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Analiza izbranih obstoječih algoritmov za generalizacijo digitalnega modela reliefa na primeru uporabe na izbranih področjih v geodeziji in geoinformatiki : diplomska naloga
Banovec, Gašper (Author), Ambrožič, Tomaž (Mentor) More about this mentor... This link opens in a new window, Podobnikar, Tomaž (Co-mentor), Stopar, Bojan (Thesis defence commission member), Kuhar, Miran (Thesis defence commission member)

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Abstract
Namen naloge je analiza izbranih obstoječih algoritmov za generalizacijo digitalnega modela reliefa na primeru uporabe na izbranih področjih v geodeziji in geoinformatiki kot so kartografija, prostorskih analiz v GIS-u in kontrola kakovosti. V praktičnem delu naloge sem uporabil testno območje Semiča, ki predstavlja ravninski del in semiško goro, ki se dviga nad Semičem. S pomočjo programa ArcGIS sem izvedel nekaj enostavnih analiz površja: izračun naklona, ekspozicije, ukrivljenosti in analitično senčenje z uporabo podatkov DMV 5, DMV 12,5 ter lidarskega DMR-ja,. Z bilinearno metodo interpolacije sem vse tri sloje prostorskih podatkov prevzorčil iz ločljivosti 12,5 m na ločljivost 25 m in 50 m in opravil analize. Rezultate analiz sem interpretiral v smislu treh izbranih področij v geodeziji in geoinformatiki. Lidarski DMR je najbolj kakovosten prikaz glede na stopnjo detajlnosti. DMV 12,5 in DMV 5 sta bolj skladna, predvsem v ravninskem delu, DMV 5 in lidarski DMR pa v hribovitem predelu. Analitično senčenje pri azimutu 45° daje nedefinirane prikaze površja, pri azimutu 135 obratno predstavo površja, hribi so doline in doline hribi, senčenje pri vertikalnem kotu 10° pa dolge sence. Generiranje najboljših plastnic dobimo iz lidarskega DMR-ja, kjer se plastnice generalizirajo in reducirajo predvsem na ravninskem predelu. Pri prevzorčenju na boljšo ločljivost iz 12,5 m na 1 m sem iskal napake na slojih. Pri posrednem vzorčenju sem v primerjavi s neposrednim dobil še bolj generalizirane prikaze. Ekspozicija je najmanj občutljiva na generalizacijo, veliko bolj pa so naklon in ukrivljenost, Pri ukrivljenosti se dovolj dobro razlike vidijo pri ločljivosti 25 m, kjer tudi vizualno opazimo razlike v konkavnosti in konveksnosti. Najmanj razlik opazimo pri tlorisni ukrivljenosti. S prevzorčenjem na manjšo ločljivost na 25 m in na 50 m se zmanjša kakovost prikaza in kakovost podatkov DMV-ja, vendar sem pri ločljivosti 50 m dobil enakovrednejše prikaze med sloji.

Language:Slovenian
Keywords:geodezija, diplomska naloga, UNI, digitalni model reliefa, generalizacija, kartografija, GIS analize, kakovost podatkov, analitično senčenje, prevzorčenje
Work type:Undergraduate thesis (m5)
Tipology:2.11 - Undergraduate Thesis
Organization:FGG - Faculty of Civil and Geodetic Engineering
Year:2016
Publisher:[G. Banovec]
Number of pages:XX, 121 str., [5] str. pril.
UDC:528.9 (043.2)
COBISS.SI-ID:7796065 This link opens in a new window
Views:1433
Downloads:582
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Secondary language

Language:English
Title:Analysis of selected algorithms for generalization digital elevation model with applications of selected fields of geodesy and geoinformatics
Abstract:
The purpose of the thesis is an analysis of selected existing algorithms for DEM generalization through case application in selected fields of geodesy and geoinformatics like cartography, spatial analyses in GIS, and quality control. In its practical part, I used the test area of Semič, consisting of a plain and the Semič hill rising above. Via ArcGIS program, I conducted several simple surface analyses: calculation of slope, angle, curvature, and analytical hillshading using DEM 5, DEM 12.5 and LIDAR data. By bilinear interpolation method, I resampled all three layers of spatial data from 12.5 resolution to 25m and 50m resolutions, and conducted analyses. I interpreted the analyses' results in the sense of three selected fields in geodesy and geoinformatics. LIDAR is the highest quality display regarding the level of detail. Especially in the flat part, DEM 12.5 and DEM 5 are more consistent, whereas DEM 5 and LIDAR tally better in the hilly tract. Analytical hillshading at 45° azimuth yields undefined surface displays, a reverse surface display at 135° azimuth, hills being valleys and valleys hills, while hillshading at a vertical 10° angle forms long shadows. The best contours are generated from LIDAR, where contours are generalized and reduced mainly in the flat tract. Resampling to better resolution from 12.5m to 1m, I searched for errors in the layers. Through indirect sampling, I obtained even more generalized displays compared to direct sampling. Exposition is least sensitive to generalization, while angle and curvature are much more so. In curvature, differences are well enough seen at 25m resolution, where we can also visually perceive the differences in concavity and convexity. The least differences are seen in plan curvature. When resampling to smaller resolution of 25m and 50m, the quality of display and DEM data decreases, but I obtained more equivalent inter-layer displays at 50m resolution.

Keywords:geodesy, graduation thesis, digital elevation model, generalization, generalization, cartography, GIS analysis, data quality, analitical hillshading, resampling

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