scholarly journals Digital surface models and the landscape: interaction between bedrock and glacial geology in the Ullapool area

2009 ◽  
Vol 45 (2) ◽  
pp. 99-105 ◽  
Author(s):  
K. M. Goodenough ◽  
M. Krabbendam ◽  
T. Bradwell ◽  
A. Finlayson ◽  
A. G. Leslie
Keyword(s):  
Surface Model ◽  
Digital Surface Model ◽  
Glacial Geology ◽  
Glacial Geomorphology ◽  
Landscape Elements ◽  
Bedrock Geology ◽  
Geological Features ◽  
Surface Models ◽  
Elevation Data ◽  
Thrust Zone

SynopsisThe front cover image for this volume is a hill-shaded digital surface model (DSM) of the Ullapool area, created using NEXTMap Britain elevation data from Intermap Technologies. This is a classic area for bedrock geology, transected by the Moine Thrust Zone, and in recent years it has also been studied in detail for its glacial history. Perhaps equally important, this is one of Scotland's most iconic landscapes. The geology of the area comprises a number of distinct sequences, each of which has a characteristic landscape expression as illustrated by the DSM. This paper considers the influence of the bedrock geology on the glacial geomorphology, and shows that the interplay of the two has led to the development of the different landscape elements of this spectacular area. Surprisingly, it is not always the major geological features – such as the Moine Thrust – that have the strongest topographic expression.

scholarly journals BUILDING OUTLINE EXTRACTION FROM AERIAL IMAGERY AND DIGITAL SURFACE MODEL WITH A FRAME FIELD LEARNING FRAMEWORK

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 487-493
Author(s):  
X. Sun ◽  
W. Zhao ◽  
R. V. Maretto ◽  
C. Persello
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Surface Model ◽  
Digital Surface Model ◽  
Frame Field ◽  
Learning Framework ◽  
Elevation Data ◽  
3D Information ◽  
Direction Information

Abstract. Deep learning-based semantic segmentation models for building delineation face the challenge of producing precise and regular building outlines. Recently, a building delineation method based on frame field learning was proposed by Girard et al. (2020) to extract regular building footprints as vector polygons directly from aerial RGB images. A fully convolution network (FCN) is trained to learn simultaneously the building mask, contours, and frame field followed by a polygonization method. With the direction information of the building contours stored in the frame field, the polygonization algorithm produces regular outlines accurately detecting edges and corners. This paper investigated the contribution of elevation data from the normalized digital surface model (nDSM) to extract accurate and regular building polygons. The 3D information provided by the nDSM overcomes the aerial images’ limitations and contributes to distinguishing the buildings from the background more accurately. Experiments conducted in Enschede, the Netherlands, demonstrate that the nDSM improves building outlines’ accuracy, resulting in better-aligned building polygons and prevents false positives. The investigated deep learning approach (fusing RGB + nDSM) results in a mean intersection over union (IOU) of 0.70 in the urban area. The baseline method (using RGB only) results in an IOU of 0.58 in the same area. A qualitative analysis of the results shows that the investigated model predicts more precise and regular polygons for large and complex structures.

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 SEMI – GLOBAL MERGING OF DIGITAL SURFACE MODELS FROM MULTIPLE STEREOPAIRS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 267-271
Author(s):  
S. Pang ◽  
X. Hu ◽  
M. Zhang ◽  
L. Ye
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Experimental Results ◽  
Surface Model ◽  
Digital Surface Model ◽  
Global Optimization Algorithm ◽  
Smoothness Constraint ◽  
Dense Matching ◽  
Surface Models ◽  
Global Matching

The semi-global optimization algorithm, which approximates a global 2D smoothness constraint by combining several 1D constraints, has been widely used in the field of image dense matching for digital surface model (DSM) generation. However, due to occlusion, shadow and textureless area of the matching images, some inconsistency may exist in the overlapping areas of different DSMs. To address this problem, based on the DSMs generated by semi-global matching from multiple stereopairs, a novel semi-global merging algorithm is proposed to generate a reliable and consistent DSM in this paper. Two datasets, each covering 1&amp;thinsp;km<sup>2</sup>, are used to validate the proposed method. Experimental results show that the optimal DSM after merging can effectively eliminate the inconsistency and reduce redundancy in the overlapping areas.

scholarly journals Urban Feature Extraction from Merged Airborne LiDAR Data and Digital Camera Data

2021 ◽  
Vol 7 (2) ◽  
pp. 57-74
Author(s):  
Lamyaa Gamal EL-Deen Taha ◽  
A. I. Ramzi ◽  
A. Syarawi ◽  
A. Bekheet
Keyword(s):  
Neural Network ◽  
Digital Camera ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Surface Model ◽  
Digital Surface Model ◽  
3D Point Clouds ◽  
Digital Elevation ◽  
Surface Models ◽  
Elevation Model

