Education, Science, Technology, Innovation and Life
Open Access
Sign In

Integration of GIS and Artificial Intelligence Algorithms in Rural Landscape Protection and Planning

Download as PDF

DOI: 10.23977/jaip.2024.070201 | Downloads: 3 | Views: 76

Author(s)

Wei Songjiayi 1

Affiliation(s)

1 School of Rural Revitalization, Jiangsu Open University, Nanjing, 210036, China

Corresponding Author

Wei Songjiayi

ABSTRACT

This article explored the application of the integration technology of GIS and artificial intelligence (AI) algorithms in rural landscape protection and planning. By analyzing the problems existing in traditional methods, the article elaborated on the necessity and feasibility of combining GIS (Geographic Information System) and CNN (Convolutional Neural Network) algorithms to improve data processing capabilities and strengthen comprehensive analysis capabilities. Through case studies and empirical analysis, the article demonstrated the practical application effect and potential of this fusion technology, providing a new perspective and method for the scientific planning and effective protection of rural landscapes. In the experimental stage, four experiments were designed to evaluate the performance of GIS and CNN fusion. In the first landscape basic feature extraction experiment, the CNN algorithm achieved an accuracy of 95% in extracting features from rural landscape images; the Multi-layer Perceptron algorithm achieved 85%; the RF (Random Forest) achieved an accuracy of 80%; the Support Vector Machine (SVM) achieved 82%. Although the CNN algorithm achieved a processing time of 2 seconds, it had a high accuracy advantage. In the second landscape diversity assessment experiment, the method of integrating GIS and CNN improved species richness by 15%, landscape heterogeneity by 20%, and landscape connectivity by 25%. In landscape change detection experiments, the fusion technology of GIS and CNN has significant advantages in capturing subtle landscape changes. In the experimental data conclusion, the fusion technology of GIS and CNN had a high performance advantage in improving rural planning and management processes.

KEYWORDS

GIS Technology; CNN Algorithm; Rural Landscape Protection and Planning; Dynamic Monitoring

CITE THIS PAPER

Jiazhen Hu, Study on Eco-Management Program of Status of Illegal Trade in Wildlife. Journal of Artificial Intelligence Practice (2024) Vol. 7: 1-8. DOI: http://dx.doi.org/10.23977/jaip.2024.070201.

REFERENCES

[1] Xu Xue. Research on a GIS based digital video monitoring and operation management platform [J]. Architectural Development, 2021, 5 (4): 27-28.
[2] Yuhui Ha, Zhiwei Wen. Land development and consolidation application based on GIS geographic information technology [J]. Geological and Mineral Surveying (2630-4732), 2020, 003 (001): P.110-111.
[3] Peng Jinchen, Ye Changsheng, Gan Feng. A study on the relationship between landscape pattern changes and human interference in the urban agglomeration around Poyang Lake [J]. Journal of Environmental Engineering Technology, 2024, 14 (2): 1-11.
[4] Yan Qinghua, Liu Ping, Wen Shijun, et al. Research on Village level Industrial Layout and Planning under the Background of Rural Revitalization - Taking Anle Village in Daguan County as an Example [J]. Green Technology, 2023, 25 (5): 201-206.
[5] Shi Wei, Wu Zhitao. Design and application of a GIS based visualization system for regional soil and water conservation achievements [J]. Experimental Technology and Management, 2022, 39 (4): 7-9.
[6] Wang Honglei, Xie Yijuan, Liu Shiqi, et al. Research and Implementation of Dynamic Monitoring and Supervision Technology Platform for Building Houses on Randomly Occupied Farmland in Guizhou [J]. Geographic Information World, 2022, 29 (6): 16-20.
[7] Xie H, Zhu Z, He Y, et al. Integrated framework of rural landscape research: Based on the global perspective[J]. Landscape Ecology, 2022, 37(4): 1161-1184.
[8] Gabriele M, Brumana R, Previtali M, et al. A combined GIS and remote sensing approach for monitoring climate change-related land degradation to support landscape preservation and planning tools: The Basilicata case study[J]. Applied Geomatics, 2023, 15(3): 497-532.
[9] Renjing Z, Jiarui L I U, Xiong L I. Domestic and Foreign Research Hotspots on Rural Landscape (2000—2020)[J]. Landscape Architecture, 2022, 29(3): 12-18.
[10] Kaim D, Ziółkowska E, Grădinaru S R, et al. Assessing the suitability of urban-oriented land cover products for mapping rural settlements[J]. International Journal of Geographical Information Science, 2022, 36(12): 2412-2426.
[11] Otero Peña D, Perrotti D, Mohareb E. Advancing urban metabolism studies through GIS data: Resource flows, open space networks, and vulnerable communities in Mexico City[J]. Journal of Industrial Ecology, 2022, 26(4): 1333-1349.
[12] Zhuang Q D, Hussein M K, Ariffin N F M, et al. Landscape character: a knowledge mapping analysis using CiteSpace[J]. International Journal of Environmental Science and Technology, 2022, 19(10): 10477-10492.
[13] Mira I G, Sarabia-Bautista J. Multiscaled archaeological survey in eastern Iberia: ancient settlement dynamics, agrarian practices, and rural landscapes [J]. Journal of Field Archaeology, 2022, 47(7): 471-485.
[14] Omarzadeh D, Pourmoradian S, Feizizadeh B, et al. A GIS-based multiple ecotourism sustainability assessment of West Azerbaijan province, Iran [J]. Journal of Environmental Planning and Management, 2022, 65(3): 490-513.

Downloads: 6525
Visits: 188079

Sponsors, Associates, and Links


All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.