1,808 research outputs found
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곡νλΆ(μνμ‘°κ²½ν), 2023. 2. λ₯μλ ¬.Precise estimation of the number of trees and individual tree location with species information all over the city forms solid foundation for enhancing ecosystem service. However, mapping individual trees at the city scale remains challenging due to heterogeneous patterns of urban tree distribution. Here, we present a novel framework for merging multiple sensing platforms with leveraging various deep neural networks to produce a fine-grained urban tree map. We performed mapping trees and detecting species by relying only on RGB images taken by multiple sensing platforms such as airborne, citizens and vehicles, which fueled six deep learning models. We divided the entire process into three steps, since each platform has its own strengths. First, we produced individual tree location maps by converting the central points of the bounding boxes into actual coordinates from airborne imagery. Since many trees were obscured by the shadows of the buildings, we applied Generative Adversarial Network (GAN) to delineate hidden trees from the airborne images. Second, we selected tree bark photos collected by citizen for species mapping in urban parks and forests. Species information of all tree bark photos were automatically classified after non-tree parts of images were segmented. Third, we classified species of roadside trees by using a camera mounted on a car to augment our species mapping framework with street-level tree data. We estimated the distance from a car to street trees from the number of lanes detected from the images. Finally, we assessed our results by comparing it with Light Detection and Ranging (LiDAR), GPS and field data. We estimated over 1.2 million trees existed in the city of 121.04 kmΒ² and generated more accurate individual tree positions, outperforming the conventional field survey methods. Among them, we detected the species of more than 63,000 trees. The most frequently detected species was Prunus yedoensis (21.43 %) followed by Ginkgo biloba (19.44 %), Zelkova serrata (18.68 %), Pinus densiflora (7.55 %) and Metasequoia glyptostroboides (5.97 %). Comprehensive experimental results demonstrate that tree bark photos and street-level imagery taken by citizens and vehicles are conducive to delivering accurate and quantitative information on the distribution of urban tree species.λμ μ μμ μ‘΄μ¬νλ λͺ¨λ μλͺ©μ μ«μμ κ°λ³ μμΉ, κ·Έλ¦¬κ³ μμ’
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μ λ³΄κ° νμ§λμμΌλ©°, μ΄μ€ κ°μ₯ λΉλ²ν νμ§λ μλͺ©μ μλ²λ무 (Prunus yedoensis, 21.43 %)μλ€. μνλ무 (Ginkgo biloba, 19.44 %), λν°λ무 (Zelkova serrata, 18.68 %), μλ무 (Pinus densiflora, 7.55 %), κ·Έλ¦¬κ³ λ©νμΈμΏΌμ΄μ΄ (Metasequoia glyptostroboides, 5.97 %) λ±μ΄ κ·Έ λ€λ₯Ό μ΄μλ€. ν¬κ΄μ μΈ κ²μ¦μ΄ μνλμκ³ , λ³Έ μ°κ΅¬μμλ μλ―Όμ΄ μμ§ν μνΌ μ¬μ§κ³Ό μ°¨λμΌλ‘λΆν° μμ§λ λλ‘λ³ μ΄λ―Έμ§λ λμ μμ’
λΆν¬μ λν μ ννκ³ μ λμ μΈ μ 보λ₯Ό μ 곡νλ€λ κ²μ κ²μ¦νμλ€.1. Introduction 6
2. Methodology 9
2.1. Data collection 9
2.2. Deep learning overall 12
2.3. Tree counting and mapping 15
2.4. Tree species detection 16
2.5. Evaluation 21
3. Results 22
3.1. Evaluation of deep learning performance 22
3.2. Tree counting and mapping 23
3.3. Tree species detection 27
4. Discussion 30
4.1. Multiple sensing platforms for urban areas 30
4.2. Potential of citizen and vehicle sensors 34
4.3. Implications 48
5. Conclusion 51
Bibliography 52
Abstract in Korean 61μ
Monument Monitor: using citizen science to preserve heritage
This research demonstrates how data collected by citizen scientists can act as a valuable resource for heritage managers. It establishes to what extent visitorsβ photographs can be used to assist in aspects of condition monitoring focusing on biological and plant growth, erosion, stone/mortar movement, water ingress/pooling and antisocial behaviour.
This thesis describes the methodology and outcomes of Monument Monitor (MM), a project set up in collaboration with Historic Environment Scotland (HES) that requested visitors at selected Scottish heritage sites to submit photographs of their visit. Across twenty case study sites participants were asked to record evidence of a variety of conservation issues. Patterns of contributions to the project are presented alongside key stakeholder feedback, which show how MM was received and where data collection excelled. Alongside this, the software built to manage and sort submissions is presented as a scalable methodology for the collection of citizen generated data of heritage sites.
