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Journal articles on the topic 'Label cover'

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Published: 4 June 2021

Last updated: 10 February 2022

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1

M Fadil Ramadhan and M. Subur Drajat. "Kegiatan Marketing Pr Label Musik Digital Audio Tape Bandung." Jurnal Riset Public Relations 1, no. 1 (2021): 33–38. http://dx.doi.org/10.29313/jrpr.v1i1.80.

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Abstract. Music labels Digital Audio Tape having the marketing public relations that his services can be used by audience. The marketing public relations label was set up by Trizha Harun as public relations at the music labels Digital Audio Tape. ( ruslan 2008: 249 ), Public relations serves to communicate both sides between the company with public internal and external relationships and mutual with the audience be considered important by label. Activity public relations is held mutual communication between institution with public intended to create mutual understanding and support for the ach

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Nyboe, Jacob Ølgaard. "Coverets kapital: En empirisk undersøgelse af genremærkatens betydning for vurderingen af det litterære værk." K&K - Kultur og Klasse 45, no. 124 (2017): 233–52. http://dx.doi.org/10.7146/kok.v45i124.103920.

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Based on a large survey (n=539) this study examines how book covers in general and the genre label in particular play a role in the aesthetic positioning and marketing of books. A given work can succeed with large sales numbers and economic capital as well as by obtaining more field specific cultural and social capital. These different ways of positioning oneself on the market are, according to Bourdieu, to a large extent inverse – and it is therefore hypothesized that they require different strategies. In order to examine this phenomenon, the respondents are split into two groups and asked to

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Charikar, Moses, MohammadTaghi Hajiaghayi, and Howard Karloff. "Improved Approximation Algorithms for Label Cover Problems." Algorithmica 61, no. 1 (2011): 190–206. http://dx.doi.org/10.1007/s00453-010-9464-3.

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Dinur, Irit, and Shmuel Safra. "On the hardness of approximating label-cover." Information Processing Letters 89, no. 5 (2004): 247–54. http://dx.doi.org/10.1016/j.ipl.2003.11.007.

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Wang, Yu, Jingxiong Zhang, Di Liu, Wenjing Yang, and Wangle Zhang. "Accuracy Assessment of GlobeLand30 2010 Land Cover over China Based on Geographically and Categorically Stratified Validation Sample Data." Remote Sensing 10, no. 8 (2018): 1213. http://dx.doi.org/10.3390/rs10081213.

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Land cover information is vital for research and applications concerning natural resources and environmental modeling. Accuracy assessment is an important dimension in use and production of land cover information. GlobeLand30 is a relatively new global land cover information product with a fine spatial resolution of 30 m and is potentially useful for many applications. This paper describes the methods for and results from the first country-wide and statistically based accuracy assessment of GlobeLand30 2010 land cover dataset over China. For this, a total of 8400 validation sample pixels were

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REINERT, JOHN F. "Redescription of the holotype of Finlaya lepchana Barraud, 1923 (Diptera: Culicidae: Aedini)." Zootaxa 1767, no. 1 (2008): 64. http://dx.doi.org/10.11646/zootaxa.1767.1.4.

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Barraud (1923a) provided the original description of Finlaya lepchana and based it on the holotype male and two other males from India, Tindharia, Darjeeling Hills, which were reared from larvae collected from bamboo stumps in October 1922. The description is brief and lacks many morphological details. Therefore, the holotype male, deposited in the Natural History Museum (NHM), London, United Kingdom, is redescribed below. The specimen is mounted on a minuten pin extending more or less vertically through the thorax between the mesal margins of the upper proepisterna and exiting the scutum ante

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Maas, A., F. Rottensteiner, and C. Heipke. "USING LABEL NOISE ROBUST LOGISTIC REGRESSION FOR AUTOMATED UPDATING OF TOPOGRAPHIC GEOSPATIAL DATABASES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 133–40. http://dx.doi.org/10.5194/isprsannals-iii-7-133-2016.

