Creating highly informative rasters based on laser scanning and aerial photography data

Aerophototopography (Aerial Surveying And Photogrammetry)

UDC:

528.065/.067

DOI:

10.22389/0016-7126-2022-989-11-40-49

1 Valkov V.A.

2 Vinogradov K.P.

3 Valkova E.O.

4 Mustafin M.G.

Year:

2022

№:

989

Pages:

40-49

Saint-Petersburg Mining University

1,

2,

3,

4,

Abstract:

The paper is focused on scenario and terrain modeling using the results of aerial laser scanning combined with digital aerial photography. The advantages and disadvantages of these technologies are discussed in regard to the construction of large-scale topographic maps. The generalized sequence of cameral processing aerial survey data is investigated. Based on our research, we believe that developing a combined technique of presenting Lidar survey and aerial photography materials is feasible; it could simplify and speed up the operator`s (cartographer`s) work. The novelty of the research is the formation of algorithms for creating original raster images containing more information on the terrain in each section than the orthophotoplane familiar to the interested user. The criteria for the object composition of materials are worked out taking into account the specifics of the methods under consideration, variants of information combinations are formulated for broader opportunities of analyzing and interpreting the data on the territory. Various approaches to the implementation of these ideas are shown. Examples of testing developments are given.

Keywords:

aerial laser scanning, digital aerial photography, digital terrain model, laser points cloud, orthophotomap, orthoimage, raster data, topographic plan

References:

1. Aksenov A.L., Kozlov O.I. (2021) Satellite and aerial imagery geo-referencing using ground features. Geodezia i Kartografia, 82(9), pp. 21-29. (In Russian). DOI: 10.22389/0016-7126-2021-975-9-21-29.

2. Arbuzov S. A., Khlebnikova E. P., Nikitin V. N. Avtomatizirovannaya identifikatsiya i opredelenie porodnogo sostava drevesnykh rastenii po materialam tsifrovoi mnogozonal'noi aeros"emki lesnykh massivov. Vestnik SGUGiT, 2020, Vol. 24, no. 4, pp. 68–76. DOI: 10.33764/2411-1759-2020-25-4-68-76.

3. Gusev V. N., Puporevich A. A. Povyshenie tochnosti giroskopicheskogo orientirovaniya za schet ucheta dreifa giroazimutov. Gornyi informatsionno-analiticheskii byulleten' (nauchno-tekhnicheskii zhurnal), 2021, no. 10, pp. 134–145. DOI: 10.25018/0236_1493_2021_10_0_134.

4. Kovyazin V. F., Kitsenko A. A., Shobairi S. O. R. Kadastrovaya otsenka lesnykh zemel' s uchetom stepeni razvitosti ikh infrastruktury. Zapiski Gornogo in-ta, 2021, Vol. 249, pp. 449–462. DOI: 10.31897/PMI.2021.3.14.

5. Kremcheev E. A., Danilov A. S., Smirnov Yu. D. Sostoyanie metrologicheskogo obespecheniya sistem monitoringa na baze bespilotnykh vozdushnykh sudov. Zapiski Gornogo in-ta, 2019, Vol. 235, pp. 96–105. DOI: 10.31897/PMI.2019.1.96.

6. Mokrova S.M., Petrov R.P., Milich V.N. (2020) Algorithm for determining the orientation elements of an infrared image obtained from a drone using reference points. Geodezia i Kartografia, 81(4), pp. 41-50. (In Russian). DOI: 10.22389/0016-7126-2020-958-4-41-50.

7. Mustafin M. G., Balandin V. N., Bryn' M. Ya., Matveev A. Yu., Men'shikov I. V., Firsov Yu. G. Topografo-geodezicheskoe i kartograficheskoe obespechenie Arkticheskoi zony Rossiiskoi Federatsii. Zapiski Gornogo in-ta, 2018, Vol. 232, pp. 375–382. DOI: 10.31897/PMI.2018.4.375.

8. Khlebnikova T. A., Yambaev Kh. S., Opritova O. A. Razrabotka tekhnologicheskoi skhemy sbora i obrabotki dannykh aerofotos"emki s ispol'zovaniem bespilotnykh aviatsionnykh sistem dlya modelirovaniya geoprostranstva. Vestnik SGUGiT, 2020, Vol. 25, no. 1, pp. 106–118. DOI: 10.33764/2411-1759-2020-25-1-106-118.

