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M Fadil Ramadhan and M. Subur Drajat. "Kegiatan Marketing Pr Label Musik Digital Audio Tape Bandung." Jurnal Riset Public Relations 1, no. 1 (July 6, 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 achievement of a a particular purpose, policy, production activities the progress of institution or a positive image of institutions concerned. The public function of relations at the labels music Digital Audio Tape to increase the consumers which will perform recording on music label Digital Audio Tape. The purpose of this research that is to know the marketing public relations done by music labels Digital Audio Tape. The methodology qualitative perspective case study by Robert K. Yin more trying to map technique single case analysis in the analysis the marketing public relations done by music labels Digital Audio Tape. The research music label the concept of representatives using three with the creation event showcase cover tune in instagram, created an impromptu event or shocking venues, combining vocal technique of various genre of music becomes hip hop.
Abstrak. Label music Digital Audio Tape memiliki kegiatan marketing Public Relations agar jasanya dapat digunakan oleh khalayak. Kegiatan marketing Public Relations label tersebut dibuat oleh Trizha Harun selaku Public Relations pada label musik Digital Audio Tape. (Ruslan 2008 :249), Public Relations berfungsi untuk menjalin komunikasi dua arah antara perusahaan dengan publik internal dan eksternal serta membina hubungan yang saling menguntungkan dengan khalayak atau pihak yang dianggap penting oleh label. Aktivitas Public Relations adalah menyelenggarakan komunikasi timbal balik antara lembaga dengan publik yang bertujuan untuk menciptakan saling pengertian dan dukungan bagi tercapainya suatu tujuan tertentu, kebijakan, kegiatan produksi demi kemajuan lembaga atau citra positif lembaga bersangkutan. Fungsi Public Relations pada label musik Digital Audio Tape untuk meningkatkan pelaku konsumen yang akan melakukan rekaman di label musik Digital Audio Tape. Tujuan penelitian ini yaitu untuk mengetahui kegiatan marketing public relations yang dilakukan oleh label musik Digital Audio Tape. Metode penelitian kualitatif dengan perspektif studi kasus Robert K. Yin yang lebih berupaya memetakan teknik single case analysis pada analisis kegiatan marketing public relations yang dilakukan oleh label musik Digital Audio Tape. Dari hasil penelitian label musik menggunakan tiga konsep MPR dengan cara menciptakan event showcase cover lagu di instagram, menciptakan mengadakan event dadakan atau (shocking venue), menggabungkan teknik vocal dari berbagai genre musik menjadi hip hop.
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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 (December 31, 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 evaluate four different covers with respect to broad audience appeal. Furthermore, they are asked to give a short description of the content they would expect in the different books. The cover samples are identical for the two groups, but alternating genre labels have been removed in one of the groups while being preserved in the other. Based on the responses, it is confirmed that genre labels have a significant effect on the reception of and expectations to the work. It is also demonstrated how the commercial and artistic appeal varies among the four books indicating an effect of the paratext as a whole. It is indicated by its cover, to what extent a book targets commercial interests or strives for a more field specific capital and recognition – and the genre label is a salient part of this positioning.
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Charikar, Moses, MohammadTaghi Hajiaghayi, and Howard Karloff. "Improved Approximation Algorithms for Label Cover Problems." Algorithmica 61, no. 1 (February 3, 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 (March 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 (August 2, 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 collected based on a sampling design featuring two levels of stratification (ten geographical regions, each with nine or eight land-cover classes). Validation sample data with reference class labels were acquired from visual interpretation based on Google Earth high-resolution satellite images. Error matrices for individual regions and entire China were estimated properly based on the sampling design adopted, with the former aggregated to get the latter through suitable weighting. Results were obtained, with agreement at a sample pixel defined both as a match between the map (class) label and either the primary or alternate reference label therein and, more strictly, as a match between the map label and the primary reference label only. Based on the former definition of agreement, the overall accuracy of GlobeLand30 2010 land cover for China was assessed to be 84.2%. User’s accuracy and producer’s accuracy were both greater than 80% for cultivated land, forest, permanent snow and ice, and bareland, with user’s accuracy for water bodies estimated 94.2% (82.1% for wetland, 79.8% for artificial surface) and producer’s accuracy for grassland estimated 89.0%. These indicate that GlobeLand30 2010 depicts land cover circa 2010 in China quite accurately, although estimates of accuracy indicators based on the latter definition of agreement were lower as expected with an estimated national overall accuracy of 81.0%. Regional and class variations in accuracy were revealed and examined in the light of their associations with land cover distributions and patterns. Implications for use and production of GlobeLand30 land cover information were discussed, so were commonality and lack of it between GlobeLand30 and other fine-resolution land cover products.
