division, Doris Daou, NASA Planetary Science Division ; Science Organizing Committee, Stephen Mackwell, Universities Space Research Association [and 14 others]PARTIAL CONTENTS: Exploration Missions to the Kuiper Belt and Oort Cloud--Future Mercury Exploration: Unique Science Opportunities from Our Solar System’s Innermost Planet--A Vision for Ice Giant Exploration--BAOBAB (Big and Outrageously Bold Asteroid Belt) Project--Asteroid Studies: A 35-Year Forecast--Sampling the Solar System: The Next Level of Understanding--A Ground Truth-Based Approach to Future Solar System Origins Research--Isotope Geochemistry for Comparative Planetology of Exoplanets--The Moon as a Laboratory for Biological Contamination Research--“Be Careful What You Wish For:” The Scientific, Practical, and Cultural Implications of Discovering Life in Our Solar System--The Importance of Particle Induced X-Ray Emission (PIXE) Analysis and Imaging to the Search for Life on the Ocean Worlds--Follow the (Outer Solar System) Water: Program Options to Explore Ocean Worlds--Analogies Among Current and Future Life Detection Missions and the Pharmaceutical/ Biomedical Industries--On Neuromorphic Architectures for Efficient, Robust, and Adaptable Autonomy in Life Detection and Other Deep Space Missions

UVR8 mediated spatial differences as a prerequisite for UV-B induced inflorescence phototropism

In Arabidopsis hypocotyls, phototropins are the dominant photoreceptors for the positive phototropism response towards unilateral ultraviolet-B (UV-B) radiation. We report a stark contrast of response mechanism with inflorescence stems with a central role for UV RESISTANCE LOCUS 8 (UVR8). The perception of UV-B occurs mainly in the epidermis and cortex with a lesser contribution of the endodermis. Unilateral UV-B exposure does not lead to a spatial difference in UVR8 protein levels but does cause differential UVR8 signal throughout the stem with at the irradiated side 1) increase of the transcription factor ELONGATED HYPOCOTYL 5 (HY5), 2) an associated strong activation of flavonoid biosynthesis genes and flavonoid accumulation, 3) increased GA2oxidase expression, diminished gibberellin1 levels and accumulation of DELLA protein REPRESSOR OF GA1 (RGA) and, 4) increased expression of the auxin transport regulator, PINOID, contributing to local diminished auxin signalling. Our molecular findings are in support of the Blaauw theory (1919), suggesting that differential growth occurs trough unilateral photomorphogenic growth inhibition. Together the data indicate phototropin independent inflorescence phototropism through multiple locally UVR8-regulated hormone pathways

11th International Coral Reef Symposium Proceedings

A defining theme of the 11th International Coral Reef Symposium was that the news for coral reef ecosystems are far from encouraging. Climate change happens now much faster than in an ice-age transition, and coral reefs continue to suffer fever-high temperatures as well as sour ocean conditions. Corals may be falling behind, and there appears to be no special silver bullet remedy. Nevertheless, there are hopeful signs that we should not despair. Reef ecosystems respond vigorously to protective measures and alleviation of stress. For concerned scientists, managers, conservationists, stakeholders, students, and citizens, there is a great role to play in continuing to report on the extreme threat that climate change represents to earth’s natural systems. Urgent action is needed to reduce CO2 emissions. In the interim, we can and must buy time for coral reefs through increased protection from sewage, sediment, pollutants, overfishing, development, and other stressors, all of which we know can damage coral health. The time to act is now. The canary in the coral-coal mine is dead, but we still have time to save the miners. We need effective management rooted in solid interdisciplinary science and coupled with stakeholder buy in, working at local, regional, and international scales alongside global efforts to give reefs a chance.https://nsuworks.nova.edu/occ_icrs/1000/thumbnail.jp