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Supervised classification of remotely sensed images is a classical method to update topographic geospatial databases. The task requires training data in the form of image data with known class labels, whose generation is time-consuming. To avoid this problem one can use the labels from the outdated database for training. As some of these labels may be wrong due to changes in land cover, one has to use training techniques that can cope with wrong class labels in the training data. In this paper we adapt a label noise tolerant training technique to the problem of database updating. No labelled d

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Maas, A., F. Rottensteiner, and C. Heipke. "USING LABEL NOISE ROBUST LOGISTIC REGRESSION FOR AUTOMATED UPDATING OF TOPOGRAPHIC GEOSPATIAL DATABASES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 133–40. http://dx.doi.org/10.5194/isprs-annals-iii-7-133-2016.

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Supervised classification of remotely sensed images is a classical method to update topographic geospatial databases. The task requires training data in the form of image data with known class labels, whose generation is time-consuming. To avoid this problem one can use the labels from the outdated database for training. As some of these labels may be wrong due to changes in land cover, one has to use training techniques that can cope with wrong class labels in the training data. In this paper we adapt a label noise tolerant training technique to the problem of database updating. No labelled d

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Frieden, Joyce. "CMS to Cover Some Off-Label Cancer Drugs." Internal Medicine News 38, no. 21 (2005): 55. http://dx.doi.org/10.1016/s1097-8690(05)72278-2.

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Maas, A., F. Rottensteiner, and C. Heipke. "CLASSIFICATION UNDER LABEL NOISE BASED ON OUTDATED MAPS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-1/W1 (May 30, 2017): 215–22. http://dx.doi.org/10.5194/isprs-annals-iv-1-w1-215-2017.

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Supervised classification of remotely sensed images is a classical method for change detection. The task requires training data in the form of image data with known class labels, whose manually generation is time-consuming. If the labels are acquired from the outdated map, the classifier must cope with errors in the training data. These errors, referred to as label noise, typically occur in clusters in object space, because they are caused by land cover changes over time. In this paper we adapt a label noise tolerant training technique for classification, so that the fact that changes affect l

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Frieden, Joyce. "Medicare to Cover Some Off-Label Cancer Drug Trials." Skin & Allergy News 36, no. 11 (2005): 36. http://dx.doi.org/10.1016/s0037-6337(05)70820-2.

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HAMID, I. SAHUL, and A. ANITHA. "ON THE LABEL GRAPHOIDAL COVERING NUMBER-II." Discrete Mathematics, Algorithms and Applications 03, no. 01 (2011): 1–7. http://dx.doi.org/10.1142/s179383091100095x.

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Let G = (V, E) be a graph with p vertices and q edges. An acyclic graphoidal cover of G is a collection ψ of paths in G which are internally disjoint and covering each edge of the graph exactly once. Let f : V → {1, 2, …, p} be a labeling of the vertices of G. Let ↑Gf be the directed graph obtained by orienting the edges uv of G from u to v provided f(u) < f(v). If the set ψf of all maximal directed paths in ↑Gf, with directions ignored, is an acyclic graphoidal cover of G, then f is called a graphoidal labeling of G and G is called a label graphoidal graph and ηl = min {|ψf|: f is a grapho

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Wu, Guangming, Yimin Guo, Xiaoya Song, et al. "A Stacked Fully Convolutional Networks with Feature Alignment Framework for Multi-Label Land-cover Segmentation." Remote Sensing 11, no. 9 (2019): 1051. http://dx.doi.org/10.3390/rs11091051.

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Applying deep-learning methods, especially fully convolutional networks (FCNs), has become a popular option for land-cover classification or segmentation in remote sensing. Compared with traditional solutions, these approaches have shown promising generalization capabilities and precision levels in various datasets of different scales, resolutions, and imaging conditions. To achieve superior performance, a lot of research has focused on constructing more complex or deeper networks. However, using an ensemble of different fully convolutional models to achieve better generalization and to preven

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FRANKLIN, DEEANNA. "Medicare May No Longer Cover Off-Label Use of Nesiritide." Internal Medicine News 39, no. 2 (2006): 8. http://dx.doi.org/10.1016/s1097-8690(06)72674-9.