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REINERT, JOHN F. "Redescription of the holotype of Finlaya lepchana Barraud, 1923 (Diptera: Culicidae: Aedini)." Zootaxa 1767, no. 1 (May 12, 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 anterior to the prescutellar area. The lower part of the minuten pin is inserted in a rectangular, yellow, plastic stage which is attached near its margin to an insect pin. Four labels are attached to the insect pin and include the following information: Finlaya lepchana Barr., % TYPE (handwritten in black ink except TYPE is typed on a red, paper, triangular label); terminalia ON SLIDE (hand printed in black ink on a white, paper, rectangular label); India: Tindaria, Darjiling Hills, IX.1922, bamboos, Capt. P.J. Barraud, B.M. 1923-207 (partially handwritten and remainder typed in black ink on a white, paper, rectangular label); HOLO- TYPE (printed in black ink on a small, circular, white, paper label with red border). The male genitalia, previously mounted on a microscope slide, have two labels as follow: AЁDES (FINLAYA) LEPCHANA Barr., INDIA, TINDHARIA, DARJEELING Dist., x.1922, from Bamboos, Coll. P. J. BARRAUD, B.M. 1923-207 (hand printed in black ink on a rectangular, brown paper label attached to the left side of the slide); HOLO- TYPE (label printed on a small, circular, white paper label with red border attached to the left side of the slide). The genitalia are mounted beneath a square cover slip and five psocids are embedded in the mounting medium on the margin of the cover slip. The spelling of “Tindaria” and “Darjiling” on the adult label differs from that reported by Barraud (1923a).
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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 data other than the existing database are necessary. The resulting label image and transition matrix between the labels can help to update the database and to detect changes between the two time epochs. Our experiments are based on different test areas, using real images with simulated existing databases. Our results show that this method can indeed detect changes that would remain undetected if label noise were not considered in training.
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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 data other than the existing database are necessary. The resulting label image and transition matrix between the labels can help to update the database and to detect changes between the two time epochs. Our experiments are based on different test areas, using real images with simulated existing databases. Our results show that this method can indeed detect changes that would remain undetected if label noise were not considered in training.
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Frieden, Joyce. "CMS to Cover Some Off-Label Cancer Drugs." Internal Medicine News 38, no. 21 (November 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 larger clusters of pixels is considered. We also integrate the existing map into an iterative classification procedure to act as a prior in regions which are likely to contain changes. Our experiments are based on three test areas, using real images with simulated existing databases. Our results show that this method helps to distinguish between real changes over time and false detections caused by misclassification and thus improves the accuracy of the classification results.
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Frieden, Joyce. "Medicare to Cover Some Off-Label Cancer Drug Trials." Skin & Allergy News 36, no. 11 (November 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 (March 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 graphoidal labeling of G} is called the label graphoidal covering number of G. An orientation of G in which every vertex of degree greater than 2 is either a sink or a source is a graphoidal orientation. In this paper we characterize graphs for which (i) ηl = ηa and (ii) ηl = Δ. Also, we discuss the relation between graphoidal labeling and graphoidal orientation.
