KİTLE KAYNAĞIN UZAKTAN ALGILAMADA KULLANIMI

Yıl 2018, Cilt: 13 Sayı: 1, 37 - 52, 19.01.2018

Ekrem Saralıoğlu Oğuz Güngör

Öz

Uzaktan algılama yöntemleri ile
elde edilen veriler ile arazi okyanus ve atmosfer özellikleri hakkında bilgi
sağlanır. Bu veriler işlenerek çok çeşitli çevresel araştırmalar ve Coğrafi
Bilgi Sistemi uygulamaları yapılmaktadır. Bilgisayar teknolojilerinde meydana
gelen büyük gelişmelere rağmen, uydu verilerinin analizi ve yorumlamasında
çeşitli zorluklar meydana gelmektedir. Bu zorlukların aşımında kitle kaynak
kullanılabilir. Kitle kaynak; veri elde etme, problem çözme gibi çeşitli
uygulamalarda insanların kullanılmasıdır. Kitle kaynak ile kolayca
çözümlenemeyen problemlere çözüm bulunabildiği gibi, çeşitli uygulamaların
yapımında harcanan zaman, maliyet ve çaba da azalmaktadır. Bu çalışmada
sistematik bir literatür taraması yapılarak, uzaktan algılama biliminde kitle
kaynağın kullanım alanları incelenmiştir. Yapılan çalışma, uzaktan algılama
biliminde kitle kaynağın kullanımının oldukça yeni olduğunu göstermektedir.
Ayrıca bu konudaki makalelerin
çoğunluğunun 2016 ve sonrasında yayınlanmaya başlandığı görülmektedir. Basılan
yayınlara göre kitle kaynak, uzamsal problemlerin çözümünde geleneksel
algoritmalara oranla çok daha doğru sonuç veren çözümler üretebilmektedir.   

Anahtar Kelimeler

Kitle Kaynak, Gönüllü Coğrafi Bilgi, Uzaktan Algılama, Toplum Bilimi, , Sistematik Literatür İncelemesi

Kaynakça

• 1. Jensen, J.R., (2009). Remote Sensing of the Environment: An Earth Resource Perspective 2/E. Pearson Education India.
• 2. Lillesand, T.M. and Kiefer, R.W., (1994). Remote Sensing and Photo Interpretation. 3rd. Edition, John Wiley & Sons: New York.
• 3. Silvestri, S. and Omri, M., (2008). A Method for the Remote Sensing Identification of Uncontrolled Landfills: Formulation and Validation. International Journal of Remote Sensing, 29(4), 975-989.
• 4. Tang, A.P., Ran, C., Wang, L.F., Gai, L.H., and Dai, M., (2009). Intelligent Digital System in Urban Natural Hazard Mitigation, World Congress on Software Engineering, 2, 355-359.
• 5. Jha, R.K., Karnataka, H.C., and Pant, D.N., (2009). Forest Land Use Planning for Thano Range, Dehradun Forest Division, Uttaranchal, Range Management and Agroforestry, 30(1), 72-77.
• 6. Du, P.J., Liu, P., and Luo, Y., (2009). Urban Thermal Environment Simulation and Prediction Based on Remote Sensing and GIS, IEEE International Geoscience and Remote Sensing Symposium, 1-5, 2357-2360.
• 7. Zhang, X.C., Kang, T.J., Wang, H.Y., and Sun, Y., (2010). Analysis on Spatial Structure of Landuse Change Based on Remote Sensing and Geographical Information System, International Journal of Applied Earth Observation and Geoinformation, 12(2), 145-150.
• 8. Hu, Z. and Wu, W., (2012). A Satellite Data Portal Developed for Crowdsourcing Data Analysis and Interpretation. In E-Science (E-Science), IEEE 8th International Conference On (Pp. 1-8). IEEE,
• 9. Thenkabail, P.S., (Ed.)., (2015). Remotely Sensed Data Characterization, Classification, and Accuracies. CRC Press.
• 10. Howe, J., (2008). CROWDSOURCING Kalabalıkların Gücü, Bir İşin Geleceğine Nasıl Şekil Verebilir?. Koçsistem Yayınları.
