scholarly journals Aerial Laser Scanning in Archeology

2011 ◽  
Vol 6 ◽  
pp. 103-108
Author(s):  
Martina Faltýnová ◽  
Karel Pavelka
Keyword(s):  
Public Service ◽  
Laser Scanning ◽  
Archaeological Sites ◽  
Surface Model ◽  
Archaeological Research ◽  
Digital Surface Model ◽  
Limited Size ◽  
Aerial Laser Scanning ◽  
Custom Made ◽  
Data Source

Technology of aerial laser scanning is often well used for a DTM generation. The DTM (digital surface model) displayed in appropriate form, e.g. shaded surface, can be used as a data source for searching for archaeological sites. Aerial laser scanning data acquisition is unfortunately too expensive for non-commercial projects.  It can be solution to use the ALS data acquired primarily for another reason by public service. This data has in general lower density, than expensive custom-made data, but can be borrowed for research purpose in a limited size. We tested the data from The Czech Office for Surveying, Mapping and Cadastre. The aim was to find, if it is possible to use data characterized by density of about 1 point/m2 for archaeological research. We used the DTM in form of shaded surface and inspect the data around few well known archaeological sites from different periods. It is also possible to use different outputs from the original DTM to better display terrain discontinuities, which could be caused by human activity.

scholarly journals UAV AERIAL SURVEY: ACCURACY ESTIMATION FOR AUTOMATICALLY GENERATED DENSE DIGITAL SURFACE MODEL AND ORTHOTHOTO PLAN

2016 ◽  
Vol XLI-B6 ◽  
pp. 155-159
Author(s):  
M. A. Altyntsev ◽  
S. A. Arbuzov ◽  
R. A. Popov ◽  
G. V. Tsoi ◽  
M. O. Gromov
Keyword(s):  
Survey Data ◽  
Laser Scanning ◽  
Aerial Survey ◽  
Surface Model ◽  
Model Generation ◽  
Digital Surface Model ◽  
Reliable Criterion ◽  
Accuracy Estimation ◽  
Survey Accuracy ◽  
Digital Orthophoto

A dense digital surface model is one of the products generated by using UAV aerial survey data. Today more and more specialized software are supplied with modules for generating such kind of models. The procedure for dense digital model generation can be completely or partly automated. Due to the lack of reliable criterion of accuracy estimation it is rather complicated to judge the generation validity of such models. One of such criterion can be mobile laser scanning data as a source for the detailed accuracy estimation of the dense digital surface model generation. These data may be also used to estimate the accuracy of digital orthophoto plans created by using UAV aerial survey data. The results of accuracy estimation for both kinds of products are presented in the paper.

scholarly journals TESTING OF LAND COVER CLASSIFICATION FROM MULTISPECTRAL AIRBORNE LASER SCANNING DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 161-169 ◽  
Author(s):  
K. Bakuła ◽  
P. Kupidura ◽  
Ł. Jełowicki
Keyword(s):  
Land Cover ◽  
Laser Scanning ◽  
Near Infrared ◽  
Airborne Laser Scanning ◽  
Surface Model ◽  
Digital Surface Model ◽  
Airborne Laser ◽  
Laser Reflectance ◽  
Elevation Data ◽  
Intensity Images

Multispectral Airborne Laser Scanning provides a new opportunity for airborne data collection. It provides high-density topographic surveying and is also a useful tool for land cover mapping. Use of a minimum of three intensity images from a multiwavelength laser scanner and 3D information included in the digital surface model has the potential for land cover/use classification and a discussion about the application of this type of data in land cover/use mapping has recently begun. In the test study, three laser reflectance intensity images (orthogonalized point cloud) acquired in green, near-infrared and short-wave infrared bands, together with a digital surface model, were used in land cover/use classification where six classes were distinguished: water, sand and gravel, concrete and asphalt, low vegetation, trees and buildings. In the tested methods, different approaches for classification were applied: spectral (based only on laser reflectance intensity images), spectral with elevation data as additional input data, and spectro-textural, using morphological granulometry as a method of texture analysis of both types of data: spectral images and the digital surface model. The method of generating the intensity raster was also tested in the experiment. Reference data were created based on visual interpretation of ALS data and traditional optical aerial and satellite images. The results have shown that multispectral ALS data are unlike typical multispectral optical images, and they have a major potential for land cover/use classification. An overall accuracy of classification over 90% was achieved. The fusion of multi-wavelength laser intensity images and elevation data, with the additional use of textural information derived from granulometric analysis of images, helped to improve the accuracy of classification significantly. The method of interpolation for the intensity raster was not very helpful, and using intensity rasters with both first and last return numbers slightly improved the results.