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KELLY, CATHY. "Medicare to Cover On-Label Use of Prostate Ca Vaccine." Internal Medicine News 44, no. 7 (2011): 47. http://dx.doi.org/10.1016/s1097-8690(11)70348-1.

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Brakensiek, Joshua, and Venkatesan Guruswami. "The Quest for Strong Inapproximability Results with Perfect Completeness." ACM Transactions on Algorithms 17, no. 3 (2021): 1–35. http://dx.doi.org/10.1145/3459668.

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The Unique Games Conjecture has pinned down the approximability of all constraint satisfaction problems (CSPs), showing that a natural semidefinite programming relaxation offers the optimal worst-case approximation ratio for any CSP. This elegant picture, however, does not apply for CSP instances that are perfectly satisfiable, due to the imperfect completeness inherent in the Unique Games Conjecture. This work is motivated by the pursuit of a better understanding of the approximability of perfectly satisfiable instances of CSPs. We prove that an “almost Unique” version of Label Cover can be a

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Lin, Chuang, Shanxin Guo, Jinsong Chen, et al. "Deep Learning Network Intensification for Preventing Noisy-Labeled Samples for Remote Sensing Classification." Remote Sensing 13, no. 9 (2021): 1689. http://dx.doi.org/10.3390/rs13091689.

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The deep-learning-network performance depends on the accuracy of the training samples. The training samples are commonly labeled by human visual investigation or inherited from historical land-cover or land-use maps, which usually contain label noise, depending on subjective knowledge and the time of the historical map. Helping the network to distinguish noisy labels during the training process is a prerequisite for applying the model for training across time and locations. This study proposes an antinoise framework, the Weight Loss Network (WLN), to achieve this goal. The WLN contains three m

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Xing, Dingfan, Stephen V. Stehman, Giles M. Foody, and Bruce W. Pengra. "Comparison of Simple Averaging and Latent Class Modeling to Estimate the Area of Land Cover in the Presence of Reference Data Variability." Land 10, no. 1 (2021): 35. http://dx.doi.org/10.3390/land10010035.

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Estimates of the area or percent area of the land cover classes within a study region are often based on the reference land cover class labels assigned by analysts interpreting satellite imagery and other ancillary spatial data. Different analysts interpreting the same spatial unit will not always agree on the land cover class label that should be assigned. Two approaches for accommodating interpreter variability when estimating the area are simple averaging (SA) and latent class modeling (LCM). This study compares agreement between area estimates obtained from SA and LCM using reference data

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Buhori, Ahmad. "[RESENSI] TEORI KAJIAN BUDAYA DAN TERAPANNYA." RELIGI JURNAL STUDI AGAMA-AGAMA 10, no. 2 (2016): 279. http://dx.doi.org/10.14421/rejusta.2014.1002-08.

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Judul Buku: Cultural Studies di PTAI, Teori dan PraktekEditor: Ustadi Hamzah, Fahruddin Faiz, MuryanaTebal: 226 halaman (termasuk Cover)Penerbit: Laboratorium Religi dan Budaya Lokal (LABeL) Fakultas Ushuluddin, Studi Agama dan Pemikiran IslamCetakan Pertama: Tahun 2014

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Wang, Zheng, Qiao Wang, Tanjie Zhu, and Xiaojun Ye. "Extending LINE for Network Embedding With Completely Imbalanced Labels." International Journal of Data Warehousing and Mining 16, no. 3 (2020): 20–36. http://dx.doi.org/10.4018/ijdwm.2020070102.

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Network embedding is a fundamental problem in network research. Semi-supervised network embedding, which benefits from labeled data, has recently attracted considerable interest. However, existing semi-supervised methods would get biased results in the completely-imbalanced label setting where labeled data cannot cover all classes. This article proposes a novel network embedding method which could benefit from completely-imbalanced labels by approximately guaranteeing both intra-class similarity and inter-class dissimilarity. In addition, the authors prove and adopt the matrix factorization fo

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Handiseni, Maxwell, Young-Ki Jo, and Xin-Gen (Shane) Zhou. "Integration of Brassica Cover Crop with Host Resistance and Azoxystrobin for Management of Rice Sheath Blight." Plant Disease 99, no. 6 (2015): 883–85. http://dx.doi.org/10.1094/pdis-08-14-0845-re.