9. Badenko V., Fedotov A., Vinogradov K. (2018) Algorithms of laser scanner data processing for ground surface reconstruction. Lecture notes in computer science, pp. 397–411. DOI: 10.1007/978-3-319-95165-2_28.

10. Bykasov D. A., Zubov A. V., Mustafin M. G. (2020) Applying Newton's second order optimization method to define transition keys between planar coordinate systems. E3S Web of Conferences, Volume 224, no. 01003, pp. 1–9. DOI: 10.1051/e3sconf/202022401003.

11. Bykowa E., Dyachkova I. (2021) Modeling the size of protection zones of cultural heritage sites based on factors of the historical and cultural assessment of lands. Land, no. 10 (11), pp. 1–20. DOI: 10.3390/land10111201.

12. Camarretta N., Ehbrecht M., Seidel D., Wenzel A., Zuhdi M., Merk M. S., Schlund M., Erasmi S., Knohl A. (2021) Using airborne laser scanning to characterize land-use systems in a tropical landscape based on vegetation structural metrics. Remote Sensing, no. 13 (23), pp. 1–23. DOI: 10.3390/rs13234794.

13. Chen J., Chen Y., Liu Z. (2021) Classification of typical tree species in laser point cloud based on deep learning. Remote Sensing, no. 13 (23), pp. 1–21. DOI: 10.3390/rs13234750.

14. Deschaud J.-E., Duque D., Richa J. P., Velasco-Forero S., Marcotegui B., Goulette F. (2021) Paris-carla-3d: A real and synthetic outdoor point cloud dataset for challenging tasks in 3d mapping. Remote Sensing, no. 13 (22), pp. 1–24. DOI: 10.3390/rs13224713.

15. Kochneva A. A., Vinogradov E. A., Sankovsky A. A. (2020) Study of laser scanning data redundancy for constructing digital simulation of terrain. International Journal of Advanced Science and Technology, no. 29 (9s),

16. Kopylova N. S., Starikov I. P. (2018) Fresh approaches to Earth surface modeling. Journal of Physics: Conference Series. Institute of Physics Publishing, Ser. 1015.1015. no. 3, pp. 1–5. DOI: 10.1088/1742-6596/1015/3/032062.

17. Krisanski S., Taskhiri M. S., Gonzalez S., Herries`s D., Muneri A., Gurung`s M. B., Montgomery`s J., Turner P. (2021) Forest structural complexity tool – an open source, fully-automated tool for measuring forest point clouds. Remote Sensing, no. 13 (22), pp. 1–31. DOI: 10.3390/rs13224677.

18. Kuzin A. A., Kovshov S. V. (2017) Accuracy evaluation of terrain digital models for landslide slopes based on aerial laser scanning results. Ecology, environment and conservation, no. 249 (5), pp. 908–914.

19. Li H., Ye W., Liu J., Pirasteh S., Fatholahi S. N., Li J. (2021) High-resolution terrain modeling using airborne lidar data with transfer learning. Remote Sensing, no. 13 (17), pp. 1–19. DOI: 10.3390/rs13173448.

20. Mustafin M., Bykasov D. (2021) Adjustment of planned surveying and geodetic networks using second-order nonlinear programming methods. Computation, Volume 9, no. 131, pp. 18–27. DOI: 10.3390/computation9120131.

21. Sharma M., Garg R. D., Badenko V., Fedotov A., Min L., Yao A. (2020) Potential of airborne LiDAR data for terrain parameters extraction. Quaternary International, no. 575-576, pp. 317–327. DOI: 10.1016/j.quaint.2020.07.039.

22. Vystrchil M. G., Sukhov A., Novozhenin S., Popov A. V., Guba S. A. (2020) Quality analysis of digital photogrammetric models obtained in low light conditions. Journal of Physics, Conference Series, no. 1661 (1), pp. 1–6. DOI: 10.1088/1742-6596/1661/1/012089.

Citation:

Valkov V.A.,

Vinogradov K.P.,

Valkova E.O.,

Mustafin M.G.,

(2022) Creating highly informative rasters based on laser scanning and aerial photography data. Geodesy and cartography = Geodezia i Kartografia, 83(11), pp. 40-49. (In Russian). DOI: 10.22389/0016-7126-2022-989-11-40-49