scholarly journals TESTING OF LAND COVER CLASSIFICATION FROM MULTISPECTRAL AIRBORNE LASER SCANNING DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 161-169 ◽  
Author(s):  
K. Bakuła ◽  
P. Kupidura ◽  
Ł. Jełowicki
Keyword(s):  
Land Cover ◽  
Laser Scanning ◽  
Near Infrared ◽  
Airborne Laser Scanning ◽  
Surface Model ◽  
Digital Surface Model ◽  
Airborne Laser ◽  
Laser Reflectance ◽  
Elevation Data ◽  
Intensity Images

Multispectral Airborne Laser Scanning provides a new opportunity for airborne data collection. It provides high-density topographic surveying and is also a useful tool for land cover mapping. Use of a minimum of three intensity images from a multiwavelength laser scanner and 3D information included in the digital surface model has the potential for land cover/use classification and a discussion about the application of this type of data in land cover/use mapping has recently begun. In the test study, three laser reflectance intensity images (orthogonalized point cloud) acquired in green, near-infrared and short-wave infrared bands, together with a digital surface model, were used in land cover/use classification where six classes were distinguished: water, sand and gravel, concrete and asphalt, low vegetation, trees and buildings. In the tested methods, different approaches for classification were applied: spectral (based only on laser reflectance intensity images), spectral with elevation data as additional input data, and spectro-textural, using morphological granulometry as a method of texture analysis of both types of data: spectral images and the digital surface model. The method of generating the intensity raster was also tested in the experiment. Reference data were created based on visual interpretation of ALS data and traditional optical aerial and satellite images. The results have shown that multispectral ALS data are unlike typical multispectral optical images, and they have a major potential for land cover/use classification. An overall accuracy of classification over 90% was achieved. The fusion of multi-wavelength laser intensity images and elevation data, with the additional use of textural information derived from granulometric analysis of images, helped to improve the accuracy of classification significantly. The method of interpolation for the intensity raster was not very helpful, and using intensity rasters with both first and last return numbers slightly improved the results.

scholarly journals Physical Modelling of Blue Mussel Dropper Lines for the Development of Surrogates and Hydrodynamic Coefficients

2019 ◽  
Vol 7 (3) ◽  
pp. 65 ◽  
Author(s):  
Jannis Landmann ◽  
Thorsten Ongsiek ◽  
Nils Goseberg ◽  
Kevin Heasman ◽  
Bela Buck ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Blue Mussel ◽  
Physical Modelling ◽  
Point Clouds ◽  
Surface Model ◽  
Test Duration ◽  
Blue Mussels ◽  
Hydrodynamic Behaviour ◽  
Custom Made

In this work, laboratory tests with live bivalves as well as the conceptual design of additively manufactured surrogate models are presented. The overall task of this work is to develop a surrogate best fitting to the live mussels tested in accordance to the identified surface descriptor, i.e., the Abbott–Firestone Curve, and to the hydrodynamic behaviour by means of drag and inertia coefficients. To date, very few investigations have focused on loads from currents as well as waves. Therefore, tests with a towing carriage were carried out in a wave flume. A custom-made rack using mounting clamps was built to facilitate carriage-run tests with minimal delays. Blue mussels (Mytilus edulis) extracted from a site in Germany, which were kept in aerated seawater to ensure their survival for the test duration, were used. A set of preliminary results showed drag and inertia coefficients C D and C M ranging from 1.16–3.03 and 0.25 to 1.25. To derive geometrical models of the mussel dropper lines, 3-D point clouds were prepared by means of 3-D laser scanning to obtain a realistic surface model. Centered on the 3-D point cloud, a suitable descriptor for the mass distribution over the surface was identified and three 3-D printed surrogates of the blue mussel were developed for further testing. These were evaluated regarding their fit to the original 3-D point cloud of the live blue mussels via the chosen surface descriptor.

scholarly journals Updating Geospatial Data by Creating a High Resolution Digital Surface Model

2018 ◽  
Vol 8 (2) ◽  
pp. 51-58 ◽  
Author(s):  
Iuliana Adriana Cuibac Picu
Keyword(s):  
Laser Scanning ◽  
Smart Cities ◽  
Geographic Information ◽  
Geospatial Data ◽  
Aerial Images ◽  
Aerial Photographs ◽  
Planning System ◽  
Surface Model ◽  
Digital Surface Model ◽  
Digital Terrain

Abstract 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.

scholarly journals DSM-to-LoD2: Spaceborne Stereo Digital Surface Model Refinement

2018 ◽  
Vol 10 (12) ◽  
pp. 1926 ◽  
Author(s):  
Ksenia Bittner ◽  
Pablo d’Angelo ◽  
Marco Körner ◽  
Peter Reinartz
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Laser Scanning ◽  
Missing Values ◽  
Least Square ◽  
Rapid Expansion ◽  
Surface Model ◽  
Digital Surface Model ◽  
Sensing Applications ◽  
3D Surface