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Sheath blight caused by Rhizoctonia solani is the most important rice disease that can cause significant losses in grain yield and quality in the southern United States. Current management options for sheath blight primarily consist of fungicides, tolerant cultivars, and cultural practices. These options are not always very effective. Brassica plants have been used for soil fumigation to manage a variety of different soilborne pathogens. In this field study, the efficacy of a Brassica juncea cover crop integrated with use of a tolerant rice cultivar and fungicide application was evaluated in 2

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Peleg, David. "Approximation algorithms for the Label-CoverMAX and Red-Blue Set Cover problems." Journal of Discrete Algorithms 5, no. 1 (2007): 55–64. http://dx.doi.org/10.1016/j.jda.2006.03.008.

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Contreras, I. C., M. Khodadadzadeh, and R. Gloaguen. "MULTI-LABEL CLASSIFICATION FOR DRILL-CORE HYPERSPECTRAL MINERAL MAPPING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 383–88. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-383-2020.

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Abstract. A multi-label classification concept is introduced for the mineral mapping task in drill-core hyperspectral data analysis. As opposed to traditional classification methods, this approach has the advantage of considering the different mineral mixtures present in each pixel. For the multi-label classification, the well-known Classifier Chain method (CC) is implemented using the Random Forest (RF) algorithm as the base classifier. High-resolution mineralogical data obtained from Scanning Electron Microscopy (SEM) instrument equipped with the Mineral Liberation Analysis (MLA) software ar

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WILFONG, GORDON. "NEAREST NEIGHBOR PROBLEMS." International Journal of Computational Geometry & Applications 02, no. 04 (1992): 383–416. http://dx.doi.org/10.1142/s0218195992000226.

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Suppose E is a set of labeled points (examples) in some metric space. A subset C of E is said to be a consistent subset ofE if it has the property that for any example e∈E, the label of the closest example in C to e is the same as the label of e. We consider the problem of computing a minimum cardinality consistent subset. Consistent subsets have applications in pattern classification schemes that are based on the nearest neighbor rule. The idea is to replace the training set of examples with as small a consistent subset as possible so as to improve the efficiency of the system while not signi

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Ortega-Tenezaca, Bernabe, Viviana Quevedo-Tumailli, Harbil Bediaga, et al. "PTML Multi-Label Algorithms: Models, Software, and Applications." Current Topics in Medicinal Chemistry 20, no. 25 (2020): 2326–37. http://dx.doi.org/10.2174/1568026620666200916122616.

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By combining Machine Learning (ML) methods with Perturbation Theory (PT), it is possible to develop predictive models for a variety of response targets. Such combination often known as Perturbation Theory Machine Learning (PTML) modeling comprises a set of techniques that can handle various physical, and chemical properties of different organisms, complex biological or material systems under multiple input conditions. In so doing, these techniques effectively integrate a manifold of diverse chemical and biological data into a single computational framework that can then be applied for screenin

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Harvey, Jessica H., and Dirk Trauner. "Cover Picture: Regulating Enzymatic Activity with a Photoswitchable Affinity Label (ChemBioChem 2/2008)." ChemBioChem 9, no. 2 (2008): 165. http://dx.doi.org/10.1002/cbic.200890000.

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Austrin, Per, Ryan O’Donnell, Li-Yang Tan, and John Wright. "New NP-Hardness Results for 3-Coloring and 2-to-1 Label Cover." ACM Transactions on Computation Theory 6, no. 1 (2014): 1–20. http://dx.doi.org/10.1145/2537800.

