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Mekansal zekanın getirdiği paradigma değişimi

Year 2019, Volume: 6 Issue: 2, 128 - 142, 01.11.2019
https://doi.org/10.9733/JGG.2019R0006.T

Abstract

Niceliksel ve niteliksel
olarak artan, veri türü olarak çeşitlenen görüntü kaynaklarından anlamlı ve
faydalı bilginin yapay öğrenme temelli olarak üretilmesi giderek yaygınlık
kazanmaktadır. Mekansal bilgi sistemi uygulamalarında bilinçli karar verebilmek
için nesnelere, olgulara ve içinde bulundukları ortama ilişkin bağlamın,
ilişkilerin, örüntülerin ve eğilimlerin yapay öğrenme teknikleri ile
belirlenmesi mekansal bilgi sistemi projelerinin başarımını ve verimliliğini
arttırmaktadır. Bu tür yönelimler mekansal bilişim endüstrisinde mekansal zeka
temelli sistemlerin kullanımını yaygınlaştırmaktadır. Gözlem ve ölçme
sistemlerinden bulut ortamında çalışan bilgi sistemlerine kadar geniş bir
yelpazede mekansal zeka özellikli çözümler geliştirilebilmektedir. Mekansal
zeka özellikli sistemlerin etkin ve verimli biçimde kullanılabilmesi için
mekansal zeka kavramının ne olduğu, hangi alanlarda kullanılabileceği ve daha
yüksek bir katma değer sağlayabilmesi için nasıl bir yol haritasının
oluşturulması gerektiği bu çalışma kapsamında irdelenmeye çalışılmıştır.
 

References

  • Blum, A. L., & Langley, P. (1997). Selection of relevant features and examples in machine learning. Artificial intelligence, 97(1-2), 245-271.
  • Christensen, C. M. (1997). The innovator's dilemma: when new technologies cause great firms to fail. Harvard Business Review Press.
  • Collobert, R., & Weston, J. (2008). A unified architecture for natural language processing: Deep neural networks with multitask learning. Proceedings of the 25th international conference on Machine learning. 160-167.
  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems. 2672-2680.
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  • Güney, C. (2019), Yerel Yönetimlerde Paradigma Değişimi, Sosyal Demokrat Dergi, 97-98.
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  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778.
  • Jiao, J. (2018). Machine Learning Assisted High-Definition Map Creation. 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), 1, 367-373.
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  • Mason, L., Baxter, J., Bartlett, P. L., & Frean, M. R. (1999). Boosting algorithms as gradient descent. Advances in neural information processing systems. 512-518.
  • Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.
  • Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1-9.
  • VoPham, T., Hart, J. E., Laden, F., & Chiang, Y. Y. (2018). Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology. Environmental Health, 17(1), 40.
  • Yu, D., & Hang, C. C. (2010). A reflective review of disruptive innovation theory. International journal of management reviews, 12(4), 435-452.
  • Yu, R., Shi, Z., Huang, C., Li, T., & Ma, Q. (2017). Deep reinforcement learning based optimal trajectory tracking control of autonomous underwater vehicle. In 2017 36th Chinese Control Conference (CCC). 4958-4965.
  • URL-1: http://benchmark.ini.rub.de/, (Erişim Tarihi: 17 Kasım 2018).
  • URL-2: https://xgboost.ai/about, (Erişim Tarihi: 8 Aralık 2018).
  • URL-3: https://www.opengeospatial.org/standards/wps, (Erişim Tarihi: 7 Temmuz 2018).
  • URL-4: https://www.whitehouse.gov/articles/accelerating-americas-leadership-in-artificial-intelligence/?utm_source=twitter&utm_mediu m=social&utm_campaign=wh, (Erişim Tarihi: 16 Mart 2019).
  • URL-5: https://udi.ornl.gov/geoai, (Erişim Tarihi: 4 Eylül 2018).
  • URL-6: https://aag.secure-abstracts.com/AAG%20Annual%20Meeting%202019/sessions-gallery/23171, (Erişim Tarihi: 23 Mart 2019).
  • URL-7: http://www.opengeospatial.org/pressroom/pressreleases?from=hogzpqvtpaj&page=98, (Erişim Tarihi: 5 Ocak 2019).
  • URL-8: https://www.esri.com/en-us/landing-page/lp/product/2018/geo-ai, (Erişim Tarihi: 2 Şubat 2019).
  • URL-9: https://www.meetup.com/Mekansal-Zeka/, (Erişim Tarihi: 30 Mart 2019).

Paradigm shift by spatial intelligence

Year 2019, Volume: 6 Issue: 2, 128 - 142, 01.11.2019
https://doi.org/10.9733/JGG.2019R0006.T

Abstract

It is becoming prevalent
to produce meaningful and useful information based on artificial learning from
quantitative and qualitatively increasing data sources. In order to make
informed decisions in spatial information system applications, determining the
context, relations, patterns and trends related to objects, facts and
environment by artificial learning techniques increases the performance and
efficiency of spatial information system projects. Such trends will accelerate
the spread of GeoAI-based systems in the spatial informatics industry. A wide
range of spatial intelligence solutions can be developed from observation and
measurement systems to cloud computing information systems. In the scope of the
study, it has been examined that first what the concept of spatial intelligence
means, then in which areas geospatial intelligence can be utilized, and
finally, how to create a road map to provide a higher added value from spatial
intelligence.

