Continuous and accurate monitoring of agricultural landscapes is crucial for understanding crop phenology and responding to climatic and anthropogenic changes. However, the widely used optical satellite remote sensing...
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Continuous and accurate monitoring of agricultural landscapes is crucial for understanding crop phenology and responding to climatic and anthropogenic changes. However, the widely used optical satellite remote sensing is limited by revisit cycles and weather conditions, leading to gaps in agricultural monitoring. To address these limitations, we designed and deployed a Near Surface Camera (NSCam) Network across China, and explored its application in agricultural land monitoring and achieving climate-smart agriculture (CSA). By analyzing the image data captured by the NSCam Network, we can accurately assess long-term or abrupt agricultural land changes. According to the preliminary monitoring results, integrating NSCam data with remote sensing imagery greatly enhances the temporal details and accuracy of agricultural monitoring, aiding agricultural managers in making informed decisions. The impacts of abnormal weather conditions and human activities on agricultural land, which are not captured by remote sensing imagery, can be complemented by incorporating our NSCam Network. The successful implementation of this method underscores its potential for broader application in CSA, promoting resilient and sustainable agricultural practices.
Unlike single geospatial objects extraction, the task of road extraction faces many challenges, including its narrowness, sparsity, diversity, and class imbalance. In order to solve the above problems, this paper prop...
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Unlike single geospatial objects extraction, the task of road extraction faces many challenges, including its narrowness, sparsity, diversity, and class imbalance. In order to solve the above problems, this paper proposes a modified convolution neural network with transfer learning (MCNNTL) for road extraction from remote sensing imagery. The techniques of data augmentation, transfer learning, data preprocessing, and backpropagation algorithm are used in order to get better performance. The Massachusetts roads dataset is chosen as the dataset to carry out the experiment of road extraction, and the result shows that this model outperforms traditional methods of road extraction from remote sensing imagery in precision, recall rate and composite accuracy.
作者：
Dutt, NikilRegazzoni, Carlo S.Rinner, BernhardYao, XinNikil Dutt (Fellow
IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member
IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and
Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In c...
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Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.
In the New-Generation Network, Information-centric Networking (ICN) is one of the most promising technologies for communication systems which are suitable for the content data provisioning and delivery from/to users. ...
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In the New-Generation Network, Information-centric Networking (ICN) is one of the most promising technologies for communication systems which are suitable for the content data provisioning and delivery from/to users. To create a new ICN on the JGN-X network virtualization platform, developing new networking technologies such as virtual network resources control from users, in-network processing for content data creation, and energy efficient routing on the virtual network, were challenged.
A major contributor to the expense and length of time to design, build, and test new systems has been the need to build and test hardware prototypes to determine their effectiveness in meeting operational requirements...
A major contributor to the expense and length of time to design, build, and test new systems has been the need to build and test hardware prototypes to determine their effectiveness in meeting operational requirements. Recent and dramatic advances in computer simulation technologies hold forth the promise of revolutionizing design and acquisition strategies by providing the means to validate end users' requirements prior to hardware construction. By designing and operationally testing virtual prototypes in a virtual environment, these technologies will soon offer naval architects the ability to build and launch ships in computer-based cyberspace in lieu of the shipbuilder's ways. The authors of this paper provide the background for these developments, explore the significance and ramifications of these technologies to the current process of ship and system design, outline challenges lying ahead, and present their vision and recommendations for future development.
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