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Voelsen, M., D. Lobo Torres, R. Q. Feitosa, F. Rottensteiner, and C. Heipke. "INVESTIGATIONS ON FEATURE SIMILARITY AND THE IMPACT OF TRAINING DATA FOR LAND COVER CLASSIFICATION." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2021 (June 17, 2021): 181–89. http://dx.doi.org/10.5194/isprs-annals-v-3-2021-181-2021.

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Abstract. Fully convolutional neural networks (FCN) are successfully used for pixel-wise land cover classification - the task of identifying the physical material of the Earth’s surface for every pixel in an image. The acquisition of large training datasets is challenging, especially in remote sensing, but necessary for a FCN to perform well. One way to circumvent manual labelling is the usage of existing databases, which usually contain a certain amount of label noise when combined with another data source. As a first part of this work, we investigate the impact of training data on a FCN. We

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Kaps, Martin L., and Marilyn B. Odneal. "Split and Tank-mix Preemergence Application of Herbicide for Controlling Weeds in Grapes." HortScience 29, no. 6 (1994): 619–20. http://dx.doi.org/10.21273/hortsci.29.6.619.

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Spring vs. fall plus spring (split) herbicide application times and single vs. tank-mix spring herbicide applications were compared as a means of extending summer annual weed control in vineyards. About 30% of the nontreated control areas were weed-covered by April or May of each of 3 years. Most treatments gave 60 or more days of acceptable annual weed control (≤ 30% cover) beyond the nontreated control. Fall plus spring application of diuron, norflurazon, or simazine at the half-label rate did not increase the days of control over spring application alone at the full-label rate. The tank-mix

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Zhao, Chuanpeng, and Yaohuan Huang. "A Deep Neural Networks Approach for Augmenting Samples of Land Cover Classification." Land 9, no. 8 (2020): 271. http://dx.doi.org/10.3390/land9080271.

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Land cover is one of key indicators for modeling ecological, environmental, and climatic processes, which changes frequently due to natural factors and anthropogenic activities. The changes demand various samples for updating land cover maps, although in reality the number of samples is always insufficient. Sample augment methods can fill this gap, but these methods still face difficulties, especially for high-resolution remote sensing data. The difficulties include the following: (1) excessive human involvement, which is mostly caused by human interpretation, even by active learning-based met

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Wang, Sherrie, William Chen, Sang Michael Xie, George Azzari, and David B. Lobell. "Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery." Remote Sensing 12, no. 2 (2020): 207. http://dx.doi.org/10.3390/rs12020207.

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Accurate automated segmentation of remote sensing data could benefit applications from land cover mapping and agricultural monitoring to urban development surveyal and disaster damage assessment. While convolutional neural networks (CNNs) achieve state-of-the-art accuracy when segmenting natural images with huge labeled datasets, their successful translation to remote sensing tasks has been limited by low quantities of ground truth labels, especially fully segmented ones, in the remote sensing domain. In this work, we perform cropland segmentation using two types of labels commonly found in re

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Zhang, Xiaokang, Wenzhong Shi, and Zhiyong Lv. "Uncertainty Assessment in Multitemporal Land Use/Cover Mapping with Classification System Semantic Heterogeneity." Remote Sensing 11, no. 21 (2019): 2509. http://dx.doi.org/10.3390/rs11212509.

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Land use/cover (LUC) data are commonly relied on to provide land surface information in a variety of applications. However, the exchange and joint use of LUC information from different datasets can be challenging due to semantic differences between common classification systems (CSs). In this paper, we propose an uncertainty assessment schema to capture the semantic translation uncertainty between heterogeneous LUC CSs and evaluate the data label uncertainty of multitemporal LUC mapping results caused by uncertainty propagation. The semantic translation uncertainty between CSs is investigated

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Dinitz, Michael, Guy Kortsarz, and Ran Raz. "Label Cover Instances with Large Girth and the Hardness of Approximating Basic k -Spanner." ACM Transactions on Algorithms 12, no. 2 (2016): 1–16. http://dx.doi.org/10.1145/2818375.