• 11. Mcconchie, A., (2015). Hacker Cartography: Crowdsourced Geography, Openstreetmap, and The Hacker Political Imaginary. ACME Int. E-J. Crit. Geogr., 14, 874–898.
• 12. Goodchild, M.F., (2007). Citizens as Sensors: The World of Volunteered Geography. Geojournal, 69, 211–221.
• 13. Turner, A., (2006). Introduction to Neogeography; O’Reilly: Sebastopol, CA, USA.
• 14. Howe, J., (2006). The Rise of Crowdsourcing. Wired Mag., 14, 1–4.
• 15. Bonney, R., Cooper, C.B., Dickinson, J., Kelling, S., Phillips, T., Rosenberg, K.V., Shirk, J., (2009). Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy. Bioscience, 59, 977–984.
• 16. Krumm, J., Davies, N., and Narayanaswami, C., (2008). User-Generated Content. IEEE Pervasive Comput., 7, 10–11.
• 17. See, Linda, et al., (2016). Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information. ISPRS International Journal of Geo-Information 5.5, 55.
• 18. Behrend, T.S., et al. (2013). The Viability of Crowdsourcing for Survey Research. Behavior Research Methods 43.3,800.
• 19. Wald, D.J., Quitoriano, V., Worden, B., Hopper, M., Dewey, J.W., (2011). USGS “Did You Feel It?” Internet-Based Macroseismic Intensity Maps. Ann. Geophys., 54(6), 688–707. Doi: 10.4401/Ag-5354.
• 20. Baklanov, A. and Fritz, S., (2017). Khachay, M., Nurmukhametov, O., Salk, C., See, L., & Shchepashchenko, D. Vote Aggregation Techniques in The Geo-Wiki Crowdsourcing Game: A Case Study. In International Conference on Analysis of Images, Social Networks and Texts (pp:41-50). Springer, Cham.
• 21. Fritz, S., Mccallum, I., Schill, C., Perger, C., Grillmayer, R., Achard, F., and Obersteiner, M., (2009). Geo-Wiki. Org: The Use of Crowdsourcing to Improve Global Land Cover. Remote Sensing, 1(3), 345-354.
• 22. http://www.citizensensor-cost.eu, Erişim tarihi:08/11/2017.
• 23. Digitalglobe: http://www.digitalglobe.com, Erişim tarihi:08/11/2017.
• 24. Tomnod: http://www.tomnod.com, Erişim tarihi:08/11/2017.
• 25. Kahraman, Fatih, et al., (2015). Enterprise Crowdsourcing Platform for Geospatial Image Analysis. Signal Processing and Communications Applications Conference (SIU), IEEE.
• 26. Kitchenham, B.A. and Charters, S., (2007). Guidelines for Performing Systematic Literature Reviews in Software Engineering. Tech. Rep. EBSE 2007-001, Keele University and Durham University Joint Report.
• 27. Biolchini, J., Mian, P.G., Natali, A.C.C., and Travassos, G.H., (2005). Systematic Review in Software Engineering. Technical Report, Systemas Engineering and Computer Science Department/UFRJ.
• 28. Horita, F.E.A., et al., (2007). The Use of Volunteered Geographic Information (VGI) and Crowdsourcing in Disaster Management: A Systematic Literature Review.
• 29. Dyba, T., Dingsøyr, T., and Hanssen, G.K., (2007). Applying Systematic Reviews to Diverse Study Types: An Experience Report. In First International Symposium on Empirical Software Engineering and Measurement.
• 30. See, L., Perger, C., Duerauer, M., Fritz, S., Bechtel, B., and Ching, J.S., (2015). I. Developing a Community-Based Worldwide Urban Morphology and Materials Database (WUDAPT) Using Remote Sensing and Crowdsourcing for Improved Urban Climate Modelling. In Urban Remote Sensing Event (JURSE), 2015 Joint (pp:1-4). IEEE.
• 31. Huynh, A., Eguchi, M., Lin, A.Y.M., and Eguchi, R., (2014). Limitations of Crowdsourcing Using the EMS-98 Scale in Remote Disaster Sensing. in Aerospace Conference, IEEE (pp:1-7).
