116 research outputs found

    Deep Learning for Remote Sensing Image Processing

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    Remote sensing images have many applications such as ground object detection, environmental change monitoring, urban growth monitoring and natural disaster damage assessment. As of 2019, there were roughly 700 satellites listing “earth observation” as their primary application. Both spatial and temporal resolutions of satellite images have improved consistently in recent years and provided opportunities in resolving fine details on the Earth\u27s surface. In the past decade, deep learning techniques have revolutionized many applications in the field of computer vision but have not fully been explored in remote sensing image processing. In this dissertation, several state-of-the-art deep learning models have been investigated and customized for satellite image processing in the applications of landcover classification and ground object detection. First, a simple and effective Convolutional Neural Network (CNN) model is developed to detect fresh soil from tunnel digging activities near the U.S. and Mexico border by using pansharpened synthetic hyperspectral images. These tunnels’ exits are usually hidden under warehouses and are used for illegal activities, for example, by drug dealers. Detecting fresh soil nearby is an indirect way to search for these tunnels. While multispectral images have been used widely and regularly in remote sensing since the 1970s, with the fast advances in hyperspectral sensors, hyperspectral imagery is becoming popular. A combination of 80 synthetic hyperspectral channels with the original eight multispectral channels collected by the WorldView-2 satellite are used by CNN to detect fresh soil. Experimental results show that detection performance can be significantly improved by the combination of synthetic hyperspectral images with those original multispectral channels. Second, an end-to-end, pixel-level Fully Convolutional Network (FCN) model is implemented to estimate the number of refugee tents in the Rukban area near the Syrian-Jordan border using high-resolution multispectral satellite images collected by WordView-2. Rukban is a desert area crossing the border between Syria and Jordan, and thousands of Syrian refugees have fled into this area since the Syrian civil war in 2014. In the past few years, the number of refugee shelters for the forcibly displaced Syrian refugees in this area has increased rapidly. Estimating the location and number of refugee tents has become a key factor in maintaining the sustainability of the refugee shelter camps. Manually counting the shelters is labor-intensive and sometimes prohibitive given the large quantities. In addition, these shelters/tents are usually small in size, irregular in shape, and sparsely distributed in a very large area and could be easily missed by the traditional image-analysis techniques, making the image-based approaches also challenging. The FCN model is also boosted by transfer learning with the knowledge in the pre-trained VGG-16 model. Experimental results show that the FCN model is very accurate and has less than 2% of error. Last, we investigate the Generative Adversarial Networks (GAN) to augment training data to improve the training of FCN model for refugee tent detection. Segmentation based methods like FCN require a large amount of finely labeled images for training. In practice, this is labor-intensive, time consuming, and tedious. The data-hungry problem is currently a big hurdle for this application. Experimental results show that the GAN model is a better tool as compared to traditional methods for data augmentation. Overall, our research made a significant contribution to remote sensing image processin

    Colliding Worlds: Modern Computational Methods for Scattering Amplitude Calculations and Responding to Crisis Situations

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    Precision theoretical predictions for high multiplicity scattering rely on the evaluation of increasingly complicated scattering amplitudes which come with an extremely high CPU cost. For state-of-the-art processes this can cause technical bottlenecks in the production of fully differential distributions. In this thesis we explore the possibility of using neural networks to approximate multi-jet scattering amplitudes and provide efficient inputs for Monte Carlo integration. We begin by focussing on QCD corrections to e+e−→≀5e^+e^- \to \leq 5 jets up to one-loop. We demonstrate reliable interpolation when a series of networks are trained on amplitudes that have been divided into sectors defined by their infrared singularity structure. Complete simulations for one-loop distributions show speed improvements of at least an order of magnitude over standard approaches. We extend our analysis to the case of loop-induced diphoton production through gluon fusion and develop a realistic simulation method that can be applied to hadron collider observables. Specifically, we present a detailed study for 2→32\to3 and 2→42\to4 scattering problems which are extremely relevant for future phenomenological studies and find excellent agreement with amplitudes generated using traditional methods. In order to provide a useable technology, we present an interface with the \sherpa~Monte Carlo event generator. The techniques underlying our machine learning methodology and Monte Carlo event generator simulations are widely applicable in other domains as well. In this thesis we will also discuss the use of machine learning to aid in rapid response to crises situations, and the parallels between multi-particle event generators and multi-agent simulations for modelling the spread of epidemics. In this latter case, we develop a new agent-based model with highly granular resolution and discuss its applications to modelling the spread of COVID-19 in England, and in refugee and internally displaced person settlements to aid data driven decision making

