HL
H. Ledoux
149 records found
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Reconstructing urban scenarios for computational fluid dynamics simulations typically requires significant manual effort, especially when higher geometrical details are required. To address this issue, we present a workflow to automatically reconstruct buildings in three levels o
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We introduce CityJSON Text Sequences (CityJSONSeq in short), a format based on CityJSON and JSON Text Sequences. CityJSONSeq was added to the CityJSON specifications version 2.0 to allow us to stream very large 3D city models. The main idea is to decompose a CityJSON dataset into
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Digital Elevation Models (DEMs) are a necessity for modelling many large-scale environmental processes. In this study, we investigate the potential of data from two spaceborne lidar altimetry missions, ICESat-2 and GEDI—with respect to their vertical accuracies and planimetric da
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DeltaDTM
A global coastal digital terrain model
Coastal elevation data are essential for a wide variety of applications, such as coastal management, flood modelling, and adaptation planning. Low-lying coastal areas (found below 10 m +Mean Sea Level (MSL)) are at risk of future extreme water levels, subsidence and changing extr
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cjdb
A Simple, Fast, and Lean Database Solution for the CityGML Data Model
When it comes to storing 3D city models in a database, the implementation of the CityGML data model can be quite demanding and often results in complicated schemas. As an example, 3DCityDB, a widely used solution, depends on a schema having 66 tables, mapping closely the CityGML
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This paper presents a new algorithm for filling holes in Level of Detail 2 (LoD2) building mesh models, addressing the challenges posed by geometric inaccuracies and topological errors. Unlike traditional methods that often alter the original geometric structure or impose stringe
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Using Landsat land surface temperature as a proxy for air temperature in urban settings
Experiments in the Netherlands
Understanding the UHI effect in any city requires high-resolution temperature data. This data is often difficult to obtain as cities usually have only a few ground sensors, leaving large data gaps. To fill these gaps, we compare Landsat-derived land surface temperature (LST) with
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PSSNet
Planarity-sensible Semantic Segmentation of large-scale urban meshes
We introduce a novel deep learning-based framework to interpret 3D urban scenes represented as textured meshes. Based on the observation that object boundaries typically align with the boundaries of planar regions, our framework achieves semantic segmentation in two steps: planar
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We propose an enhancement module called depth discontinuity learning (DDL) for learning-based multi-view stereo (MVS) methods. Traditional methods are known for their accuracy but struggle with completeness. While recent learning-based methods have improved completeness at the co
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Historical maps are increasingly used for studying how cities have evolved over time, and their applications are multiple: understanding past outbreaks, urban morphology, economy, etc. However, these maps are usually scans of older paper maps, and they are therefore restricted to
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While three-dimensional (3D) building models play an increasingly pivotal role in many real-world applications, obtaining a compact representation of buildings remains an open problem. In this paper, we present a novel framework for reconstructing compact, watertight, polygonal b
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From road centrelines to carriageways
A reconstruction algorithm
Roads are important for many urban planning applications, such as traffic modelling and delivery vehicle routing. At present, most available datasets represent roads only as centrelines. This is particularily true for OpenStreetMap which provides, among many features, road networ
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In the computational fluid dynamics simulation workflow, the geometry preparation step is often regarded as a tedious, time-consuming task. Many practitioners consider it one of the main bottlenecks in the simulation process. The more complex the geometry, the longer the necessar
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Data on the number of floors is required for several applications, for instance, energy demand estimation, population estimation, and flood response plans. Despite this, open data on the number of floors is very rare, even when a 3D city model is available. In practice, it is mos
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3D-representaties van onze leefomgeving zijn belangrijk in toepassingen die helpen bij de planning, de inrichting en het beheer van de openbare ruimte. Met 3D-modellen kunnen simulaties worden uitgevoerd voor bijvoorbeeld geluid, energie, luchtkwaliteit, windcomfort, zicht en wat
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SUM
A benchmark dataset of Semantic Urban Meshes
Recent developments in data acquisition technology allow us to collect 3D texture meshes quickly. Those can help us understand and analyse the urban environment, and as a consequence are useful for several applications like spatial analysis and urban planning. Semantic segmentati
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Automated semantic segmentation and object detection are of great importance in geospatial data analysis. However, supervised machine learning systems such as convolutional neural networks require large corpora of annotated training data. Especially in the geospatial domain, such
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Three-dimensional city models are essential to assess the impact that environmental factors will have on citizens, because they are the input to several simulation and prediction software. Examples of such environmental factors are noise (Stoter et al., 2008), wind (Garcı́a-Sánch
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As web applications become more popular, 3D city models would greatly benefit from a proper web-based solution to visualise and manage them. CityJSON was introduced as a JSON encoding of the CityGML data model and promises, among several benefits, the ability to be integrated wit
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