The development of current massive technology plays an important role in the field of disaster risk reduction and disaster management. In particular, the issue of disruptive technology in which the emerging of new technologies comes with big disruption in many fields. For instance, the development on using satellite imagery big data for disaster management to reduce the disaster risk.The unique characteristics of big data i.e. volume, velocity, and variety stimulate this study to obtain more information on the big data processing and analyze the opportunities and challenges of satellite imagery in the case of disaster risk reduction through literature reviews. The huge amount of data and the ability for real-time analysis provide capabilities onÂ time series analysis and possitive impact on disaster detection, monitoring, and prediction. However, such technology creates disruption due to the change on using and anlysing the data based on cloud environment. Issues on the technology uses, data security might arise. Nonetheless, the use of satellite imagery big data will remain vastly developed and is able to promote the development of satellite imagery technology.
Breiman, Leo. 2001. Random forests. Machine learning 45, no. 1: 5-32.
Centre for Research on the Epidemiology of Disasters (CRED). 2019. Natural Disaters 2017: Executive Summary. Institute Health and Society, UniversitÃ© Catholique de Louvain. Brussels. 8p.
Christensen, C.M., M.E. Raynor and R. McDonald. 2015. What Is Disruptive Innovation? Harvard Business Review, December. [terhubung berkala]. https://hbr.org/2015/12/what-is-disruptive-innovation [31 Oktober 2019].
Goodchild, M. F. 2013. The quality of big (geo) data. Dialogues in Human Geography, 3(3), 280-284.
Gorelick, Noel, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, and Rebecca Moore. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202 (2017): 18-27.
Guo, Huadong, Zhen Liu, Hao Jiang, Changlin Wang, Jie Liu, and Dong Liang. 2017. Big Earth Data: A new challenge and opportunity for Digital Earthâ€™s development. International Journal of Digital Earth 10, no. 1: 1-12.
ITU. 2019. Disruptive technologies and their use in disaster risk reduction and management 2019. Telecommunication Development Bureau, International Telecommunication Union. Switzerland. 60p.
Kempler, S., & Mathews, T. 2017. Earth science data analytics: Definitions, techniques and skills. Data Science Journal, 16.
Manning, C. D. 2015. Computational linguistics and deep learning. Computational Linguistics, 41 (4), 701â€“707.
Melati, D. N. 2019. Multi Temporal Remotely Sensed Image Modelling For Deforestation Monitoring. Jurnal Alami: Jurnal Teknologi Reduksi Risiko Bencana, 3(1), 43-51.
Parks, Sean, Lisa Holsinger, Morgan Voss, Rachel Loehman, and Nathaniel Robinson. 2018. Mean composite fire severity metrics computed with google earth engine offer improved accuracy and expanded mapping potential. Remote Sensing 10, no. 6: 879.
Sellars, Scott, Phu Nguyen, Wei Chu, Xiaogang Gao, Kuo-lin Hsu, and Soroosh Sorooshian. 2013. Computational Earth science: Big data transformed into insight. Eos, Transactions American Geophysical Union 94, no. 32: 277-278.
Sudmanns, Martin, Dirk Tiede, Stefan Lang, Helena Bergstedt, Georg Trost, Hannah Augustin, Andrea Baraldi, and Thomas Blaschke. 2019. Big Earth data: disruptive changes in Earth observation data management and analysis?. International Journal of Digital Earth: 1-19.
Sun, Alexander Y., and Bridget R. Scanlon. 2019. How can big data and machine learning benefit environment and water management: A survey of methods, applications, and future directions. Environmental Research Letters.
Uddin, Kabir, Mir A. Matin, and Franz J. Meyer. 2019. Operational flood mapping using multi-temporal sentinel-1 SAR images: a case study from Bangladesh. Remote Sensing 11, no. 13: 1581.
United Nation. 2015. Data-Pop Alliance Synthesis Report Big Data for Climate Change and Disaster Resilience: Realizing the Benefits for Developing Countries. [terhubung berkala] http://datapopalliance.org/wp-content/
uploads/2015/11/Big-Data-for-Resilience-2015-Report.pdf [01 November 2019].
Wang, Z., Xiao, D., Fang, F., Govindan, R., Pain, C. C., & Guo, Y. 2018. Model identification of reduced order fluid dynamics systems using deep learning. International Journal for Numerical Methods in Fluids, 86(4), 255â€“268.
Yang, Chaowei, Manzhu Yu, Yun Li, Fei Hu, Yongyao Jiang, Qian Liu, Dexuan Sha, Mengchao Xu, and Juan Gu. 2019. Big Earth data analytics: a survey. Big Earth Data: 1-25.
Yu, Bo, Fang Chen, and Shakir Muhammad. 2018. Analysis of satellite-derived landslide at Central Nepal from 2011 to 2016. Environmental earth sciences 77, no. 9: 331.
Yu, Manzhu, Chaowei Yang, and Yun Li. 2018. Big data in natural disaster management: a review. Geosciences 8, no. 5: 165.