    GEOBIA 2016 : Solutions and Synergies., 14-16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC): open access e-book

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    Remote Sensing of Natural Hazards

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    Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches

    Journal of environmental geography : Vol. XIV. No 1-2.

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    Geneva Health Forum 2020 Poster Book

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    From 16 to 18 November 2020, the eighth edition of the Geneva Health Forum, which took place in the difficult context of the Covid 19 pandemic, hosted 165 posters. The present collection offers through 65 posters a wide range of topics discussed

    Global PeaceTech : unlocking the better angels of our techne

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    The double-edged nature of technology pervades human history. Today, the potential for peace offered by the internet, social networks, mobile devices, digital identities, AI, blockchain, big data, geospatial information, is matched by the risks of disinformation, polarisation, online violence, surveillance, data privacy, cyber-attacks, and power concentration. Faced with this knife-edge between the bright and dark sides of disruptive technologies, how do we conjure up the better angels of our nature? Many agents for change around the world have sought to employ and regulate new technologies to foster peaceful processes under the aegis of “PeaceTech” initiatives. This paper introduces “Global PeaceTech” as a new field of social inquiry in the context of International Relations and Global Affairs, with the aim of analysing the global context in which these initiatives are embedded and interconnected, in order to draw prescriptive lessons. The deployment of technology for peace entails legal, political, economic, and ethical dilemmas that transcend national borders and require new models of transnational governance. By bringing together the world of “tech-for-good” and the field of international studies broadly defined as the study of patterns of global change, “Global PeaceTech” fills a gap at the intersection between peace studies and global governance and promotes policy innovation at the transnational level. The paper offers an overview of this agenda in four parts: Part I starts from the IR literature and explores the relationship between technology, peace and war. Part II defines the main differences between PeaceTech and Global PeaceTech. Part III sets out a new research agenda in Global PeaceTech, introducing core analytical concepts and research methods, and discussing its potential political and societal impact. In Part IV, we conclude by presenting a series of example of relevant research areas as a reference for further research in Global PeaceTech

    LIDAR based semi-automatic pattern recognition within an archaeological landscape

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    LIDAR-Daten bieten einen neuartigen Ansatz zur Lokalisierung und Überwachung des kulturellen Erbes in der Landschaft, insbesondere in schwierig zu erreichenden Gebieten, wie im Wald, im unwegsamen GelĂ€nde oder in sehr abgelegenen Gebieten. Die manuelle Lokalisation und Kartierung von archĂ€ologischen Informationen einer Kulturlandschaft ist in der herkömmlichen Herangehensweise eine sehr zeitaufwĂ€ndige Aufgabe des Fundstellenmanagements (Cultural Heritage Management). Um die Möglichkeiten in der Erkennung und bei der Verwaltung des kulturellem Erbes zu verbessern und zu ergĂ€nzen, können computergestĂŒtzte Verfahren einige neue LösungsansĂ€tze bieten, die darĂŒber hinaus sogar die Identifizierung von fĂŒr das menschliche Auge bei visueller Sichtung nicht erkennbaren Details ermöglichen. Aus archĂ€ologischer Sicht ist die vorliegende Dissertation dadurch motiviert, dass sie LIDAR-GelĂ€ndemodelle mit archĂ€ologischen Befunden durch automatisierte und semiautomatisierte Methoden zur Identifizierung weiterer archĂ€ologischer Muster zu Bodendenkmalen als digitale „LIDAR-Landschaft“ bewertet. Dabei wird auf möglichst einfache und freie verfĂŒgbare algorithmische AnsĂ€tze (Open Source) aus der Bildmustererkennung und Computer Vision zur Segmentierung und Klassifizierung der LIDAR-Landschaften zur großflĂ€chigen Erkennung archĂ€ologischer DenkmĂ€ler zurĂŒckgegriffen. Die Dissertation gibt dabei einen umfassenden Überblick ĂŒber die archĂ€ologische Nutzung und das Potential von LIDAR-Daten und definiert anhand qualitativer und quantitativer AnsĂ€tze den Entwicklungsstand der semiautomatisierten Erkennung archĂ€ologischer Strukturen im Rahmen archĂ€ologischer Prospektion und Fernerkundungen. DarĂŒber hinaus erlĂ€utert sie Best Practice-Beispiele und den einhergehenden aktuellen Forschungsstand. Und sie veranschaulicht die QualitĂ€t der Erkennung von BodendenkmĂ€lern durch die semiautomatisierte Segmentierung und Klassifizierung visualisierter LIDAR-Daten. Letztlich identifiziert sie das Feld fĂŒr weitere Anwendungen, wobei durch eigene, algorithmische Template Matching-Verfahren großflĂ€chige Untersuchungen zum kulturellen Erbe ermöglicht werden. ResĂŒmierend vergleicht sie die analoge und computergestĂŒtzte Bildmustererkennung zu Bodendenkmalen, und diskutiert abschließend das weitere Potential LIDAR-basierter Mustererkennung in archĂ€ologischen Kulturlandschaften