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Wu, Guangming, Yimin Guo, Xiaoya Song, Zhiling Guo, Haoran Zhang, Xiaodan Shi, Ryosuke Shibasaki, and Xiaowei Shao. "A Stacked Fully Convolutional Networks with Feature Alignment Framework for Multi-Label Land-cover Segmentation." Remote Sensing 11, no. 9 (May 3, 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 prevent overfitting has long been ignored. In this research, we design four stacked fully convolutional networks (SFCNs), and a feature alignment framework for multi-label land-cover segmentation. The proposed feature alignment framework introduces an alignment loss of features extracted from basic models to balance their similarity and variety. Experiments on a very high resolution(VHR) image dataset with six categories of land-covers indicates that the proposed SFCNs can gain better performance when compared to existing deep learning methods. In the 2nd variant of SFCN, the optimal feature alignment gains increments of 4.2% (0.772 vs. 0.741), 6.8% (0.629 vs. 0.589), and 5.5% (0.727 vs. 0.689) for its f1-score, jaccard index, and kappa coefficient, respectively.
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FRANKLIN, DEEANNA. "Medicare May No Longer Cover Off-Label Use of Nesiritide." Internal Medicine News 39, no. 2 (January 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 (April 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 (August 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 approximated within a constant factor on satisfiable instances. Our main conceptual contribution is the formulation of a (hypergraph) version of Label Cover that we call V Label Cover . Assuming a conjecture concerning the inapproximability of V Label Cover on perfectly satisfiable instances, we prove the following implications: • There is an absolute constant c 0 such that for k ≥ 3, given a satisfiable instance of Boolean k -CSP, it is hard to find an assignment satisfying more than c 0 k 2 /2 k fraction of the constraints. • Given a k -uniform hypergraph, k ≥ 2, for all ε > 0, it is hard to tell if it is q -strongly colorable or has no independent set with an ε fraction of vertices, where q =⌈ k +√ k -1/2⌉. • Given a k -uniform hypergraph, k ≥ 3, for all ε > 0, it is hard to tell if it is ( k -1)-rainbow colorable or has no independent set with an ε fraction of vertices.
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Lin, Chuang, Shanxin Guo, Jinsong Chen, Luyi Sun, Xiaorou Zheng, Yan Yang, and Yingfei Xiong. "Deep Learning Network Intensification for Preventing Noisy-Labeled Samples for Remote Sensing Classification." Remote Sensing 13, no. 9 (April 27, 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 main parts: (1) the segmentation subnetwork, which any state-of-the-art segmentation network can replace; (2) the attention subnetwork (λ); and (3) the class-balance coefficient (α). Four types of label noise (an insufficient label, redundant label, missing label and incorrect label) were simulated by dilate and erode processing to test the network’s antinoise ability. The segmentation task was set to extract buildings from the Inria Aerial Image Labeling Dataset, which includes Austin, Chicago, Kitsap County, Western Tyrol and Vienna. The network’s performance was evaluated by comparing it with the original U-Net model by adding noisy training samples with different noise rates and noise levels. The result shows that the proposed antinoise framework (WLN) can maintain high accuracy, while the accuracy of the U-Net model dropped. Specifically, after adding 50% of dilated-label samples at noise level 3, the U-Net model’s accuracy dropped by 12.7% for OA, 20.7% for the Mean Intersection over Union (MIOU) and 13.8% for Kappa scores. By contrast, the accuracy of the WLN dropped by 0.2% for OA, 0.3% for the MIOU and 0.8% for Kappa scores. For eroded-label samples at the same level, the accuracy of the U-Net model dropped by 8.4% for OA, 24.2% for the MIOU and 43.3% for Kappa scores, while the accuracy of the WLN dropped by 4.5% for OA, 4.7% for the MIOU and 0.5% for Kappa scores. This result shows that the antinoise framework proposed in this paper can help current segmentation models to avoid the impact of noisy training labels and has the potential to be trained by a larger remote sensing image set regardless of the inner label error.