• 32. Liu, J., Hyyppä, J., Yu, X., Jaakkola, A., Kukko, A., and Kaartinen, H.H., (2017). A Novel GNSS Technique for Predicting Boreal Forest Attributes at Low Cost. IEEE Transactions on Geoscience and Remote Sensing, 55(9), 4855-4867.
• 33. Berriel, R.F., Lopes, A.T., De Souza, A.F., and Oliveira-Santos, T., (2017). Deep Learning-Based Large-Scale Automatic Satellite Crosswalk Classification. IEEE Geoscience and Remote Sensing Letters, 14(9), 1513-1517.
• 34. Chi, M., Sun, Z., Qin, Y., Shen, J., and Benediktsson, J.A., (2017). A Novel Methodology to Label Urban Remote Sensing Images Based on Location-Based Social Media Photos. Proceedings of the IEEE, 105(10), 1926-1936.
• 35. De Oca, A.M.M., Bahmanyar, R., Nistor, N., and Datcu, M., (2017). Earth Observation Image Semantic Bias: A Collaborative User Annotation Approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
• 36. Semen, P., Boris, S., and Viacheslav, Z., (2015). River Floods Short-Term Forecasting System Based on Integrated Use of Space-Ground Data. In Geoscience and Remote Sensing Symposium (IGARSS), IEEE International (pp:1377-1380).
• 37. Huynh, A., Ponto, K., Lin, A. Y. M., and Kuester, F., (2013). Visual Analytics of Inherently Noisy Crowdsourced Data on Ultra High Resolution Displays. In Aerospace Conference, IEEE (Pp. 1-8).
• 38. Hu, Z. and Wu, W., (2012). A Satellite Data Portal Developed for Crowdsourcing Data Analysis and Interpretation. In E-Science (E-Science), IEEE 8th International Conference On (pp:1-8).
• 39. Dell’Acqua, F., and De Vecchi, D., (2017). Potentials of Active and Passive Geospatial Crowdsourcing in Complementing Sentinel Data and Supporting Copernicus Service Portfolio. Proceedings of The IEEE, 105(10), 1913-1925.
• 40. Kaiser, P., Wegner, J.D., Lucchi, A., Jaggi, M., Hofmann, T., and Schindler, K., (2017). Learning Aerial Image Segmentation from Online Maps. IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6054-6068.
• 41. Gueguen, L., Koenig, J., Reeder, C., Barksdale, T., Saints, J., Stamatiou, K., and Johnston, C., (2017). Mapping Human Settlements and Population at Country Scale from VHR Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(2), 524-538.
• 42. Tang, L., Yang, X., Dong, Z., and Li, Q., (2016). CLRIC: Collecting Lane-Based Road Information Via Crowdsourcing. IEEE Transactions on Intelligent Transportation Systems, 17(9), 2552-2562.
• 43. Galeazzo, D.A., De Vecchi, D., Dell'Acqua, F., and Demattei, P.A., (2005). Small Step towards the Citizen Sensor: A Multi-Purpose Framework for Mobile Apps. In Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International (pp:1348-1350).
• 44. Foody, G.M., (2015). Citizen Science In Support of Remote Sensing Research. In Geoscience and Remote Sensing Symposium (IGARSS), IEEE International (pp:5387-5390).
• 45. Pyhälahti, T., Toivanen, T., Kallio, K. Y., Järvinen, M., Molinier, M., Koponen, S., and Lindholm, M., (2015). Advances in Combining Optical Citizen Observations on Water Quality with Satellite Observations as Part of an Environmental Monitoring System. In Geoscience and Remote Sensing Symposium (IGARSS), IEEE International (pp:5395-5398).
• 46. Rossi, C., Stemberger, W., Bielski, C., Zeug, G., Costa, N., Poletto, D., and Dominici, F., (2015). Coupling Crowdsourcing, Earth Observations, and E-GNSS in a Novel Flood Emergency Service in the Cloud. In Geoscience and Remote Sensing Symposium (IGARSS), IEEE International (pp:2703-2706).
• 47. Kahraman, F., Huroglu, C., Imamoglu, M., Ozcan, B.Y., Kalkan, M. I., Hocaoglu, M.A., and Kurt, B., (2015). Enterprise Crowdsourcing Platform for Geospatial Image Analysis. In Signal Processing and Communications Applications Conference (SIU), 2015 23th (pp:1813-1816).
