This paper provides a comprehensive overview of advances in surveying and mapping technology applicable to special terrains and extreme environments. Highlighting innovations such as LiDAR, Geographic Information Systems (GIS), autonomous drones, and the integration of Artificial Intelligence (AI) and Machine Learning (ML), it explores how these technologies are transforming the field of geospatial data collection. LiDAR technology has emerged as a pivotal tool, capable of generating precise 3D models and penetrating dense cover to produce accurate maps in visually obscured environments. GIS has enhanced the capacity for sophisticated data analysis, enabling more informed decision-making in multiple sectors, such as environmental management and urban planning. Additionally, the use of autonomous drones has revolutionized data collection, providing safe, efficient, and cost-effective means of accessing challenging terrains. The paper also discusses future trends, including the potential integration of virtual reality (VR) and augmented reality (AR) to create immersive data interaction experiences, and the application of cloud computing and the Internet of Things (IoT) to enhance real-time data accessibility and collaboration. These technologies promise to further advance the field of surveying and mapping, ensuring more accurate, efficient, and safer geographical data collection and analysis in complex environments.

Yi Jiang, Yang Yang. Surveying and Mapping Technology in Extreme Environments. International Journal of Engineering Technology and Construction (2024), Vol. 5, Issue 1: 18-28. https://doi.org/10.38007/IJETC.2024.050103.

[1] Chen, J., & Wang, L. (2020). "Advancements in LiDAR Technology for Environmental Monitoring and their Applications." Environmental Monitoring and Assessment, 192(3).

[2] Harris, C., & Wright, A. (2019). "GIS in Environmental Management: A Review." Journal of Environmental Management, 241, 170-183.

[3] Brown, N., & Green, T. (2018). "The Use of Drones in Ecological and Wildlife Research: A Systematic Review." Journal of Ecological Engineering, 45(5), 591-609.

[4] Li, X., & Zhao, Y. (2021). "Machine Learning and AI in Geospatial Data Processing: Current Trends and Future Directions." Geo-spatial Information Science, 24(1), 1-14.

[5] Patel, K., & Singh, R. (2019). "Integrating VR and AR into Geospatial Information Systems: Opportunities and Challenges." Computers & Geosciences, 133, 104331.

[6] Kumar, P., & Singh, A. (2018). "Internet of Things for Geospatial Data Collection: A Review." Sensors, 18(11), 3770.

[7] Anderson, K., & Gaston, K. J. (2019). "Lightweight Drones in Ecological Monitoring." Methods in Ecology and Evolution, 10(6), 885-893.

[8] Zhang, J., & Goodchild, M. F. (2020). "Cloud Computing in GIS and Geospatial Data Processing." Geographic Information Systems Quarterly, 44(2), 209-220.

[9] Morrison, J. B., & Tsai, W. H. (2017). "LiDAR Data for Landscape Archaeology in Forest Environments." Archaeological Prospection, 24(3), 205-213.

[10] Tan, P., & Shao, G. (2021). "The Application of Artificial Intelligence in the Spatial Analysis of Environmental Data." Environmental Modelling & Software, 134, 104926.

[11] Reid, J. F., O’Connor, M., & Lloyd, C. (2022). "Augmented Reality Applications in Forestry and Natural Resource Management." Forestry: An International Journal of Forest Research, 95(2), 175-186.

[12] Freeman, E. A., & Moisen, G. G. (2018). "A Comparison of the Performance of Threshold Criteria for Binary Classification in Terms of Predicted Prevalence and Kappa." Ecological Modelling, 222(8), 1359-1369.

[13] Thompson, R., & Kolka, R. (2018). "Using Internet of Things to Improve Ecological Measurements." Biogeosciences, 15(8), 2589-2602.

[14] Mather, P. M. (2019). "Integration of Multispectral and LiDAR Data in GIS." Photogrammetric Engineering & Remote Sensing, 85(5), 365-377.

[15] Gupta, S., & Kumar, D. (2020). "Advanced Surveying: Integrating LiDAR and GIS for Environmental Management." International Journal of Remote Sensing, 41(9), 3462-3487.