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., Mohan, R. (2015). New Horizons in Planning Smart Cities using LiDAR Technology. International Journal of Applied Remote Sensing and GIS , 1(2), 40–50. Jankowska, M., Oleński, W., Błażejewski, M. (2008). Analiza urbanistyczna lokalizacji budynków wysokościowych na obszarze śródmieścia Warszawy . Warszawa: Miejska Pracownia Planowania Przestrzennego i Strategii Rozwoju. Jaśkiewicz, M. et. al. (2009). Możliwości lokalizacji obiektów wysokościowych w aspekcie ochrony panoramy miasta Krakowa – analiza . Kraków: Biuro Planowania Przestrzennego Urzędu Miasta Krakowa


Smart Cities are no longer just an aspiration, they are a necessity. For a city to be smart, accurate data collection or improvement the existing ones is needed, also an infrastructure that allows the integration of heterogeneous geographic information and sensor networks at a common technological point. Over the past two decades, laser scanning technology, also known as LiDAR (Light Detection and Ranging), has become a very important measurement method, providing high accuracy data and information on land topography, vegetation, buildings, and so on. Proving to be a great way to create Digital Terrain Models. The digital terrain model is a statistical representation of the terrain surface, including in its dataset the elements on its surface, such as construction or vegetation. The data use in the following article is from the LAKI II project “Services for producing a digital model of land by aerial scanning, aerial photographs and production of new maps and orthophotomaps for approximately 50 000 sqKm in 6 counties: Bihor, Arad, Hunedoara, Alba, Mures, Harghita including the High Risk Flood Zone (the border area with the Republic of Hungary in Arad and Bihor)”, which are obtained through LiDAR technology with a point density of 8 points per square meter. The purpose of this article is to update geospatial data with a higher resolution digital surface model and to demonstrate the differences between a digital surface models obtain by aerial images and one obtain by LiDAR technology. The digital surface model will be included in the existing geographic information system of the city Marghita in Bihor County, and it will be used to help develop studies on land use, transport planning system and geological applications. It could also be used to detect changes over time to archaeological sites, to create countur lines maps, flight simulation programs, or other viewing and modelling applications.


The aim of the research carried out in 2018 and financed by the Forest Fund was the analysis of biometric features and parameters of pine stands in the area of the “Bory Tucholskie” National Park (PNBT), where a program of active protection of lichen was initiated in 2017. Environmental analyses were conducted in relation to selected biometric features of trees and stands using laser scanning (LiDAR), including ULS (Unmanned Laser Scanning; RIEGL VUX-1) and TLS (Terrestrial Laser Scanning; FARO FOCUS 3D; X130). Thanks to the application of LiDAR technology, the structure of pine stands was precisely determined by means of a series of descriptive statistics characterizing the 3D spatial structure of vegetation. Using the Trees Crown Model (CHM), the analysis of the volume of tree crowns and the volume of space under canopy was performed. For the analysed sub-compartments, GIS solar analyses were carried out for the solar energy reaching the canopy and the ground level due to active protection of lichen. Multispectral photos were obtained using a specialized RedEdge-M camera (MicaSense) mounted on the UAV multi rotor platform Typhoon H520 (Yuneec). Flights with a thermal camera were also performed in order to detect places on the ground with high temperature. Plant indices: NDVI, NDRE, GNDVI and GRVI were also calculated for sub-compartments. The data obtained in 2017 and 2018 were the basis for spatial and temporal analyses of 4-D changes in stands which were related to the removal of some trees and organic layer (litter, moss layer).