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Dong, Runmin, Cong Li, Haohuan Fu, et al. "Improving 3-m Resolution Land Cover Mapping through Efficient Learning from an Imperfect 10-m Resolution Map." Remote Sensing 12, no. 9 (2020): 1418. http://dx.doi.org/10.3390/rs12091418.

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Substantial progress has been made in the field of large-area land cover mapping as the spatial resolution of remotely sensed data increases. However, a significant amount of human power is still required to label images for training and testing purposes, especially in high-resolution (e.g., 3-m) land cover mapping. In this research, we propose a solution that can produce 3-m resolution land cover maps on a national scale without human efforts being involved. First, using the public 10-m resolution land cover maps as an imperfect training dataset, we propose a deep learning based approach that

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Bonhôte, Pierre. "Le bois du Jura – de la tradition à l'appellation d'origine contrôlée (AOC) | Wood from Jura – from tradition to a label of origin (AOC)." Schweizerische Zeitschrift fur Forstwesen 157, no. 7 (2006): 260–62. http://dx.doi.org/10.3188/szf.2006.0260.

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Within the context of a very competitive market characterised by prices that hardly cover the costs of production the creation of a label of origin (AOC) for the resinous wood of the Jura region seem to offer a promising way profiling a well-known product of high quality. According to an increasing number of scientific studies carried out within the framework of an Interreg project, the Swiss association for an AOC «wood from Jura»and its French counterpart have submitted an application for the recognition of an AOC label for the basic transformed products of resinous wood from this region. In

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YANG, HUA, MASAAKI KASHIMURA, NORIKADU ONDA, and SHINJI OZAWA. "EXTRACTION OF BIBLIOGRAPHY INFORMATION BASED ON IMAGE OF BOOK COVER." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 07 (2000): 963–78. http://dx.doi.org/10.1142/s0218001400000611.

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This paper describes a new system for extracting and classifying bibliography regions from the color image of a book cover. The system consists of three major components: preprocessing, color space segmentation and text region extraction and classification. Preprocessing extracts the edge lines of the book and geometrically corrects and segments the input image, into the parts of front cover, spine and back cover. The same as all color image processing researches, the segmentation of color space is an essential and important step here. Instead of RGB color space, HSI color space is used in thi

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Cui, Guoqing, Zhiyong Lv, Guangfei Li, Jón Atli Benediktsson, and Yudong Lu. "Refining Land Cover Classification Maps Based on Dual-Adaptive Majority Voting Strategy for Very High Resolution Remote Sensing Images." Remote Sensing 10, no. 8 (2018): 1238. http://dx.doi.org/10.3390/rs10081238.

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Land cover classification that uses very high resolution (VHR) remote sensing images is a topic of considerable interest. Although many classification methods have been developed, the accuracy and usability of classification systems can still be improved. In this paper, a novel post-processing approach based on a dual-adaptive majority voting strategy (D-AMVS) is proposed to improve the performance of initial classification maps. D-AMVS defines a strategy for refining each label of a classified map that is obtained by different classification methods from the same original image, and fusing th

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Lin, Cong, Peijun Du, Alim Samat, Erzhu Li, Xin Wang, and Junshi Xia. "Automatic Updating of Land Cover Maps in Rapidly Urbanizing Regions by Relational Knowledge Transferring from GlobeLand30." Remote Sensing 11, no. 12 (2019): 1397. http://dx.doi.org/10.3390/rs11121397.

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Land-cover map is the basis of research and application related to urban planning, environmental management and ecological protection. Land-cover updating is an essential task especially in a rapidly urbanizing region, where fast development makes it necessary to monitor land-cover change in a timely manner. However, conventional approaches always have the limitations of large amounts of sample collection and exploitation of relational knowledge between multi-modality remote sensing datasets. With some global land-cover products being available, it is important to produce new land-cover maps b

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Mas, J. F., and R. González. "CHANGE DETECTION AND LAND USE / LAND COVER DATABASE UPDATING USING IMAGE SEGMENTATION, GIS ANALYSIS AND VISUAL INTERPRETATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3 (August 19, 2015): 61–65. http://dx.doi.org/10.5194/isprsarchives-xl-3-w3-61-2015.