    Slavery from space: an analysis of the modern slavery-environmental degradation nexus using remote sensing data

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    Modern slavery has been connected to degradation of the environment, and has been found to contribute to anthropogenic climate change. Three sectors have been investigated using satellite Earth Observation (EO) data in order to provide a unique insight into the modern slavery-environmental degradation nexus. Remote sensing affords a unique ability to measure and understand these ecological changes over large timescales, and vast geographical areas. A local, regional, and global assessment of sectors known to heavily use modern slavery practices within their workforce has been undertaken using a variety of remotely sensed data sources and products. Fish-processing, brick kilns, and tree loss associated with multiple sectors, have all been analysed. Levels of environmental damage in the affected sectors have been noted, and measured using satellite EO data. These effects have included: tree loss of mangroves and tropical forests for fish-processing camps and oil palm plantations; the emission of pollutants which contribute to atmospheric climate change; the extraction of resources, such as groundwater and good-quality topsoil; and changes to landcover and land-use in areas that are important for production of food and economic support for large populations. Over the course of this investigation, ten post-harvest fish-processing camps have been located, and the first replicable methodology for estimating the number of brick kilns in the South Asian ‘Brick Belt’ region has been provided – where open access satellite EO data enabled the estimation of 55,387 brick kilns. The latter has since enabled machine learning methodologies to provide accurate locations and kiln ages which have assisted in the environmental assessment of this large-scale transnational industry. Furthermore, if modern slavery practices were eliminated from this industry, the environmental impact of the brick-making could be reduced by the equivalent of almost 10,000 kilns. Finally, tree loss has been quantified and the policy implications of deforestation and forest degradation as a result of modern slavery have been explored in four countries. Ultimately, there are a large variety of environmentally degrading activities known to use modern slavery practices that may be explored using satellite EO data. Remote sensing throughout this thesis has enabled the exploration of these implications for some sectors, and proved the proof of concept that additional data acquisition from remotely sensed sources, can support in the overall goal of assisting in the understanding and eradication of modern slavery. Satellite EO is an underutilised methodology within the antislavery community and, as shown within this thesis, there is the power to investigate the environmental implications of these sectors which have had numerous documented cases of modern slavery. In order to achieve the Sustainable Development Goals (SDGs) – particularly target 8.7 which aims to end modern slavery by 2030 – multiple avenues of investigation are required to understand, locate, and eradicate modern slavery. Applying remote sensing to assess the ecological impact of these cases is one such avenue that can provide information to assist in this achievement, and support the success of multiple SDGs. The author would like to acknowledge that they have written the thesis from the starting point of being a non-survivor

    Spatiotemporal enabled Content-based Image Retrieval

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