Until recently, the most highly accurate digital surface models were obtained from airborne lidar. With the development of a new generation of large format digital photogrammetric aerial camera, a fully digital photogrammetric workflow became possible. Digital airborne images are sources for elevation extraction and orthophoto generation. This research concerned with the generation of digital surface models and orthophotos as applications from high-resolution images.  In this research, the following steps were performed. A Benchmark data of LIDAR and digital aerial camera have been used.  Firstly, image orientation, AT have been performed. Then the automatic digital surface model DSM generation has been produced from the digital aerial camera. Thirdly true digital ortho has been generated from the digital aerial camera also orthoimage will be generated using LIDAR digital elevation model (DSM). Leica Photogrammetric Suite (LPS) module of Erdsa Imagine 2014 software was utilized for processing. Then the resulted orthoimages from both techniques were mosaicked. The results show that automatic digital surface model DSM that been produced from digital aerial camera method has very high dense photogrammetric 3D point clouds compared to the LIDAR 3D point clouds. It was found that the true orthoimage produced from the second approach is better than the true orthoimage produced from the first approach. The five approaches were tested for classification of the best-orthorectified image mosaic using subpixel based (neural network) and pixel-based ( minimum distance and maximum likelihood).Multicues were extracted such as texture(entropy-mean),Digital elevation model, Digital surface model ,normalized digital surface model (nDSM) and intensity image. The contributions of the individual cues used in the classification have been evaluated. It was found that the best cue integration is intensity (pan) +nDSM+ entropy followed by intensity (pan) +nDSM+mean then intensity image +mean+ entropy after that DSM )image and two texture measures (mean and entropy) followed by the colour image. The integration with height data increases the accuracy. Also, it was found that the integration with entropy texture increases the accuracy. Resulted in fifteen cases of classification it was found that maximum likelihood classifier is the best followed by minimum distance then neural network classifier. We attribute this to the fine resolution of the digital camera image. Subpixel classifier (neural network) is not suitable for classifying aerial digital camera images. 

scholarly journals The Creation of Space Vector Models of Buildings From RPAS Photogrammetry Data

2017 ◽  
Vol 25 (2) ◽  
pp. 7-14
Author(s):  
Ondrej Trhan
Keyword(s):  
Surface Model ◽  
Digital Surface Model ◽  
Space Vector ◽  
Building Model ◽  
Surface Models ◽  
The Creation ◽  
Aircraft System

Abstract The results of Remote Piloted Aircraft System (RPAS) photogrammetry are digital surface models and orthophotos. The main problem of the digital surface models obtained is that buildings are not perpendicular and the shape of roofs is deformed. The task of this paper is to obtain a more accurate digital surface model using building reconstructions. The paper discusses the problem of obtaining and approximating building footprints, reconstructing the final spatial vector digital building model, and modifying the buildings on the digital surface model.

DETECTION OF COLLAPSED BUILDINGS DUE TO EARTHQUAKES USING A DIGITAL SURFACE MODEL CONSTRUCTED FROM AERIAL IMAGES

2014 ◽  
Vol 08 (01) ◽  
pp. 1450003 ◽  
Author(s):  
YOSHIHISA MARUYAMA ◽  
AKIRA TASHIRO ◽  
FUMIO YAMAZAKI
Keyword(s):  
Aerial Images ◽  
Surface Model ◽  
Digital Surface Model ◽  
Severe Damage ◽  
Aerial Photogrammetry ◽  
Surface Models ◽  
Event Models ◽  
Collapsed Buildings

The buildings that collapsed during the 2007 Niigata Chuetsu-oki earthquake are detected based on aerial photogrammetry using digital aerial images. The digital surface models (DSMs) in the area where severe damage to buildings was observed after the earthquake are constructed using digital aerial camera images. Pre- and post-event aerial images are employed to obtain the DSMs in this study. The differences in building heights between the pre- and post-event models are considered to detect collapsed buildings and the accuracy of the method is discussed in this paper. The results indicate that the collapsed buildings can be detected and undamaged buildings can also be correctly recognized by the proposed method.

scholarly journals Mapping with Pléiades—End-to-End Workflow

2019 ◽  
Vol 11 (17) ◽  
pp. 2052 ◽  
Author(s):  
Roland Perko ◽  
Hannes Raggam ◽  
Peter M. Roth
Keyword(s):  
Digital Terrain Model ◽  
Building Blocks ◽  
Surface Model ◽  
Data Sets ◽  
3D Mapping ◽  
Digital Surface Model ◽  
Terrain Model ◽  
Digital Terrain ◽  
Surface Models ◽  
End To End