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This article presents a hybrid method that combines image segmentation, GIS analysis, and visual interpretation in order to detect discrepancies between an existing land use/cover map and satellite images, and assess land use/cover changes. It was applied to the elaboration of a multidate land use/cover database of the State of Michoacán, Mexico using SPOT and Landsat imagery. The method was first applied to improve the resolution of an existing 1:250,000 land use/cover map produced through the visual interpretation of 2007 SPOT images. A segmentation of the 2007 SPOT images was carried out to

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Liu, Jun, Bin Luo, Qianqing Qin, and Guopeng Yang. "Alike Scene Retrieval from Land-Cover Products Based on the Label Co-Occurrence Matrix (LCM) †." Remote Sensing 9, no. 9 (2017): 912. http://dx.doi.org/10.3390/rs9090912.

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Lu, Qikai, Xin Huang, Tingting Liu, and Liangpei Zhang. "A structural similarity-based label-smoothing algorithm for the post-processing of land-cover classification." Remote Sensing Letters 7, no. 5 (2016): 437–45. http://dx.doi.org/10.1080/2150704x.2016.1149252.

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Kaps, Martin L., and Marilyn B. Odneal. "FALL, SPLIT, AND TANK-MIX APPLICATION METHODS AS ALTERNATIVES TO SPRING PREEMERGENT HERBICIDE APPLICATION." HortScience 27, no. 6 (1992): 627g—628. http://dx.doi.org/10.21273/hortsci.27.6.627g.

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Preemergent herbicides were applied to vineyards in the southcentral Missouri Ozark region. These were applied at full label rate in the fall or in the spring, at half rate in the fall and again in the spring, and as tank-mixes in the spring. Days of acceptable annual weed control (30% or less cover) beyond the untreated control were determined for these application methods over three years. The fall applications were effective at controlling winter annual weeds and early summer annual weed growth the following season. By mid summer the fall applied preemergents lost residual activity. Splitti

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Dabia, Said, Stefan Ropke, and Tom van Woensel. "Cover Inequalities for a Vehicle Routing Problem with Time Windows and Shifts." Transportation Science 53, no. 5 (2019): 1354–71. http://dx.doi.org/10.1287/trsc.2018.0885.

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This paper introduces the vehicle routing problem with time windows and shifts (VRPTWS). At the depot, several shifts with nonoverlapping operating periods are available to load the planned trucks. Each shift has a limited loading capacity. We solve the VRPTWS exactly by a branch-and-cut-and-price algorithm. The master problem is a set partitioning with an additional constraint for every shift. Each constraint requires the total quantity loaded in a shift to be less than its loading capacity. For every shift, a pricing subproblem is solved by a label-setting algorithm. Shift capacity constrain

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Palhano, Matheus G., Jason K. Norsworthy, and Tom Barber. "Sensitivity and Likelihood of Residual Herbicide Carryover to Cover Crops." Weed Technology 32, no. 3 (2018): 236–43. http://dx.doi.org/10.1017/wet.2018.7.

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AbstractResearch was conducted to evaluate the sensitivity of cover crops to a low rate of soil-applied herbicides and investigate the likelihood of herbicide carryover to fall-seeded cover crops following an irrigated corn crop. In the sensitivity study, herbicides were applied at a 1/16×rate (to simulate four half-lives) 1 d after cover crop planting, whereas for the carryover study residual herbicides were applied at a 2×rate at the maximum label corn height or growth stage and cover crops sown immediately after corn harvest. In the sensitivity experiment, atrazine, diuron, fluridone, fomes

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Onken, Ulrich, Agnieszka Koettgen, Holger Scheidat, Patrick Schueepp, and Fabrice Gallou. "Environmental Metrics to Drive a Cultural Change: Our Green Eco-Label." CHIMIA International Journal for Chemistry 73, no. 9 (2019): 730–36. http://dx.doi.org/10.2533/chimia.2019.730.