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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 (January 4, 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 obtained by seven trained, professional interpreters who independently interpreted an annual time series of land cover reference class labels for 300 sampled Landsat pixels. We also compare the variability of the LCM and SA area estimates over different numbers of interpreters and different subsets of interpreters within each interpreter group size, and examine area estimates of three land cover classes (forest, developed, and wetland) and three change types (forest gain, forest loss, and developed gain). Differences between the area estimates obtained from SA and LCM are most pronounced for the estimates of wetland and the three change types. The percent area estimates of these rare classes were usually greater for LCM compared to SA, with the differences between LCM and SA increasing as the number of interpreters providing the reference data increased. The LCM area estimates generally had larger standard deviations and greater ranges over different subsets of interpreters, indicating greater sensitivity to the selection of the individual interpreters who carried out the reference class labeling.
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Buhori, Ahmad. "[RESENSI] TEORI KAJIAN BUDAYA DAN TERAPANNYA." RELIGI JURNAL STUDI AGAMA-AGAMA 10, no. 2 (August 14, 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 (July 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 form of LINE (a famous network embedding method) as the network structure preserving model. Extensive experiments demonstrate the superiority and robustness of this method.
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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 (June 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 2011, 2012, and 2013. The B. juncea cover crop significantly lowered sheath blight severity in all 3 years and led to a significantly higher grain yield in 2013 as compared with the fallow control. ‘Presidio’ rice had lower sheath blight severity and higher yield than ‘Cocodrie’ in 2012 and 2013. Fungicide applications with azoxystrobin at the label rate (0.16 kg a.i./ha) or half the label rate (0.08 kg a.i./ha) significantly reduced sheath blight severity in all 3 years, resulting in a yield increase in 2 of the 3 years. B. juncea along with use of a tolerant rice cultivar and half the label rate of azoxystrobin can be an effective approach for management of sheath blight in rice.
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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 (March 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 are used for generating the training data set. The drill-core hyperspectral data used in this paper cover the visible-near infrared (VNIR) and the short-wave infrared (SWIR) range of the electromagnetic spectrum. The quantitative and qualitative analysis of the obtained results shows that the multi-label classification approach provides meaningful and descriptive mineral maps and outperforms the single-label RF classification for the mineral mapping task.
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WILFONG, GORDON. "NEAREST NEIGHBOR PROBLEMS." International Journal of Computational Geometry & Applications 02, no. 04 (December 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 significantly affecting its accuracy. The problem of finding a minimum size consistent subset of a set of examples is shown to be NP-complete. A special case is described and is shown to be equivalent to an optimal disc cover problem. A polynomial time algorithm for this optimal disc cover problem is then given.
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Ortega-Tenezaca, Bernabe, Viviana Quevedo-Tumailli, Harbil Bediaga, Jon Collados, Sonia Arrasate, Gotzon Madariaga, Cristian R. Munteanu, M. Natália D. S. Cordeiro, and Humbert González-Díaz. "PTML Multi-Label Algorithms: Models, Software, and Applications." Current Topics in Medicinal Chemistry 20, no. 25 (November 3, 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 screening lead chemicals as well as to find clues for improving the targeted response(s). PTML models have thus been extremely helpful in drug or material design efforts and found to be predictive and applicable across a broad space of systems. After a brief outline of the applied methodology, this work reviews the different uses of PTML in Medicinal Chemistry, as well as in other applications. Finally, we cover the development of software available nowadays for setting up PTML models from large datasets.
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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 (January 25, 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 (March 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 experiment with different amounts of training data, varying w.r.t. the covered area, the available acquisition dates and the amount of label noise. We conclude that the more data is used for training, the better is the generalization performance of the model, and the FCN is able to mitigate the effect of label noise to a high degree. Another challenge is the imbalanced class distribution in most real-world datasets, which can cause the classifier to focus on the majority classes, leading to poor classification performance for minority classes. To tackle this problem, in this paper, we use the cosine similarity loss to force feature vectors of the same class to be close to each other in feature space. Our experiments show that the cosine loss helps to obtain more similar feature vectors, but the similarity of the cluster centers also increases.
