Methods for automatic transformation of irrigation network into graphical representation based on Google Earth KML files.
Keywords:
KML, Google Earth, irrigation network, graph model, topology reconstruction, proximity-based connectivity, GDAL/OGR, NetworkX, information systemAbstract
This paper proposes a method for automatically transforming an irrigation network from KML/KMZ files prepared in Google Earth into a computationally ready directed graph. The method involves reading the KML structure, normalizing coordinates to a metric system, connecting elements based on proximity, and attaching nodes to canal lines using a point-to-line projection. Polylines are segmented node-by-node, and intersection, discontinuity, and duplication errors are resolved using semantic rules. Attributes are extracted from the name/description/ExtendedData fields using a rule-based text parser. In practice, PK-s consistency and segmentation metrics were calculated using the Qizketken-Kegeyli example.
References
D. Li, M. Lu, “Integrating geometric models, site images and GIS based on Google Earth and Keyhole Markup Language,” Automation in Construction, vol. 89, pp. 317–331, May 2018, doi: 10.1016/j.autcon.2018.02.002.
H. Mohammadi, M. R. Delavar, M. A. Sharifi, M. D. Pirooz, “Spatiotemporal visualization of tsunami waves using kml on Google Earth,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-2/W7, pp. 1291–1299, Sept. 2017, doi: 10.5194/isprs-archives-XLII-2-W7-1291-2017.
Y. X. Lin, S. T. Wang, “Expression and analysis of seismic disaster information based on KML,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-3/W10, pp. 1001–1004, Feb. 2020, doi: 10.5194/isprs-archives-XLII-3-W10-1001-2020.
P. Chen, “Visualization of real-time monitoring datagraphic of urban environmental quality,” EURASIP Journal on Image and Video Processing, vol. 2019, no. 1, pp. 42, Dec. 2019, doi: 10.1186/s13640-019-0443-6.
T. Honjo, K. Umeki, D. Wang, P. Yang, H. Hsieh, “Landscape Simulation and Visualization on Google Earth,” International Journal of Virtual Reality, vol. 10, no. 2, pp. 11–15, Jan. 2011, doi: 10.20870/IJVR.2011.10.2.2806.
D. Potere, “Horizontal Positional Accuracy of Google Earth’s High-Resolution Imagery Archive,” Sensors, vol. 8, no. 12, pp. 7973–7981, Dec. 2008, doi: 10.3390/s8127973.
X. W. Wang, F. Wang, “The precision of Google Earth map analysis with the coordinates of IGS stations,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-3/W10, pp. 1053–1056, Feb. 2020, doi: 10.5194/isprs-archives-XLII-3-W10-1053-2020.
M. N. K. Boulos, “Web GIS in practice III: creating a simple interactive map of England’s Strategic Health Authorities using Google Maps API, Google Earth KML, and MSN Virtual Earth Map Control,” International Journal of Health Geographics, vol. 4, no. 1, pp. 22, 1476-072X-4–22, Dec. 2005, doi: 10.1186/1476-072X-4-22.
I. Janssen, A. Rosu, “Measuring sidewalk distances using Google Earth,” BMC Medical Research Methodology, vol. 12, no. 1, pp. 39, Dec. 2012, doi: 10.1186/1471-2288-12-39.
A. Martínez-Graña, J. González-Delgado, S. Pallarés, J. Goy, J. Llovera, “3D Virtual Itinerary for Education Using Google Earth as a Tool for the Recovery of the Geological Heritage of Natural Areas: Application in the ‘Las Batuecas Valley’ Nature Park (Salamanca, Spain),” Sustainability, vol. 6, no. 12, pp. 8567–8591, Nov. 2014, doi: 10.3390/su6128567.
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