References

  • Blum, A. L., & Langley, P. (1997). Selection of relevant features and examples in machine learning. Artificial intelligence, 97(1-2), 245-271.
  • Christensen, C. M. (1997). The innovator's dilemma: when new technologies cause great firms to fail. Harvard Business Review Press.
  • Collobert, R., & Weston, J. (2008). A unified architecture for natural language processing: Deep neural networks with multitask learning. Proceedings of the 25th international conference on Machine learning. 160-167.
  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems. 2672-2680.
  • Güney, C. (2016). Yeni Nesil Coğrafi Bilgi Sistemlerinde Yapay Zeka, XVIII. Akademik Bilişim Konferansı (AB 2016). Aydın.
  • Güney, C., & Çelik, R. N. (2017). Geomatik Mühendisliğinin Rekabet Gücü ve Endüstri 4.0, 16. Türkiye Harita Bilimsel ve Teknik Kurultayı, Ankara.
  • Güney, C. (2019), Yerel Yönetimlerde Paradigma Değişimi, Sosyal Demokrat Dergi, 97-98.
  • Hassan, N., Gillani, S., Ahmed, E., Yaqoob, I., & Imran, M. (2018). The role of edge computing in internet of things. IEEE Communications Magazine, 56(11), 110-115.
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778.
  • Jiao, J. (2018). Machine Learning Assisted High-Definition Map Creation. 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), 1, 367-373.
  • Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of artificial intelligence research, 4, 237-285.
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 1097-1105.
  • LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436.
  • Lin, Y., Chiang, Y. Y., Pan, F., Stripelis, D., Ambite, J. L., Eckel, S. P., & Habre, R. (2017). Mining public datasets for modeling intra-city PM2. 5 concentrations at a fine spatial resolution. Proceedings of the 25th ACM SIGSPATIAL international conference on advances in geographic information systems, 25.
  • Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). Ssd: Single shot multibox detector. In European conference on computer vision. 21-37. Springer, Cham.
  • Mason, L., Baxter, J., Bartlett, P. L., & Frean, M. R. (1999). Boosting algorithms as gradient descent. Advances in neural information processing systems. 512-518.
  • Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.
  • Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1-9.
  • VoPham, T., Hart, J. E., Laden, F., & Chiang, Y. Y. (2018). Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology. Environmental Health, 17(1), 40.
  • Yu, D., & Hang, C. C. (2010). A reflective review of disruptive innovation theory. International journal of management reviews, 12(4), 435-452.
  • Yu, R., Shi, Z., Huang, C., Li, T., & Ma, Q. (2017). Deep reinforcement learning based optimal trajectory tracking control of autonomous underwater vehicle. In 2017 36th Chinese Control Conference (CCC). 4958-4965.
  • URL-1: http://benchmark.ini.rub.de/, (Erişim Tarihi: 17 Kasım 2018).
  • URL-2: https://xgboost.ai/about, (Erişim Tarihi: 8 Aralık 2018).
  • URL-3: https://www.opengeospatial.org/standards/wps, (Erişim Tarihi: 7 Temmuz 2018).
  • URL-4: https://www.whitehouse.gov/articles/accelerating-americas-leadership-in-artificial-intelligence/?utm_source=twitter&utm_mediu m=social&utm_campaign=wh, (Erişim Tarihi: 16 Mart 2019).
  • URL-5: https://udi.ornl.gov/geoai, (Erişim Tarihi: 4 Eylül 2018).
  • URL-6: https://aag.secure-abstracts.com/AAG%20Annual%20Meeting%202019/sessions-gallery/23171, (Erişim Tarihi: 23 Mart 2019).
  • URL-7: http://www.opengeospatial.org/pressroom/pressreleases?from=hogzpqvtpaj&page=98, (Erişim Tarihi: 5 Ocak 2019).
  • URL-8: https://www.esri.com/en-us/landing-page/lp/product/2018/geo-ai, (Erişim Tarihi: 2 Şubat 2019).
  • URL-9: https://www.meetup.com/Mekansal-Zeka/, (Erişim Tarihi: 30 Mart 2019).
There are 32 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Caner Güney 0000-0002-1620-1347

Publication Date November 1, 2019
Submission Date April 2, 2019
Published in Issue Year 2019 Volume: 6 Issue: 2

Cite

APA Güney, C. (2019). Mekansal zekanın getirdiği paradigma değişimi. Jeodezi Ve Jeoinformasyon Dergisi, 6(2), 128-142. https://doi.org/10.9733/JGG.2019R0006.T
AMA Güney C. Mekansal zekanın getirdiği paradigma değişimi. hkmojjd. November 2019;6(2):128-142. doi:10.9733/JGG.2019R0006.T
Chicago Güney, Caner. “Mekansal zekanın getirdiği Paradigma değişimi”. Jeodezi Ve Jeoinformasyon Dergisi 6, no. 2 (November 2019): 128-42. https://doi.org/10.9733/JGG.2019R0006.T.
EndNote Güney C (November 1, 2019) Mekansal zekanın getirdiği paradigma değişimi. Jeodezi ve Jeoinformasyon Dergisi 6 2 128–142.
IEEE C. Güney, “Mekansal zekanın getirdiği paradigma değişimi”, hkmojjd, vol. 6, no. 2, pp. 128–142, 2019, doi: 10.9733/JGG.2019R0006.T.
ISNAD Güney, Caner. “Mekansal zekanın getirdiği Paradigma değişimi”. Jeodezi ve Jeoinformasyon Dergisi 6/2 (November 2019), 128-142. https://doi.org/10.9733/JGG.2019R0006.T.
JAMA Güney C. Mekansal zekanın getirdiği paradigma değişimi. hkmojjd. 2019;6:128–142.
MLA Güney, Caner. “Mekansal zekanın getirdiği Paradigma değişimi”. Jeodezi Ve Jeoinformasyon Dergisi, vol. 6, no. 2, 2019, pp. 128-42, doi:10.9733/JGG.2019R0006.T.
Vancouver Güney C. Mekansal zekanın getirdiği paradigma değişimi. hkmojjd. 2019;6(2):128-42.