• 48. Oba, H., Hirota, M., Chbeir, R., Ishikawa, H., and Yokoyama, S., (2014). Towards Better Land Cover Classification Using Geo-Tagged Photographs. In Multimedia (ISM), 2014 IEEE International Symposium on (pp:320-327).
• 49. Fritz, S., See, L., Perger, C., Mccallum, I., Schill, C., Schepaschenko, D., and Lesiv., M., (2017). A Global Dataset of Crowdsourced Land Cover and Land Use Reference Data. Scientific Data, 4, 170075.
• 50. Ali, A.L., Falomir, Z., Schmid, F., and Freksa, C. (2017). Rule-Guided Human Classification of Volunteered Geographic Information. ISPRS Journal of Photogrammetry and Remote Sensing, 127, 3-15.
• 51. Laso Bayas, J.C., See, L., Fritz, S., Sturn, T., Perger, C., Dürauer, M., and Mccallum, I., (2016). Crowdsourcing In-Situ Data on Land Cover and Land Use Using Gamification and Mobile Technology. Remote Sensing, 8(11), 905.
• 52. Kosmala, M., Crall, A., Cheng, R., Hufkens, K., Henderson, S., and Richardson, A.D., (2016). Season Spotter: Using Citizen Science to Validate and Scale Plant Phenology from Near-Surface Remote Sensing. Remote Sensing, 8(9), 726.
• 53. Salk, C., Sturn, T., See, L., and Fritz., (2016). S. Local Knowledge and Professional Background Have a Minimal Impact on Volunteer Citizen Science Performance in A Land-Cover Classification Task. Remote Sensing, 8(9), 774.
• 54. Liu, J., Hyyppä, J., Yu, X., Jaakkola, A., Liang, X., Kaartinen, H., and Hyyppä., H., (2016). Can Global Navigation Satellite System Signals Reveal the Ecological Attributes of Forests? International Journal of Applied Earth Observation and Geoinformation, 50, 74-79.
• 55. Ofli, F., Meier, P., Imran, M., Castillo, C., Tuia, D., Rey, N., and Joost, S., (2016). Combining Human Computing and Machine Learning to Make Sense of Big (Aerial) Data for Disaster Response. Big Data, 4(1), 47-59.
• 56. Foulser-Piggott, R., Spence, R., Eguchi, R., and King, A., (2016). Using Remote Sensing for Building Damage Assessment: GEOCAN Study and Validation for 2011 Christchurch Earthquake. Earthquake Spectra, 32(1), 611-631.
• 57. Li, D., Tian, K., Wang, F., and Wang, F., (2016). Home Damage Estimation After Disasters Using Crowdsourcing Ideas and Convolutional Neural Networks, ICMA.
• 58. Schepaschenko, D.G., Shvidenko, A.Z., Lesiv, M.Y., Ontikov, P.V., Shchepashchenko, M.V., and Kraxner, F., (2015). Estimation of Forest Area and Its Dynamics in Russia Based on Synthesis of Remote Sensing Products. Contemporary Problems of Ecology, 8(7), 811-817.
• 59. Garaba, S.P., Friedrichs, A., Voß, D., and Zielinski, O., (2015). Classifying Natural Waters with The Forel-Ule Colour Index System: Results, Applications, Correlations and Crowdsourcing. International Journal of Environmental Research and Public Health, 12(12), 16096-16109.
• 60. Fekete, A., Tzavella, K., Armas, I., Binner, J., Garschagen, M., Giupponi, C., and Serre., D., (2015). Critical Data Source; Tool or Even Infrastructure? Challenges of Geographic Information Systems and Remote Sensing for Disaster Risk Governance. ISPRS International Journal of Geo-Information, 4(4), 1848-1869.
• 61. Schepaschenko, D., See, L., Lesiv, M., Mccallum, I., Fritz, S., Salk, C., and Kovalevskyi, S., (2015). Development of a Global Hybrid Forest Mask Through the Synergy of Remote Sensing, Crowdsourcing and FAO Statistics. Remote Sensing of Environment, 162, 208-220.