REFERENCES 1. Chatterjee, S. (2017). Different Kinds of Convolutional Filters. Available at 2. Prabhu, R. (2018). Understanding of Convolutional Neural Network (CNN) – Deep Learning. Available at 3. Carter, J., Schmid, K., Waters, K., Betzhold, L., Hadley, B., Mataosky, R., & Halleran, J. (2012). Lidar 101: An Introduction to Lidar Technology, Data, and Applications . National Oceanic

References 1. Amar, N., (2006) LIDAR technology overview. ETI–US Geological Survey , Retrieved August 17. 2. Broggi, A. et al . (2013) Extensive Tests of Autonomous Driving Technologies. IEEE Transactions on Intelligent Transportation Systems , 14.3. 1403–1415. 3. Chirchi, V.R.E et al . (2011) Iris Biometric Recognition for Person Identification in Security Systems, International Journal of Computer Application , 24, 9. 0975-8887. 4. Jawhar, I, Mohamed, N. and Usmani, H. (2013) An Overview of Inter-Vehicular Communication Systems, Protocols and Middleware

LITERATURE Bakuła, K. (2015). Multispectral airborne laser scanning-a new trend in the development of LiDAR technology. Archiwum Fotogrametrii, Kartografii i Teledetekcji 27, 25-44. Będkowski, J., Bratuś, R., Prochaska, M., & Rzonca, A. (2015). Use of parallel computing in mass processing of laser data. Archiwum Fotogrametrii, Kartografii i Teledetekcji, 27, 45-59. Cisło-Lesicka, U., Borowiec, N., Marmol, U., & Pyka, K. (2014). Analiza przydatności lotniczego skaningu laserowego do opracowania modelu budynków 3D zgodnego ze specyfikacją INSPIRE. Archiwum

School on LiDAR Technology 2008 IIT Kanpur, India. 13. Ressl C., Mandlburger G., Pfeifer N.: Investigating adjustment of Airborne Laser Scanning strips without usage of GNSS/IMU trajectory data . ISPRS Archives –Volume XXXVIII-3/W8, pp.195-200. 2009. 14. Torr, P., Murray, D.: The development and comparison of robust methods for estimating the fundamental matrix . Int. J. Comput. Vision 24(3), 271–300. 1997. 15. Vosselman G.: Strip offset estimation using linear features . 3 rd International Workshop on Mapping Geo-Surfical Processes using Laser Altimetry, Columbs

Terrestrial Laser Scanning data and images. In RevCAD – Journal of Geodesy and Cadastre , No. 9, pg. 309-318, Aeternitas Publishing House, Alba Iulia, Romania. Bassole, A., Brunner, J., Tunstall, D., 2001. GIS: Supporting Environmental Planning and Management in West Africa. World Resources Institute, London, United Kingdom. Birjaru, C., 2011. Cercetări privind utilizarea tehnologiei LiDAR în lucrările de silvicultură (Study regarding the use of LiDAR technology in forestry), Summary of PhD Theses, Brasov, Romania. Danson, F.M., Hetherington, D., Morsdorf, F., Koetz, B

References Akkay, A. E., Oguz, H., Karas, I. R., Aruga, K., 2009: Using LiDAR technology in forestry activities. Environmental Monitoring and Assessment, 151:117-125. Assmann, E., Franz, F., 1963: Vorläufige Fichten-Ertagstafel für Bayern. Fortw Centralbl, 84:1-68. Bavlšík, J. et al., 2009: Pracovné postupy hospodárskej úpravy lesov. Zvolen, NLC, 147 p. Bradley, R. T., Christie, J. M., Johnson, D. R., 1966: Forest management tables. Forestry Commission Booklet no 16. Forestry Commission, London, 212 p. Eysn, L., Hollaus, M., Lindberg, E., Berger, F., Monnet, J. M

. Springer, Karlsruhe, pp. 39-51, 152-164. Anguelov, D., Taskarf, B., Chatalbashev, V., Koller, D., Gupta, D., Heitz, G., Ng, A., 2005. Discriminative learning of Markov Random Fields for segmentation of 3D scan data. IEEE Computer Society Conference on Computer Vision and Pattern Recognition , Vol. 2, pp. 169–176. Gaurav, S. G., 2017. Light Detection and Ranging (LiDAR): Technologies and Global Markets. BCC Research report. Hall, M. A., 1999. Doctoral Thesis. Correlation-based Feature Selection for Machine Learning, University of Waikato, Hamilton, New Zealand, pp. 25