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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 (June 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-mixed herbicides diuron, norflurazon, and oryzalin in combinations of any two at the half-label rate were as effective as the full-label rate of these herbicides used alone. Weed control by oxyflurofen or simazine was extended by tank-mixing with oryzalin (half-label rates). Chemical names used: N -(3,4-dichlorophenyl) -N,N -dimethylurea (diuron); 4-chloro-5-(methylamino)-2-(a,a,a-trifluoro-m-tolyl)-3(2 H) -pyridazinone (norflurazon); 3,5-dinitro-N4,N4-dipropyl-sulfanilamide (oryzalin); 2-chloro-l-(3-ethoxy -4-nitrophenoxy)-4-(trifluoromethyl) benzene (oxyfluorfen); and 2-chloro-4,6-bis(ethylamino)-s-triazine (simazine).
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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 (August 13, 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 methods; (2) large variations of segmented land cover objects, which affects the generalization to unseen areas especially for proposed methods that are validated in small study areas. To solve these problems, we proposed a sample augment method incorporating the deep neural networks using a Gaofen-2 image. To avoid error accumulation, the neural network-based sample augment (NNSA) framework employs non-iterative procedure, and augments from 184 image objects with labels to 75,112 samples. The overall accuracy (OA) of NNSA is 20% higher than that of label propagation (LP) in reference to expert interpreted results; the LP has an OA of 61.16%. The accuracy decreases by approximately 10% in the coastal validation area, which has different characteristics from the inland samples. We also compared the iterative and non-iterative strategies without external information added. The results of the validation area containing original samples show that non-iterative methods have a higher OA and a lower sample imbalance. The NNSA method that augments sample size with higher accuracy can benefit the update of land cover information.
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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 (January 7, 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 remote sensing datasets that can be considered sources of “weak supervision”: (1) labels comprised of single geotagged points and (2) image-level labels. We demonstrate that (1) a U-Net trained on a single labeled pixel per image and (2) a U-Net image classifier transferred to segmentation can outperform pixel-level algorithms such as logistic regression, support vector machine, and random forest. While the high performance of neural networks is well-established for large datasets, our experiments indicate that U-Nets trained on weak labels outperform baseline methods with as few as 100 labels. Neural networks, therefore, can combine superior classification performance with efficient label usage, and allow pixel-level labels to be obtained from image labels.
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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 (October 26, 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 using a dynamic semantic reference system (DSRS) model and semantic similarity analysis. An object-based unsupervised change detection algorithm is adopted to determine the probability of changes in land patches, and novel uncertainty metrics are proposed to estimate the patch label uncertainty in LUC maps. The proposed uncertainty assessment schema was validated via experiments on four LUC datasets, and the results confirmed that semantic uncertainty had great impact on data reliability and that the uncertainty metrics could be used in the development of uncertainty controls in multitemporal LUC mapping by referring to uncertainty assessment results. We anticipate our findings will be used to improve the applicability and interoperability of LUC data products.
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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 (February 12, 2016): 1–16. http://dx.doi.org/10.1145/2818375.
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Dong, Runmin, Cong Li, Haohuan Fu, Jie Wang, Weijia Li, Yi Yao, Lin Gan, Le Yu, and Peng Gong. "Improving 3-m Resolution Land Cover Mapping through Efficient Learning from an Imperfect 10-m Resolution Map." Remote Sensing 12, no. 9 (April 30, 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 can effectively transfer the existing knowledge. Then, we improve the efficiency of our method through a network pruning process for national-scale land cover mapping. Our proposed method can take the state-of-the-art 10-m resolution land cover maps (with an accuracy of 81.24% for China) as the training data, enable a transferred learning process that can produce 3-m resolution land cover maps, and further improve the overall accuracy (OA) to 86.34% for China. We present detailed results obtained over three mega cities in China, to demonstrate the effectiveness of our proposed approach for 3-m resolution large-area land cover mapping.
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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 (July 1, 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 return for an adaptation planned in Swiss forest legislation, an AOC could be granted on both sides of the national borders around 2010.
