A semi-automated method to create a lidar-based hydro-flattened DEM

Deshpande, Satish P.; Yilmaz, Alper

doi:10.1080/01431161.2017.1280632

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Meskipun hydro-flatteing dapat membantu dalam pemodelan hidrolik dan hidrologi, kebutuhan utama hydro-flattening didorong semata-mata oleh kebutuhan pemetaan kartografi. Hal ini dicapai dengan menggunakan garis 3D di sepanjang shoreline badan air (Deshpande, 2017). shoreline dapat didefinisikan sebagai garis kontak antara tanah dan air atau persimpangan tanah dengan permukaan air (Renslow, 2012).…”

Section: Pendahuluanunclassified

Optimalisasi Data DEM LiDAR pada Area Perairan Sungai

Yusuf

Setiaji

2023

ELIPSOIDA

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Light Detection and Ranging (LiDAR) merupakan salah satu alat yang dapat menghasilkan akurasi tinggi dalam mengukur tinggi di permukaan bumi. Dengan menggunakan teknologi LiDAR dapat digunakan untuk mendeteksi beberapa objek di permukaan bumi seperti ground, vegetasi atau bangunan, namun teknologi LiDAR mempunyai kelemahan yaitu sulit mendeteksi titik-titik di area perairan. Oleh karena itu kerapatan point cloud di daerah perairan termasuk kategori rendah, sehingga DEM yang diturunkan dari data LiDAR memiliki permukaan air yang tidak alami. Hydro-flattening adalah proses menciptakan DEM yang diturunkan dari data LiDAR dengan tujuan untuk membuat permukaan air sungai rata dan mengalir. Komponen penting dari pembuatan hydro flattening adalah breakline, Data utama pada proses ini adalah point cloud Lidar yang sudah terklasifkasi ground dan centerline. Ada 4 langkah dalam metode semi otomatis ini yaitu langkah pertama, Continous Bare Ground Surface (CBGS) dibuat dengan mencari elevasi terendah sepanjang sungai yang akan diproses. Pada langkah kedua, membuat radius pencarian berbentuk lingkaran yang berpusat pada centreline sungai untuk mencari elevasi point cloud LiDAR paling rendah di setiap lingkaran, elevasi tersebut digunakan untuk membuat Virtual Water Surface (VWS). VWS ini perlu dilakukan revisi karena ketinggian minimum pada data LiDAR tidak selalu merupakan ketinggian permukaan air, VWS yang direvisi ini disebut sengan Base Virtual Water Surface (B-VWS). Langkah ketiga adalah ekstraksi breakline dan smoothing hasil esktraksi breakline,. Langkah keempat adalah konversi breakline 2D ke 3D, hasil breakline 2D dimasukkan ketinggian dari B-VWS sehingga menjadi breakline 3D yang dapat digunakan untuk menghasilkan Hydro-flattening DEM. Hydro flattenning diterapkan pada 2 lokasi sungai yang berbeda-beda, dimana lokasi sungai 1 memiliki karakter sungai dengan kategori kecil, sedangkan sungai 2 memiliki karakter sungai yang bercabang. Dari hasil Hydro flattenning DEM LiDAR pada daerah perairan dapat sesuai dengan kondisi yang ada dilapangan yaitu perairan sungai yang datar dan mengalir dari hulu ke hilir. Dari kedua sungai tersebut membuktikan pentingnya dilakukan Hydro flattenning pada DEM LiDARKata kunci : LiDAR, Hydro-Flattening, Breakline, Continous Bare Ground Surface, Virtual Water Surface

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Section: Pendahuluanunclassified

Optimalisasi Data DEM LiDAR pada Area Perairan Sungai

Yusuf

Setiaji

2023

ELIPSOIDA

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“…Aerial lidar has been widely used in numerous studies in shoreline mapping (Li et al, 2008, Deshpande andYilmaz, 2017), transportation engineering (Luo et al, 2012), and many more fields (Deshpande, 2013). Mobile Mapping System utilizes multiple sensors including lidar to acquire 3D points from a mobile platform.…”