A digital surface model (DSM) provides the geometry and structure of an urban environment with buildings being the most prominent objects in it. Built-up areas change with time due to the rapid expansion of cities. New buildings are being built, existing ones are expanded, and old buildings are torn down. As a result, 3D surface models can increase the understanding and explanation of complex urban scenarios. They are very useful in numerous fields of remote sensing applications, in tasks related to 3D reconstruction and city modeling, planning, visualization, disaster management, navigation, and decision-making, among others. DSMs are typically derived from various acquisition techniques, like photogrammetry, laser scanning, or synthetic aperture radar (SAR). The generation of DSMs from very high resolution optical stereo satellite imagery leads to high resolution DSMs which often suffer from mismatches, missing values, or blunders, resulting in coarse building shape representation. To overcome these problems, we propose a method for 3D surface model generation with refined building shapes to level of detail (LoD) 2 from stereo half-meter resolution satellite DSMs using deep learning techniques. Mainly, we train a conditional generative adversarial network (cGAN) with an objective function based on least square residuals to generate an accurate LoD2-like DSM with enhanced 3D object shapes directly from the noisy stereo DSM input. In addition, to achieve close to LoD2 shapes of buildings, we introduce a new approach to generate an artificial DSM with accurate and realistic building geometries from city geography markup language (CityGML) data, on which we later perform a training of the proposed cGAN architecture. The experimental results demonstrate the strong potential to create large-scale remote sensing elevation models where the buildings exhibit better-quality shapes and roof forms than just using the matching process. Moreover, the developed model is successfully applied to a different city that is unseen during the training to show its generalization capacity.

scholarly journals Logging Trail Segmentation via a Novel U-Net Convolutional Neural Network and High-Density Laser Scanning Data

2022 ◽  
Vol 14 (2) ◽  
pp. 349
Author(s):  
Omid Abdi ◽  
Jori Uusitalo ◽  
Veli-Pekka Kivinen
Keyword(s):  
Laser Scanning ◽  
High Density ◽  
Superior Performance ◽  
Surface Model ◽  
Digital Surface Model ◽  
Commercial Thinning ◽  
Digital Elevation ◽  
Main Components ◽  
Mature Stands ◽  
Height Model

Logging trails are one of the main components of modern forestry. However, spotting the accurate locations of old logging trails through common approaches is challenging and time consuming. This study was established to develop an approach, using cutting-edge deep-learning convolutional neural networks and high-density laser scanning data, to detect logging trails in different stages of commercial thinning, in Southern Finland. We constructed a U-Net architecture, consisting of encoder and decoder paths with several convolutional layers, pooling and non-linear operations. The canopy height model (CHM), digital surface model (DSM), and digital elevation models (DEMs) were derived from the laser scanning data and were used as image datasets for training the model. The labeled dataset for the logging trails was generated from different references as well. Three forest areas were selected to test the efficiency of the algorithm that was developed for detecting logging trails. We designed 21 routes, including 390 samples of the logging trails and non-logging trails, covering all logging trails inside the stands. The results indicated that the trained U-Net using DSM (k = 0.846 and IoU = 0.867) shows superior performance over the trained model using CHM (k = 0.734 and IoU = 0.782), DEMavg (k = 0.542 and IoU = 0.667), and DEMmin (k = 0.136 and IoU = 0.155) in distinguishing logging trails from non-logging trails. Although the efficiency of the developed approach in young and mature stands that had undergone the commercial thinning is approximately perfect, it needs to be improved in old stands that have not received the second or third commercial thinning.

scholarly journals UAV AERIAL SURVEY: ACCURACY ESTIMATION FOR AUTOMATICALLY GENERATED DENSE DIGITAL SURFACE MODEL AND ORTHOTHOTO PLAN

2016 ◽  
Vol XLI-B6 ◽  
pp. 155-159 ◽  
Author(s):  
M. A. Altyntsev ◽  
S. A. Arbuzov ◽  
R. A. Popov ◽  
G. V. Tsoi ◽  
M. O. Gromov
Keyword(s):  
Survey Data ◽  
Laser Scanning ◽  
Aerial Survey ◽  
Surface Model ◽  
Model Generation ◽  
Digital Surface Model ◽  
Reliable Criterion ◽  
Accuracy Estimation ◽  
Survey Accuracy ◽  
Digital Orthophoto

A dense digital surface model is one of the products generated by using UAV aerial survey data. Today more and more specialized software are supplied with modules for generating such kind of models. The procedure for dense digital model generation can be completely or partly automated. Due to the lack of reliable criterion of accuracy estimation it is rather complicated to judge the generation validity of such models. One of such criterion can be mobile laser scanning data as a source for the detailed accuracy estimation of the dense digital surface model generation. These data may be also used to estimate the accuracy of digital orthophoto plans created by using UAV aerial survey data. The results of accuracy estimation for both kinds of products are presented in the paper.

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