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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 (November 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 this system. The color space is segmented into achromatic and chromatic regions first; and both the achromatic and chromatic regions are segmented further to complete the color space segmentation. Then text region extraction and classification follow. After detecting fundamental features (stroke width and local label width) text regions are determined. By comparing the text regions on front cover with those on spine, all extracted text regions are classified into suitable bibliography categories: author, title, publisher and other information, without applying OCR.
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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 (August 7, 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 the different refined classification maps to generate a final classification result. The proposed D-AMVS contains three main blocks. (1) An adaptive region is generated by gradually extending the region around a central pixel based on two predefined parameters (T1 and T2) to utilize the spatial feature of ground targets in a VHR image. (2) For each classified map, the label of the central pixel is refined according to the majority voting rule within the adaptive region. This is defined as adaptive majority voting. Each initial classified map is refined in this manner pixel by pixel. (3) Finally, the refined classified maps are used to generate a final classification map, and the label of the central pixel in the final classification map is determined by applying AMV again. Each entire classified map is scanned and refined pixel by pixel based on the proposed D-AMVS. The accuracies of the proposed D-AMVS approach are investigated with two remote sensing images with high spatial resolutions of 1.0 m and 1.3 m. Compared with the classical majority voting method and a relatively new post-processing method called the general post-classification framework, the proposed D-AMVS can achieve a land cover classification map with less noise and higher classification accuracies.
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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 (June 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 based on the existing land-cover products and time series images. To this end, a novel transfer learning based automatic approach was proposed for updating land cover maps of rapidly urbanizing regions. In detail, the proposed method is composed of the following three steps. The first is to design a strategy to extract reliable land-cover information from the historical land-cover map for one of the images (source domain). Then, a novel relational knowledge transfer technique is applied to transfer label information. Finally, classifiers are trained on the transferred samples with spatio-spectral features. The experimental results show that aforementioned steps can select sufficient effective samples for target images, and for the main land-cover classes in a rapidly urbanizing region; the results of an updated map show good performance in both precision and vision. Therefore, the proposed approach provides an automatic solution for urban land-cover mapping with a high degree of accuracy.
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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 create spectrally homogeneous objects with a minimum area of two hectares. Through an overlay operation with the outdated map, each segment receives the “majority” category from the map. Furthermore, spectral indices of the SPOT image were calculated for each band and each segment; therefore, each segment was characterized from the images (spectral indices) and the map (class label). In order to detect uncertain areas which present discrepancy between spectral response and class label, a multivariate trimming, which consists in truncating a distribution from its least likely values, was applied. The segments that behave like outliers were detected and labeled as “uncertain” and a probable alternative category was determined by means of a digital classification using a decision tree classification algorithm. Then, the segments were visually inspected in the SPOT image and high resolution imagery to assign a final category. The same procedure was applied to update the map to 2014 using Landsat imagery. As a final step, an accuracy assessment was carried out using verification sites selected from a stratified random sampling and visually interpreted using high resolution imagery and ground truth.
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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 (September 2, 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 (February 18, 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 (June 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. Splitting the label rate between fall and spring was no better than a full rate spring application at increasing the days of acceptable summer annual weed control. Single preemergent spring application performed as well as tank-mixes.
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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 (September 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 constraints define knapsack inequalities; hence we use valid inequalities inspired from knapsack inequalities to strengthen the linear programming relaxation of the master problem when solved by column generation. In particular, we use a family of tailored robust cover inequalities and a family of new nonrobust cover inequalities. Numerical results show that nonrobust cover inequalities significantly improve the algorithm.
