scholarly journals Digital Deforestation: Comparing Automated Approaches to the Production of Digital Terrain Models (DTMs) in Agisoft Metashape

Quaternary ◽  
2022 ◽  
Vol 5 (1) ◽  
pp. 5
Author(s):  
Matthew D. Howland ◽  
Anthony Tamberino ◽  
Ioannis Liritzis ◽  
Thomas E. Levy
Keyword(s):  
Point Cloud ◽  
Archaeological Sites ◽  
Digital Terrain Models ◽  
Digital Terrain ◽  
Terrain Models ◽  
Cloud Classification ◽  
Classification Tool ◽  
Point Cloud Classification ◽  
User Friendly

This paper tests the suitability of automated point cloud classification tools provided by the popular image-based modeling (IBM) software package Agisoft Metashape for the generation of digital terrain models (DTMs) at moderately-vegetated archaeological sites. DTMs are often required for various forms of archaeological mapping and analysis. The suite of tools provided by Agisoft are relatively user-friendly as compared to many point cloud classification algorithms and do not require the use of additional software. Based on a case study from the Mycenaean site of Kastrouli, Greece, the mostly-automated, geometric classification tool “Classify Ground Points” provides the best results and produces a quality DTM that is sufficient for mapping and analysis. Each of the methods tested in this paper can likely be improved through manual editing of point cloud classification.

scholarly journals Detection of earthflow dynamics using medium-resolution digital terrain models: Diachronic perspective of the Jovac earthflow, Southern Serbia

2021 ◽  
Vol 61 (2) ◽  
pp. 187-206
Author(s):  
Marko Milošević ◽  
Dragoljub Štrbac ◽  
Jelena Ćalić ◽  
Milan Radovanović
Keyword(s):  
Digital Terrain Models ◽  
Digital Terrain ◽  
Terrain Models ◽  
Landslide Volume ◽  
Medium Resolution ◽  
Before And After ◽  
Movement Mechanism ◽  
Research Procedure ◽  
Definition Of

The paper presents and discusses the landslide research procedure related to the topography before and after its occurrence, using the comparative analysis of two medium-resolution digital terrain models. The case study is the Jovac mega-landslide—the largest landslide to occur in Serbia in the last 100 years, active for three days in February 1977. The indicators used to determine the volume and movement mechanism were the spatial distribution of elevation differences within the two digital terrain models (DTM), and the analysis of geomorphological features before the landslide. The obtained elevation differences allowed the definition of the approximate landslide volume: 11.6 × 106 m3. All the data obtained indicate that the movement mechanism falls into the category of earthflow.

scholarly journals SEMANTIC SEGMENTATION OF MANMADE LANDSCAPE STRUCTURES IN DIGITAL TERRAIN MODELS

2019 ◽  
Vol IV-2/W7 ◽  
pp. 87-94
Author(s):  
B. Kazimi ◽  
F. Thiemann ◽  
M. Sester
Keyword(s):  
Neural Network ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Archaeological Sites ◽  
Digital Terrain Models ◽  
Digital Terrain ◽  
Terrain Models ◽  
The Neural Network ◽  
Multidimensional Matrices ◽  
Rgb Images

<p><strong>Abstract.</strong> We explore the use of semantic segmentation in Digital Terrain Models (DTMS) for detecting manmade landscape structures in archaeological sites. DTM data are stored and processed as large matrices of depth 1 as opposed to depth 3 in RGB images. The matrices usually contain continuous real-valued information upper bound of which is not fixed, such as distance or height from a reference surface. This is different from RGB images that contain integer values in a fixed range of 0 to 255. Additionally, RGB images are usually stored in smaller multidimensional matrices, and are more suitable as inputs for a neural network while the large DTMs are necessary to be split into smaller sub-matrices to be used by neural networks. Thus, while the spatial information of pixels in RGB images are important only locally within a single image, for DTM data, they are important locally, within a single sub-matrix processed for neural network, and also globally, in relation to the neighboring sub-matrices. To cope with the two differences, we apply min-max normalization to each input matrix fed to the neural network, and use a slightly modified version of DeepLabv3+ model for semantic segmentation. We show that with the architecture change, and the preprocessing, better results are achieved.</p>

scholarly journals The vertical accuracy of digital terrain models derived from the close-range photogrammetry point cloud using different methods of interpolation and resolutions

2019 ◽  
Vol 65 (3-4) ◽  
pp. 198-205
Author(s):  
Juraj Čerňava ◽  
František Chudý ◽  
Daniel Tunák ◽  
Šimon Saloň ◽  
Zuzana Vyhnáliková
Keyword(s):  
Point Cloud ◽  
Moving Average ◽  
Statistical Significance ◽  
Modular System ◽  
Digital Terrain Models ◽  
Close Range Photogrammetry ◽  
Digital Terrain ◽  
Terrain Models ◽  
Wide Range ◽  
Close Range

Abstract Structure-from-motion (SfM) in combination with multi-view stereo (MVS) represent techniques, which allow efficient generation of the point cloud from close-range photogrammetry (CRP) images of forest ground. Recent software products for the generation of digital terrain models (DTM) includes a wide range of interpolation methods. Previous studies showed different errors in elevations of DTMs interpolated with different methods. This study aims to analyze differences between the elevations of DTMs derived from CRP point cloud using different methods of interpolation. Six methods of interpolation included in modular system OPALS were tested in the study. In addition to simple methods of interpolation such as Snap or Moving average, more complex methods were used for interpolation of the DTMs elevations. For each method, 5 DTMs with resolution ranging from 1 to 20 cm were generated. Elevations of the DTMs were compared with the elevations of Global Navigation Satellite System (GNSS) surveyed check points. RMSE of DTMs elevations ranges from 3.4 cm to 16.2 cm. Differences between the elevations of DTMs interpolated using different methods and resolution were further investigated using one-way analysis of variance (ANOVA). The ANOVA rejected the statistical significance of the differences. Additionally, the spatial distribution of errors was analyzed. The analysis indicates that the interpolation of the extreme DTM values can be expected at the edges of the DTM when using the CRP images captured from single passing through the study site.

scholarly journals Estimated Land and Building Tax using Aerial Photography (Case Study: Tingkir District, Salatiga City)

2020 ◽  
Vol 4 (2) ◽  
pp. 372-378
Author(s):  
Josef Allen ◽  
Frederik Samuel Papilaya
Keyword(s):  
Aerial Photographs ◽  
Digital Terrain Models ◽  
Raster Calculator ◽  
Digital Terrain ◽  
Terrain Models ◽  
Surface Models ◽  
Building Area ◽  
The City ◽  
Area Data

Salatiga, a city that borders directly and is surrounded by Semarang Regency. It has an area of ​​56.78 km2, with four sub-districts including, Sidorejo Regency, Argomulyo Regency, Tingkir Regency, and Sidomukti Regency, which covers 23 villages. The city of Salatiga has a fairly high population density with a proportion of more than 3300 inhabitants / km2. In this study took place in the Central Tingkir and Tingkir Lor areas by taking several points that are considered as housing as a case study. This study aims to estimate the price of land and building tax on an object of land and building tax. The process of this research uses aerial photo processing, where aerial photographs will be processed using professional software Agisoft photoscan to become an orthophoto, digital surface models and digital terrain models to obtain building area and land area data, then digital surface models and digital terrain models are processed . with ArcGIS 10.6 software using a raster calculator to get the height of the object to be used as the height of the building. The data obtained will be used to perform land and building tax calculations. The results of this study estimate the estimated land and building tax prices of at least IDR 37,313.00 and a maximum value of IDR 3,809,763.00.  

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