Short Communication, Gigs Vol: 9 Issue: 5

Geo-Spatial Intelligence In Developing And Upcoming Future Development

Mahyar Yousefi

Department of geostatic, Afghan University, Afghanistan.

*Corresponding author: Mahyar Yousefi, Department of geostatic, Afghan University, Afghanistan. E-mail: yousefi.eng@gmail.com

Received: May 1, 2021 Accepted: May 15, 2021 Published: May 22 ,2021

Citation: Yousef M. (2021) GEO-SPATIAL INTELLIGENCE IN DEVELOPING AND UPCOMING FUTURE DEVELOPMENT. Geo infor Geo stat: An Overview 9(5).301

Abstract

Keywords: Geoinformatics, geodynamics

Introduction

The computing power, the maturity of learning algorithms, AI (AI) solution increase solving Geo-spatial informatics (GSIS) problems. Which include image matching, image target detection, change detection, image retrieval, and for generating data models of varied types. Introduction of AI and GSIS image search and discovery in big databases, automatic change detection, and detection of abnormalities, demonstrating that AI can integrate GSIS. Concept of Earth Observation Brain and Smart Geo-spatial Service (SGSS) it's expected to market the event of GSIS into broadening applications. Population-level. Artificial Intelligence (AI) is increasingly applied in various fields given the event and enhancement of computing power, the maturity of learning algorithms, and therefore the richness of application scenarios [1]. AI may be a comprehensive technical science that focuses on the event of theories, methods, techniques, and application systems for simulating and increasing human intelligence. People are trying to know the essence of intelligence with AI, to supply a replacement quite intelligent machines which will respond during a way almost like human intelligence. The term “Artificial Intelligence” was proposed in 1956 during a Dartmouth Conference [2]. That meeting is taken into account because the official birth of the new discipline “artificial intelligence”. At that point, IBM’s “dark blue” computer defeated the planet chess champion, and an ideal expression of AI technology An increasing number of AI applications target big data, computing power, internet of things, object detection, abnormality and alter detection, image interpretation, and robotic mapping. The development of AI enhances Geo-spatial informatics (GSIS) [3,4] especially when combined with advances in big data analysis [5,6] deep learning, or other AI techniques. Neural networks were reborn within the 1980s; AI and GSIS converged as AI solved problems like image matching and map generation within the GSIS field [7]. AI provides sophisticated techniques for GSIS projects and, at an equivalent time, GSIS may be a powerful technology with the vast data sets and a good scope of applications for AI. Since 2006, a replacement generation of data technologies, like the web of Things and Cloud Computing, was officially launched, realizing the great integration of industrialization and informatization [8].Cloud-computing centers can process the huge and sophisticated computational problems, and control and generate intelligent feedback. In 2009, countries round the world officially proposed to create a sensible Earth collaboratively; but, a sensible Earth can't be realized without AI in GSIS for supervision and management of decision-making support. Geospatial applications of GSIS: • Large-scale block adjustment • Image search in big databases • Automatic change detection in images • Abnormal target and event detection • Earth Observation Brain (EOB) and Smart Geo-spatial Service (SGSS) • An Internet + Spaceborne Information Real-time Service System (PNTRC) These applications are all supported AI to realize high performance and make progress toward breakthroughs that integrates various fields. GSIS, alongside AI, is crucial to know thoroughly the changes in time and space, solving difficult problems within the past, and can likely expand possibilities within the future Reference 1. Russell SJ, Norvig P (2002). Artificial Intelligence: A ModernApproach.RetrivedFrom doi:10.4028/www.scientific.net/AMM.263266.2829. 2. Hamet, P., and J. Tremblay. (2017). Artificial Intelligence in Medicine. Metabolism 69. 36–40. 3. Chen, J., J. Jiang, X. Zhou, Y. Zhai, W. Zhu, and M. Ding. (2009). Design of National Geo-spatial Information Service Platform: Overall Structure and Key Components. Geomatics World 3: 7–11 4. Li, D. (2012). The Geo-spatial Information Science Mission. Geo-spatial Information Science 15 (1),1–2. 5. Weng, M., X. Wei, R. Qu, and Z. Cai. (2009). A Path Planning Algorithm Based on Typical Case Reasoning. Geo-spatial Information Science 12 (1), 66–71. 6. Li, N., X. Huang, F. Zhang, and D. Li. (2015). Registration of Aerial Imagery and Lidar Data in Desert Areas Using Sand Ridges. The Photogrammetric Record 30 (151), 263–278. 7. VoÃ?¾enílek, V. (2009). Artificial Intelligence and GIS: Mutual Meeting and Passing. IEEE International Conference on Intelligent Networking and Collaborative Systems.