• 62. See, L., Schepaschenko, D., Lesiv, M., Mccallum, I., Fritz, S., Comber, A., and Siraj, M.A., (2015). Building A Hybrid Land Cover Map with Crowdsourcing and Geographically Weighted Regression. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 48-56.
• 63. Wan, Z., Hong, Y., Khan, S., Gourley, J., Flamig, Z., Kirschbaum, D., and Tang, G., (2014). A Cloud-Based Global Flood Disaster Community Cyber-Infrastructure: Development and Demonstration. Environmental Modelling & Software, 58, 86-94.
• 64. Romanovs, A., Sokolov, B.V., Lektauers, A., Potryasaev, S., and Shkodyrev, V.,(2014). Crowdsourcing Interactive Technology for Natural-Technical Objects Integrated Monitoring. In International Conference on Speech and Computer (pp:176-183). Springer, Cham.
• 65. Bello, O.M. and Aina, Y.A., (2014). Satellite Remote Sensing as a Tool in Disaster Management and Sustainable Development: Towards a Synergistic Approach. Procedia-Social and Behavioral Sciences, 120, 365-373.
• 66. See, L., Comber, A., Salk, C., Fritz, S., Van Der Velde, M., Perger, C., and Obersteiner, M., (2013). Comparing The Quality of Crowdsourced Data Contributed by Expert and Non-Experts. Plos One, 8(7), E69958.
• 67. Kerle, N. and Hoffman, R.R., (2013). Collaborative Damage Mapping for Emergency Response: The Role of Cognitive Systems Engineering. Natural Hazards and Earth System Sciences, 13(1), 97-113.
• 68. Frye, S., Percivall, G., Moe, K., Mandl, D., Handy, M., and Evans, J., (2013). Towards A Sensor Web Architecture for Disaster Management: Insights from The Namibia Flood Pilot. In Geoscience and Remote Sensing Symposium (IGARSS), International (pp:807-810).
• 69. Corbane, C., Lemoine, G., and Kauffmann, M., (2012). Relationship Between the Spatial Distribution of SMS Messages Reporting Needs and Building Damage in 2010 Haiti Disaster. Natural Hazards and Earth System Sciences, 12(2), 255-265.
• 70. Ghosh, S., Huyck, C.K., Greene, M., Gill, S.P., Bevington, J., Svekla, W., and Eguchi, R.T., (2011). Crowdsourcing for Rapid Damage Assessment: The Global Earth Observation Catastrophe Assessment Network (GEO-CAN). Earthquake Spectra, 27(S1), S179-S198.
• 71. Clark, M.L. and Aide, T.M., (2011). Virtual Interpretation of Earth Web-Interface Tool (VIEW-IT) for Collecting Land-Use/Land-Cover Reference Data. Remote Sensing, 3(3), 601-620.
• 72. Pistorius, T. and Poona, N., (2014). Accuracy Assessment of Game-Based Crowdsourced Land-Use/Land Cover Image Classification. In Geoscience and Remote Sensing Symposium (IGARSS), IEEE International (pp:4780-4783).
• 73. Barrington, L., Ghosh, S., Greene, M., Har-Noy, S., Berger, J., Gill, S., Yu-min, A., and Huyck, C., (2012). Crowdsourcing Earthquake Damage Assessment Using Remote Sensing Imagery. Annals of Geophysics, 54(6).
• 74. Heipke, C., (2010). Crowdsourcing Geospatial Data. ISPRS Journal of Photogrammetry and Remote Sensing, 65(6), 550-557.
• 75. Fritz, S., Mccallum, I., Schill, C., Perger, C., Grillmayer, R., Achard, F., and Obersteiner, M., (2009). Geo-Wiki. Org: The Use of Crowdsourcing to Improve Global Land Cover. Remote Sensing, 1(3), 345-354.
• 76. Qi, W., Su, G., Sun, L., Yang, F., and Wu, Y., (2017). “Internet+” Approach to Mapping Exposure and Seismic Vulnerability of Buildings in A Context of Rapid Socioeconomic Growth: A Case Study in Tangshan, China. Natural Hazards, 86(1), 107-139.
