3d City Modelling of Istanbul Based on Lidar Data and Panoramic Images – Issues and Challenges

Büyüksali̇h, Gürcan; Baskaraca, P.; Bayburt, S.; Buyuksalih, I.; Rahman, Alias Abdul

doi:10.5194/isprs-archives-xlii-4-w12-51-2019

Cited by 12 publications

(6 citation statements)

References 2 publications

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“…This contrasts with purely geometrical and graphical models such as KML, VRML, OBJ, GLTF, COLLADA, or X3D, which do not provide enough semantics. 3D city modelling of Istanbul [56], for example, used LiDAR data collected from different platforms (helicopters, car, and backpack system). They have created 3D model of buildings only in 3D CAD (DGN and DWG files) and BLOB.xml format.…”

Section: Discussion and Future Developmentsmentioning

confidence: 99%

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Building Virtual 3D City Model for Smart Cities Applications: A Case Study on Campus Area of the University of Novi Sad

Jovanović¹,

Milovanov²,

Ruskovski³

et al. 2020

IJGI

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The Smart Cities data and applications need to replicate, as faithfully as possible, the state of the city and to simulate possible alternative futures. In order to do this, the modelling of the city should cover all aspects of the city that are relevant to the problems that require smart solutions. In this context, 2D and 3D spatial data play a key role, in particular 3D city models. One of the methods for collecting data that can be used for developing such 3D city models is Light Detection and Ranging (LiDAR), a technology that has provided opportunities to generate large-scale 3D city models at relatively low cost. The collected data is further processed to obtain fully developed photorealistic virtual 3D city models. The goal of this research is to develop virtual 3D city model based on airborne LiDAR surveying and to analyze its applicability toward Smart Cities applications. It this paper, we present workflow that goes from data collection by LiDAR, through extract, transform, load (ETL) transformations and data processing to developing 3D virtual city model and finally discuss its future potential usage scenarios in various fields of application such as modern ICT-based urban planning and 3D cadaster. The results are presented on the case study of campus area of the University of Novi Sad.

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Section: Discussion and Future Developmentsmentioning

confidence: 99%

“…Airborne Lidar sensors provide dense height information of large areas in an efficient manner and it can be used for the development of the 3D city model [56]. In our case study it has been operated with the parameters of 40 points per square meter, and aerial images with 5 cm per pixel.…”

Section: Acquisition Parameters and Basic Processingmentioning

confidence: 99%

Building Virtual 3D City Model for Smart Cities Applications: A Case Study on Campus Area of the University of Novi Sad

Jovanović¹,

Milovanov²,

Ruskovski³

et al. 2020

IJGI

View full text Add to dashboard Cite

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“…The combination of an existing Lidar and orthomosaic dataset (used as reference), with a new aerial image acquisition (including both vertical and oblique imagery) of higher resolution, for the creation of an accurate 3D city model was presented for the area of Kallithea, in Athens, Greece [17]. In another case the development of 3D city model was based on data captured from airborne LiDAR (Light Detection and Ranging) and panoramic images [18].…”

Section: Introductionmentioning

confidence: 99%

Creation of 3D infrastructure model from UAV and TLS data in the frame of PROION project

Nikolakopoulos,

Kyriou

2023

Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023)

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Mapping and monitoring of the Earth's surface is a three dimensional question. In any geological, hydrological, vegetation science, urban or ecological planning application there is a need for accurate Digital Elevation Model in order to extract the absolute surface elevation and terrain form (slope, aspect, etc.) information. Other applications, including city modelling and simulation, civil infrastructure monitoring, disaster management and emergency response, require 3D building models of high fidelity and accuracy. In the past, aerial photos or high-resolution satellite data and photogrammetry provided the necessary 3D information. In recent years, aerial images acquired by an Unmanned Aerial Vehicle (UAV) and point clouds derived from Terrestrial of airborne Laser scanners have become the mainstream solutions to 3D reconstruction of photorealistic 3D building models due to its cost-effectiveness and convenience. PROION project carries out an infrastructure monitoring methodology based on remote sensing and in situ measurements. Massive infrastructure like the building of Geology Department and Asteri dam in the Western Greece territory are surveyed using data from SAR interferometry, GNSS and micro-accelerometers sensors. In the frame of such a monitoring there is a need for very fine and highly accurate 3D infrastructure (city) model. The specific study evaluates the spatial resolution and accuracy of a 3D model derived from UAV and TLS data and compares it with classical DSM produced by Pleiades data and other high-resolution optical data in the frame of Copernicus program.

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“…The acquired point cloud data have a wide range of applications in surveying and mapping, and also for autonomous driving. It is an important data source for producing 3D city models of a higher level of detail (Wang et al, 2018;Xia et al, 2020), such as LoD3 in Wen et al (2019) and LoD4 using indoor modeling in Buyuksalih et al (2019). The buildings' facades can often be measured with a very high point density, especially in the areas adjacent to the ground.…”

Section: Introductionmentioning

confidence: 99%

Determination of building flood risk maps from LiDAR mobile mapping data

Feng,

Xiao,

Brenner

et al. 2022

Preprint

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With increasing urbanization, flooding is a major challenge for many cities today. Based on forecast precipitation, topography, and pipe networks, flood simulations can provide early warnings for areas and buildings at risk of flooding. Basement windows, doors, and underground garage entrances are common places where floodwater can flow into a building. Some buildings have been prepared or designed considering the threat of flooding, but others have not. Therefore, knowing the heights of these facade openings helps to identify places that are more susceptible to water ingress. However, such data is not yet readily available in most cities. Traditional surveying of the desired targets may be used, but this is a very time-consuming and laborious process. Instead, mobile mapping using LiDAR (light detection and ranging) is an efficient tool to obtain a large amount of high-density 3D measurement data. To use this method, it is required to extract the desired facade openings from the data in a fully automatic manner.This research presents a new process for the extraction of windows and doors from LiDAR mobile mapping data. Deep learning object detection models are trained to identify these objects. Usually, this requires to provide large amounts of manual annotations. In this paper, we mitigate this problem by leveraging a rule-based method. In a first step, the rule-based method is used to generate pseudo-labels. A semi-supervised learning strategy is then applied with three different levels of supervision. The results show that using only automatically generated pseudo-labels, the learning-based model outperforms the rule-based approach by 14.6% in terms of F 1 -score. After five hours of human supervision, it is possible to improve the model by another 6.2%.By comparing the detected facade openings' heights with the predicted water levels from a flood simulation model, a map can be produced which assigns per-building flood risk levels. Thus, our research provides a new geographic information layer for fine-grained urban emergency response. This information can be combined with flood forecasting to provide a more targeted disaster prevention guide for the city's infrastructure and residential buildings. To the best of our knowledge, this work is the first attempt to achieve such a large scale, fine-grained building flood risk mapping.

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3d City Modelling of Istanbul Based on Lidar Data and Panoramic Images – Issues and Challenges

Cited by 12 publications

References 2 publications

Building Virtual 3D City Model for Smart Cities Applications: A Case Study on Campus Area of the University of Novi Sad

Building Virtual 3D City Model for Smart Cities Applications: A Case Study on Campus Area of the University of Novi Sad

Creation of 3D infrastructure model from UAV and TLS data in the frame of PROION project

Determination of building flood risk maps from LiDAR mobile mapping data

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