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A novel Green Chemistry Process Scorecard was developed to assess the environmental impact of chemical production processes to manufacture the Active Pharmaceutical Ingredients (API) within our portfolio. These new metrics not only cover the resource consumption from the overall chemical synthesis, but also consider the use of Substances of Concern and the number of chemical transformations. The Process Mass Intensity (PMI), i.e. the ratio of accumulated kilogram quantities of materials per kilogram of API, is used to quantify the resource consumption. An 'eco-label' for specific APIs is used

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Yang, C., F. Rottensteiner, and C. Heipke. "TOWARDS BETTER CLASSIFICATION OF LAND COVER AND LAND USE BASED ON CONVOLUTIONAL NEURAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 4, 2019): 139–46. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-139-2019.

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<p><strong>Abstract.</strong> Land use and land cover are two important variables in remote sensing. Commonly, the information of land use is stored in geospatial databases. In order to update such databases, we present a new approach to determine the land cover and to classify land use objects using convolutional neural networks (CNN). High-resolution aerial images and derived data such as digital surface models serve as input. An encoder-decoder based CNN is used for land cover classification. We found a composite including the infrared band and height data to outperform RG

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Sitasari, Almira, and Andika Trisurini. "Comic Education Media On Food Label With Folklore Character For Children." Jurnal Teknologi Kesehatan (Journal of Health Technology) 14, no. 1 (2018): 5–13. http://dx.doi.org/10.29238/jtk.v14i1.85.

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Lack of awareness in food label reading could be a problem of eating habit in Indonesian especially in youth. School-aged children are susceptible group to have risk from unsafe packaged snack food. Comic book would be one of good education media for them. Aim of the study was to raise awareness to food label reading and to make a good media to educate youth to read packaged food/snack label. This study was a research and development study (R and D study) with 4 D approach (define, design, develop, disseminate). After designing the media (including storyline and cover design) the media was rat

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Crawford, Ewan. "Them and us: Why they are nationalists and we are not. An analysis of journalists’ language in relation to others." Journalism 13, no. 5 (2011): 620–38. http://dx.doi.org/10.1177/1464884911431369.

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Liberal opinion often views nationalism as a distasteful and reactionary concept. But what does it mean to be a nationalist? This article seeks to investigate what selected newspapers in the UK mean when they label a political party or an individual as nationalist. An initial content analysis demonstrates that journalists use the label to cover a variety of movements and individuals with disparate political and cultural goals. Making use of the banal nationalism concept and the idea of strategy in discourse analysis, it is suggested here that these disparate groups are brought together under t

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Voelsen, M., J. Bostelmann, A. Maas, F. Rottensteiner, and C. Heipke. "AUTOMATICALLY GENERATED TRAINING DATA FOR LAND COVER CLASSIFICATION WITH CNNS USING SENTINEL-2 IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 767–74. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-767-2020.

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Abstract. Pixel-wise classification of remote sensing imagery is highly interesting for tasks like land cover classification or change detection. The acquisition of large training data sets for these tasks is challenging, but necessary to obtain good results with deep learning algorithms such as convolutional neural networks (CNN). In this paper we present a method for the automatic generation of a large amount of training data by combining satellite imagery with reference data from an available geospatial database. Due to this combination of different data sources the resulting training data

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Yang, Chun, Franz Rottensteiner, and Christian Heipke. "CLASSIFICATION OF LAND COVER AND LAND USE BASED ON CONVOLUTIONAL NEURAL NETWORKS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-3 (April 23, 2018): 251–58. http://dx.doi.org/10.5194/isprs-annals-iv-3-251-2018.

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Land cover describes the physical material of the earth’s surface, whereas land use describes the socio-economic function of a piece of land. Land use information is typically collected in geospatial databases. As such databases become outdated quickly, an automatic update process is required. This paper presents a new approach to determine land cover and to classify land use objects based on convolutional neural networks (CNN). The input data are aerial images and derived data such as digital surface models. Firstly, we apply a CNN to determine the land cover for each pixel of the input image

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