Section: Literature Reviewmentioning

confidence: 99%

Tunnel Modeling Using Mobile Mapping Lidar Points

Deshpande

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In this paper, a method to model a tunnel using lidar points is presented. The data used was collected using Leica Pegasus Two Ultimate with a Z+F 9012 Profiler mounted on a mobile platform. The tunnel was approximately 151 m long. Visual inspection of a cross-section of the tunnel showed two rail tracks supported on ballast and sidewalks along both sidewalls of the tunnel. The walls and the ceiling of the tunnel were made of five planar surfaces. The tunnel alignment was straight, without any horizontal or vertical curves. The bearing of the central axis of the tunnel was N12.2oW. The following methodology was developed to model just the planar surfaces of the tunnel by excluding the rails, ballast, sidewalks, powerlines, and other accessories. The entire methodology was divided into three broad parts. In the first part, a model cross-section was created. Since the design plans of the tunnel were not available, the model cross-section polyline was created using mean tunnel dimensions from random cross-section points. The model cross-section consisted of the walls and the ceiling of the tunnel. Points were placed at every 1 cm along the model polyline. Six of the model points that represented the shape of the tunnel were selected as salient points. The lower-left salient point was considered as the seed point. In the second part, to define a reference axis of the tunnel, an approximate centerline was manually defined by selecting points at its start and end. Lidar points within 1 m at the start and the end of the tunnel were modeled using the model points to determine the centroids. The reference axis was determined by connecting the centroids at the start and the end of the tunnel. In the third part, the tunnel points were sliced along the reference axis at 5 cm intervals. The model cross-section was matched to points within each tunnel slice using a three-stage approach. In the first stage, the pattern of salient points was matched to the tunnel points by placing the seed point at every tunnel point location. The distances between salient points and their nearest tunnel points were calculated. Ten sets of tunnel points with the least differences to the salient points were shortlisted. In the second stage, a dense point-to-point matching was performed between the model and sliced tunnel data at the shortlisted points. The shortlisted point location with the least difference between the tunnel and the model points was considered as a match. At this point, the model points were hinged to the tunnel points at the seed point location. Hence, in the last stage, a six-parameter affine transformation was performed to match the model points to the tunnel data. The transformed model points at every 5 cm of the length of the tunnel were considered as current shape of the tunnel.

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“…An onboard GPS/IMU system coupled with the range distance can determine the position of the ground point. Due to its direct ranging capabilities and few limitations, lidar is increasingly being used in numerous industries with applications such as structural shape modeling (Puente et al, 2016, Yi et al, 2019, geometric deformation modeling (Cabaleiro et al, 2015, Selvaraj andMadhavan, 2018), deformation determination in structural members (Cabaleiro et al, 2014), transportation engineering (Gézero and Antunes, 2019), and water resources engineering (Deshpande, 2013, Deshpande andYilmaz, 2017). The paper (Taheri Andani et al, 2018) showed the reliability of lidar sensors for the assessment of the top of rail lubricity in a laboratory environment.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Rollers Using Terrestrial Lidar Points in a Hot-Rolling Steel Mill

Deshpande

Falk²,

Plooster³

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Terrestrial lidar scanners are increasingly being used in numerous indoor mapping applications. This paper presents a methodology to model rollers used in hot-rolling steel mills. Hot-rolling steel mills are large facilities where steel is processed to different shapes. In a steel sheet manufacturing process, a steel slab is reheated at one end of the mill and is passed through multiple presses to achieve the desired cross-section. Hundreds of steel rollers are used to transport the steel slab from one end of the mill to the other. Over a period of use, these rollers wore out and need replacement. Manual determination of the damage to the rollers is a time-consuming task. Moreover, manual measurements can be influenced by the operator’s judgment. This paper presents a methodology to model rollers in a hot-rolling steel mill using lidar points. A terrestrial lidar scanner was used to collect lidar points over the roller surfaces. Data from several stations were merged to create a single point cloud. Using a bounding box, lidar points on all the rollers were clipped and used in this paper. The clipped data consisted of the roller as well as outlier points. Depending on the scan angles of scanner stations, partial surfaces of the rollers were scanned. A right-handed coordinate frame was used where the X-axis passed through the centers of all the rollers, Y-axis was parallel to the length of the first roller, and the Z-axis was in the plumb direction. Using a standard diameter of the roller, model roller points were created to extract the rollers. Both the lidar data and the model points were converted to rectangular prism-shaped voxels of dimensions 15.24 mm (0.05 ft) × 15.24 mm in the X and Z directions and extending over the entire width of the roller in the Y-direction. Voxels containing at least 40 lidar points were considered valid. Binary images of both the lidar points and the model points were created in the X-Z axes using the valid voxels. The roller locations in the lidar image were located by performing 2D FFT image matching using the model roller image. The roller points at the shortlisted locations were fitted with a circle equation to determine the mean roller diameters and mean center locations (roller’s rotation axis). The outlier points were filtered in this process for each roller. The elevation at the top of every roller was determined by adding their radii and Z-coordinates of its centers. Incorrectly located and/or modeled rollers were identified by implementing moving-average filters. Positively identified roller points were further analyzed to determine surface erosions and tilts. The above methodology showed that the rollers can be effectively modeled using the lidar points.

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A semi-automated method to create a lidar-based hydro-flattened DEM

Cited by 7 publications

References 14 publications

Optimalisasi Data DEM LiDAR pada Area Perairan Sungai

Optimalisasi Data DEM LiDAR pada Area Perairan Sungai

Tunnel Modeling Using Mobile Mapping Lidar Points

Modeling Rollers Using Terrestrial Lidar Points in a Hot-Rolling Steel Mill

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