• 77. Boulos, M.N.K., Resch, B., Crowley, D.N., Breslin, J.G., Sohn, G., Burtner, R., and Chuang, K.Y.S., (2011). Crowdsourcing, Citizen Sensing and Sensor Web Technologies for Public and Environmental Health Surveillance and Crisis Management: Trends, OGC Standards and Application Examples. International Journal of Health Geographics, 10(1), 67.
• 78. Daume, S., Albert, M., and Von Gadow, K., (2014). Assessing Citizen Science Opportunities in Forest Monitoring Using Probabilistic Topic Modelling. Forest Ecosystems, 1(1), 11.
• 79. Bogaert, P. and Gengler, S., (2017). Bayesian Maximum Entropy and Data Fusion for Processing Qualitative Data: Theory and Application for Crowdsourced Cropland Occurrences in Ethiopia. Stochastic Environmental Research and Risk Assessment, 1-17.
• 80. Ogie, R.I., Forehead, H., Clarke, R.J., and Perez, P., (2017). Participation Patterns and Reliability of Human Sensing in Crowd-Sourced Disaster Management. Information Systems Frontiers, 1-16.
• 81. Zhao, J., Wang, X., Lin, Q., and Li, J., (2015). Exploration of Applying Crowdsourcing in Geosciences: A Case Study of Qinghai-Tibetan Lake Extraction. In International Conference on Collaborative Computing: Networking, Applications and Worksharing (pp:329-334). Springer, Cham.
• 82. Maisonneuve, N. and Chopard, B., (2012). Crowdsourcing Satellite Imagery Analysis: Study of Parallel and Iterative Models. In International Conference on Geographic Information Science (Pp. 116-131). Springer, Berlin, Heidelberg.
• 83. Baklanov, A., Fritz, S., Khachay, M., Nurmukhametov, O., Salk, C., See, L., and Shchepashchenko, D., (2016). Vote Aggregation Techniques in the Geo-Wiki Crowdsourcing Game: A Case Study. In International Conference On Analysis of Images, Social Networks and Texts (pp:41-50). Springer, Cham.
• 84. Comber, A., Brunsdon, C., See, L., Fritz, S., and Mccallum, I., (2013). Comparing Expert and Non-Expert Conceptualisations of the Land: An Analysis of Crowdsourced Land Cover Data. In International Conference on Spatial Information Theory (pp.. 243-260). Springer, Cham.
• 85. Baklanov, A., Fritz, S., Khachay, M., Nurmukhametov, O., and See, L., (2016). The Cropland Capture Game: Good Annotators Versus Vote Aggregation Methods. In Advanced Computational Methods for Knowledge Engineering (pp:167-180). Springer International Publishing.
• 86. Estima, J. and Painho, M., (2013). Flickr Geotagged and Publicly Available Photos: Preliminary Study of Its Adequacy for Helping Quality Control of Corine Land Cover. In International Conference on Computational Science and Its Applications (pp:205-220). Springer, Berlin, Heidelberg.
• 87. Fritz, S., See, L., Mccallum, I., Schill, C., Perger, C., and Obersteiner, M., (2011). Building A Crowd-Sourcing Tool for The Validation of Urban Extent and Gridded Population. In International Conference On Computational Science and Its Applications (pp:39-50). Springer, Berlin, Heidelberg.
• 88. Mancini, F., Capra, A., Castagnetti, C., Ceppi, C., Bertacchini, E., and Rivola, R., (2015). Contribution of Geomatics Engineering and VGI within the Landslide Risk Assessment Procedures. In International Conference On Computational Science and Its Applications (pp:635-647). Springer, Cham.
• 89. Núñez-Redó, M., Díaz, L., Gil, J., González, D., and Huerta, J., (2011). Discovery and Integration of Web 2.0 Content into Geospatial Information Infrastructures: A Use Case in Wild Fire Monitoring. In International Conference on Availability, Reliability, and Security (pp:50-68). Springer, Berlin, Heidelberg.
• 90. Ali, A.L. and Schmid, F., (2014). Data Quality Assurance for Volunteered Geographic Information. In International Conference on Geographic Information Science (Pp. 126-141). Springer, Cham.
• 91. Ding, Y., Zheng, J., Tan, H., Luo, W., and Ni, L.M., (2014). Inferring Road Type in Crowdsourced Map Services. In International Conference on Database Systems for Advanced Applications (pp:392-406). Springer, Cham.