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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 (February 27, 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, fomesafen, metribuzin, pyrithiobac, and sulfentrazone reduced emergence of the leguminous cover crops Austrian winterpea, crimson clover, and hairy vetch. However, reduced biomass production of leguminous cover crops in the spring was only observed for atrazine, fluridone, and pyrithiobac. For rapeseed, atrazine, flumioxazin, fluridone, pyrithiobac, pyroxasulfone, sulfentrazone, and tembotrione reduced emergence, but biomass production was reduced only by atrazine and fluridone. Conversely, wheat, cereal rye, barley, oats, and triticale were not affected by soil-applied herbicides. Barley was the only cereal cover crop that showed biomass reduction due to the application of flumioxazin, fluridone, mesotrione,S-metalochlor, and sulfentrazone. In the carryover study, with the exception of crimson clover, Austrian winterpea, cereal rye, hairy vetch, rapeseed, and wheat showed no negative affect on biomass production following a 2×rate of postemergence-applied residual herbicide in corn.
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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 (September 18, 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 to visualize the environmental impact from their chemical synthesis. For an overview of the environmental impact of a complete product portfolio, a diagram of PMI or total waste quantity vs. the number of synthetic steps can also be used as a visualization tool to identify chemical syntheses with a high need for process improvements. Implementation of this process led to a dramatic change of mindset within the organization. It now supports and drives the decision making at Chemical and Analytical Development, and helps to trigger new projects more readily for sustainability reasons.
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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 RGB images in land cover classification. We also propose a CNN-based methodology for the prediction of land use label from the geospatial databases, where we use masks representing object shape, the RGB images and the pixel-wise class scores of land cover as input. For this task, we developed a two-branch network where the first branch considers the whole area of an image, while the second branch focuses on a smaller relevant area. We evaluated our methods using two sites and achieved an overall accuracy of up to 89.6% and 81.7% for land cover and land use, respectively. We also tested our methods for land cover classification using the Vaihingen dataset of the ISPRS 2D semantic labelling challenge and achieved an overall accuracy of 90.7%.</p>
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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 (May 31, 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 rated by 2 media experts, tested in school-aged children, and also requested feedback from school teachers. Quantitative data were shown in percentage of feasibility, while qualitative data were analyzed by saturation of data. The rating test from experts showed that overall comic feasibility was 87,86% (means feasible to use). The testing from users or school-aged children showed that overall comic feasibility was 96,41% (means feasible). The school teachers gave positive feedbacks on comic book. The conclusion of the study is that the comic book about food labelling with folklore character is feasible to use.
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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 (December 6, 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 the banner of nationalist to convey a sense of otherness, in contrast to the natural, timeless world of nation states which the journalists and readers inhabit. Time and space considerations require reporters to use forms of journalism shorthand when reporting on complex situations but it is argued here that the use of the label nationalist does little to enhance understanding of these complex stories. Furthermore, it is argued that, in a UK context, the exclusion by newspapers of those who support the continuation of the current British state from being categorized as nationalist is useful for those who are campaigning against local independence movements.
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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 contain a certain amount of incorrect labels. We evaluate the influence of this so called label noise regarding the time difference between acquisition of the two data sources, the amount of training data and the class structure. We combine Sentinel-2 images with reference data from a geospatial database provided by the German Land Survey Office of Lower Saxony (LGLN). With different training sets we train a fully convolutional neural network (FCN) and classify four land cover classes (Building, Agriculture, Forest, Water). Our results show that the errors in the training samples do not have a large influence on the resulting classifiers. This is probably due to the fact that the noise is randomly distributed and thus, neighbours of incorrect samples are predominantly correct. As expected, a larger amount of training data improves the results, especially for the less well represented classes. Other influences are different illuminations conditions and seasonal effects during data acquisition. To better adapt the classifier to these different conditions they should also be included in the 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. We compare different CNN structures, all of them based on an encoder-decoder structure for obtaining dense class predictions. Secondly, we propose a new CNN-based methodology for the prediction of the land use label of objects from a geospatial database. In this context, we present a strategy for generating image patches of identical size from the input data, which are classified by a CNN. Again, we compare different CNN architectures. Our experiments show that an overall accuracy of up to 85.7&thinsp;% and 77.4&thinsp;% can be achieved for land cover and land use, respectively. The classification of land cover has a positive contribution to the classification of the land use classification.