In this work, we introduce an end-to-end workflow for very high-resolution satellite-based mapping, building the basis for important 3D mapping products: (1) digital surface model, (2) digital terrain model, (3) normalized digital surface model and (4) ortho-rectified image mosaic. In particular, we describe all underlying principles for satellite-based 3D mapping and propose methods that extract these products from multi-view stereo satellite imagery. Our workflow is demonstrated for the Pléiades satellite constellation, however, the applied building blocks are more general and thus also applicable for different setups. Besides introducing the overall end-to-end workflow, we need also to tackle single building blocks: optimization of sensor models represented by rational polynomials, epipolar rectification, image matching, spatial point intersection, data fusion, digital terrain model derivation, ortho rectification and ortho mosaicing. For each of these steps, extensions to the state-of-the-art are proposed and discussed in detail. In addition, a novel approach for terrain model generation is introduced. The second aim of the study is a detailed assessment of the resulting output products. Thus, a variety of data sets showing different acquisition scenarios are gathered, allover comprising 24 Pléiades images. First, the accuracies of the 2D and 3D geo-location are analyzed. Second, surface and terrain models are evaluated, including a critical look on the underlying error metrics and discussing the differences of single stereo, tri-stereo and multi-view data sets. Overall, 3D accuracies in the range of 0 . 2 to 0 . 3 m in planimetry and 0 . 2 to 0 . 4 m in height are achieved w.r.t. ground control points. Retrieved surface models show normalized median absolute deviations around 0 . 9 m in comparison to reference LiDAR data. Multi-view stereo outperforms single stereo in terms of accuracy and completeness of the resulting surface models.

scholarly journals Ekstraksi Digital Surface Model (DSM) dari Data Unmanned Aerial Vehicle (UAV) Berbasis Point Cloud

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Indra Laksana ◽  
R Suharyadi ◽  
M. Pramono Hadi
Keyword(s):  
Data Acquisition ◽  
Point Cloud ◽  
Unmanned Aircraft ◽  
Surface Model ◽  
Digital Surface Model ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Key Points ◽  
Elevation Data ◽  
Data Point

<div class="WordSection1"><p><strong>Abstr</strong><strong>ak. </strong>Akuisisi data dengan menggunakan pesawat tanpa awak semakin sering dilakukan. Penelitian ini memodelkan data elevasi dari pengukuran lapangan dengan menggunakan pesawat tanpa awak. Tujuan dari penelitian ini :(1) untuk menguji kemampuan pesawat tanpa awak dalam mengakuisisi data elevasi, dan (2) untuk membandingkan data elevasi jika ditambahkan data point cloud dan data pengukuran batimetri. Metode pengolahan dengan menggunakan data point cloud dilakukan dengan pertama-tama mencocokkan titik kunci. Pencocokan titik kunci mengkaitkan seluruh hasil foto udara hingga membentuk satu kesatuan area yang telah difoto. Selanjutnya dilakukan penampalan titik ikat pada area yang telah terbentuk dari pencocokan titik kunci. Titik ikat berfungsi sebagai koreksi data pada saat pesawat tanpa awak melakukan pengambilan data. Foto udara yang telah dikoreksi kemudian diolah untuk mendapatkan data <em>point cloud</em>. <em>Point cloud</em> berguna sebagai data penyusun ortofoto dan data <em>Digital Surface Model</em> (DSM). Pengolahan data point cloud hingga menghasilkan DSM dilakukan dengan menggunakan software Pix4D dan Agisoft photoscan. Hasil yang diperoleh menunjukkan bahwa terjadi peningkatan kemampuan DSM ketika data pointcloud ditambahkan data titik ikat dan data pengukuran batimetri. Sehingga dapat disimpulkan bahwa akuisisi data menggunakan pesawat tanpa awak mampu menghasilkan data yang dapat dipercaya. Selain dapat dipercaya akuisisi data dengan pesawat tanpa awak lebih murah jika dibandingkan dengan akuisisi data dengan foto udara.</p><p>Keywords:  digital surface model, pesawat tanpa awak, titik ikat</p><p><strong> </strong></p><p><strong>Abstract. </strong><em>Data acquisition using unmanned aircraft is increasingly being done. This study models elevation data from field measurements using unmanned aircraft. The purpose of this study: (1) to test the ability of unmanned aircraft to acquire elevation data, and (2) to compare elevation data if added point cloud data and bathymetry measurement data. The processing method using point cloud data is done by first matching key points. Matching key points links all aerial photography results to forming a single unit area that has been photographed.</em><em> </em><em>Next, a tie point is carried out in the area formed from matching key points. Tie points function as data correction when unmanned aircraft take data. Corrected aerial photos are then processed to obtain point cloud data.</em><em> </em><em>Point cloud is useful as orthophoto compiler data and Digital Surface Model (DSM) data.</em><em> </em><em>Point cloud data processing to produce DSM is done using Pix4D and Agisoft photoscan software.</em><em>The results obtained showed that there was an increase in DSM capabilities when point cloud data was added to the tie point data and bathymetry measurement data. So, it can be concluded that data acquisition using unmanned aircraft is able to produce reliable data. Besides being reliable, data acquisition with unmanned aircraft is cheaper compared to data acquisition with aerial photography.</em></p></div><strong><em>Keywords:</em> </strong>u<em>nmanned aerial vehicle, ground c point, Digital surface model</em><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; text-justify: inter-ideograph;"> </p>

Sign in / Sign up

Export Citation Format

Share Document