To demonstrate the applicability of citizen generated data for in depth monitoring and analysis, an environmental model is created using the submissions from one case study which predicts the effect of the changing climate at the site between 1980 - 2080. Machine Learning (ML) is used to analyse submitted data in both classification and segmentation tasks. This application demonstrates the validity of utilising ML tools to assist in the analysis and categorising of volunteer submitted photographs.
The outcome of this PhD is a scalable methodology with which conservation staff can use visitor submitted images as an evidence-base to support them in the management of heritage sites
Smart and Pervasive Healthcare
Smart and pervasive healthcare aims at facilitating better healthcare access, provision, and delivery by overcoming spatial and temporal barriers. It represents a shift toward understanding what patients and clinicians really need when placed within a specific context, where traditional face-to-face encounters may not be possible or sufficient. As such, technological innovation is a necessary facilitating conduit. This book is a collection of chapters written by prominent researchers and academics worldwide that provide insights into the design and adoption of new platforms in smart and pervasive healthcare. With the COVID-19 pandemic necessitating changes to the traditional model of healthcare access and its delivery around the world, this book is a timely contribution
Earth Observation Open Science and Innovation
geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc
Seeing the City Digitally
This book explores what's happening to ways of seeing urban spaces in the contemporary moment, when so many of the technologies through which cities are visualised are digital. Cities have always been pictured, in many media and for many different purposes. This edited collection explores how that picturing is changing in an era of digital visual culture. Analogue visual technologies like film cameras were understood as creating some sort of a trace of the real city. Digital visual technologies, in contrast, harvest and process digital data to create images that are constantly refreshed, modified and circulated. Each of the chapters in this volume examines a different example of this processual visuality is reconfiguring the spatial and temporal organisation of urban life
Using Machine Vision to Estimate Fish Length from Images using Regional Convolutional Neural Networks
An image can encode date, time, location and camera information as metadata and implicitly encodes species information and data on human activity, for example the size distribution of fish removals. Accurate length estimates can be made from images using a fiducial marker; however, their manual extraction is time-consuming and estimates are inaccurate without control over the imaging system. This article presents a methodology which uses machine vision to estimate the total length (TL) of a fusiform fish (European sea bass). Three regional convolutional neural networks (R-CNN) were trained from public images. Images of European sea bass were captured with a fiducial marker with three non-specialist cameras. Images were undistorted using the intrinsic lens properties calculated for the camera in OpenCV; then TL was estimated using machine vision (MV) to detect both marker and subject. MV performance was evaluated for the three R-CNNs under downsampling and rotation of the captured images. Each R-CNN accurately predicted the location of fish in test images (mean intersection over union, 93%) and estimates of TL were accurate, with percent mean bias error (%MBE [95% CIs])Β =Β 2.2% [2.0, 2.4]). Detections were robust to horizontal flipping and downsampling. TL estimates at absolute image rotations >20Β° became increasingly inaccurate but %MBE [95% CIs] was reduced to β0.1% [β0.2, 0.1] using machine learning to remove outliers and model bias. Machine vision can classify and derive measurements of species from images without specialist equipment. It is anticipated that ecological researchers and managers will make increasing use of MV where image data are collected (e.g. in remote electronic monitoring, virtual observations, wildlife surveys and morphometrics) and MV will be of particular utility where large volumes of image data are gathered
Moving Forward with Digital Disruption: What Big Data, IoT, Synthetic Biology, AI, Blockchain, and Platform Businesses Mean to Libraries
Digital disruption, also known as βthe fourth industrial revolution,β is blurring the lines between the physical, digital, and biological spheres. This issue of Library Technology Reports (vol. 56, no. 2) examines todayβs leading-edge technologies and their disruptive impacts on our society through examples such as extended reality, Big Data, the Internet of Things (IoT), synthetic biology, 3-D bio-printing, artificial intelligence (AI), blockchain, and platform businesses in the sharing economy. This report explains how new digital technologies are merging the physical and the biological with the digital; what kind of transformations are taking place as a result in production, management, and governance; and how libraries can continue to innovate with new technologies while keeping a critical distance from the rising ideology of techno-utopianism and at the same time contributing to social good
Quantifying Quality of Life
Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject
Seeing the City Digitally
This book explores what's happening to ways of seeing urban spaces in the contemporary moment, when so many of the technologies through which cities are visualised are digital. Cities have always been pictured, in many media and for many different purposes. This edited collection explores how that picturing is changing in an era of digital visual culture. Analogue visual technologies like film cameras were understood as creating some sort of a trace of the real city. Digital visual technologies, in contrast, harvest and process digital data to create images that are constantly refreshed, modified and circulated. Each of the chapters in this volume examines a different example of this processual visuality is reconfiguring the spatial and temporal organisation of urban life
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