Preliminary Result of 3D City Modelling For Hanoi, Vietnam

Anh, Phan; Thanh, Nguyen Thi Nhat; Vu, Chu Thua; Hà, Nguyễn Việt; Hung, Bui Quang

doi:10.1109/nics.2018.8606867

Cited by 4 publications

(6 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The predominance of buildings with numerous pitched roofs and variable slopes makes these test cases quite tricky. For the variety of roof examples and the peculiarities of such stratified urban contexts, a mean absolute error (MAE) of about 2 m was considered a satisfying accuracy target for this work, in accordance with results presented in the literature [28,46]. This accuracy target considers that higher error values can be expected with a random test set since many ground-truth buildings could be temporally inconsistent or changed over time.…”

Section: Accuracy Aimssupporting

confidence: 54%

“…For the 3D reconstruction of historical urban scenarios, buildings' footprints and some neighbours or categorical features are the only information usable for the prediction. In [28] and [46], buildings' heights are inferred exploiting some machine learning techniques, relying on cadastral and statistical data, as well as some geometrical information extracted from the footprints. We hereafter present a similar approach, based only on geometric, neighbour, and categorical features computable from the digitised historical buildings.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

4D Building Reconstruction with Machine Learning and Historical Maps

2021

View full text Add to dashboard Cite

The increasing importance of three-dimensional (3D) city modelling is linked to these data’s different applications and advantages in many domains. Images and Light Detection and Ranging (LiDAR) data availability are now an evident and unavoidable prerequisite, not always verified for past scenarios. Indeed, historical maps are often the only source of information when dealing with historical scenarios or multi-temporal (4D) digital representations. The paper presents a methodology to derive 4D building models in the level of detail 1 (LoD1), inferring missing height information through machine learning techniques. The aim is to realise 4D LoD1 buildings for geospatial analyses and visualisation, valorising historical data, and urban studies. Several machine learning regression techniques are analysed and employed for deriving missing height data from digitised multi-temporal maps. The implemented method relies on geometric, neighbours, and categorical attributes for height prediction. Derived elevation data are then used for 4D building reconstructions, offering multi-temporal versions of the considered urban scenarios. Various evaluation metrics are also presented for tackling the common issue of lack of ground-truth information within historical data.

show abstract

Section: Accuracy Aimssupporting

confidence: 54%

Section: Related Workmentioning

confidence: 99%

4D Building Reconstruction with Machine Learning and Historical Maps

2021

View full text Add to dashboard Cite

show abstract

“…Some of the cities with a level of completeness of 10-20% may provide a sufficient amount of data for training a regression model to predict the heights of the remaining portion of the buildings that do not contain height information. In fact, the feasibility of this idea was demonstrated by a replication of the method in the same study area of this research- Anh et al (2018) implemented the approach in Hanoi, Vietnam.…”

Section: Discussionmentioning

confidence: 94%

“…The goal of this paper is to investigate the state of open building data in Southeast Asia to understand the current potential of generating 3D building models from free and public sources. While there are 3D city modelling developments across several countries in the region (Dissegna et al, 2019;Lagahit and Blanco, 2019;Aditya and Laksono, 2017;Stouffs et al, 2018;Ujang et al, 2018;Anh et al, 2018;Laksono and Aditya, 2019), rarely any work is based entirely on open data.…”

Section: Introductionmentioning

confidence: 99%

Exploration of Open Data in Southeast Asia to Generate 3d Building Models

Biljecki

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. This article investigates the current status of generating 3D building models across 11 countries in Southeast Asia from publicly available data, primarily volunteered geoinformation (OpenStreetMap). The following countries are analysed: Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, Timor-Leste, and Vietnam. This cross-country study includes multiple spatial levels of analysis: country, town, and micro-level (smaller neighbourhood). The main finding is that authoritative data to generate 3D building models is almost non-existent while building completeness in OpenStreetMap is highly heterogeneous, yielding location-dependent conclusions. While in general just a fraction of mapped buildings has height information and none of the administrative areas provides sufficient information to generate 3D building models, on a micro-level some areas are fully complete, providing a high potential to generate 3D building models on a precinct scale, which may be useful for certain spatial analyses. Furthermore, some areas have high building completeness, requiring only half of the work necessary for the extrusion: the collection of building height attributes. As a part of this work, a semantic 3D building model of a selected set of buildings in Singapore has been generated and released as open data (CityJSON), and the developed code was open-sourced.

show abstract

“…This is a direct result of the planning restrictions in Hanoi which restricted the height of buildings in most parts of the city other than the New Urban Areas where high-rise apartment towers were and still are permitted. In the inner city, houses often have a height of 10-13 meters and only 6-8 meters in the case of the Ancient Quarter [35]. The two-story facade of tube houses, which are the most popular dwelling typology in the Ancient Quarter, offers ideal conditions for audiovisual communication between the residents and people from the street, creating a very human-scaled streetscape [36].…”

Section: Human Scalementioning

confidence: 99%

Evaluation of Urban Design Qualities across Five Urban Typologies in Hanoi

Stevenson

Thompson

et al. 2021

Urban Science

View full text Add to dashboard Cite

Urban design has been shown to play a vital role in promoting the health and wellbeing of urban citizens. However, studies of microscale urban design are underrepresented in comparison with macroscale urban design, especially from low- and middle-income countries in Asia, where urban forms are traditionally compact, complex and with multiple layers and varied urban typologies. The study evaluated microscale urban design qualities of streets (n = 40) across five urban typologies in Hanoi—a typical city in a low- and middle-income country in Asia. The study found that urban typologies and their characteristics have particular impacts on urban design qualities. Old and high-density urban typologies tend to report higher urban design qualities than modern and low-density typologies. Urban design qualities are also significantly associated with the number of pedestrians on the streets. Compared to Western cities, the urban design qualities in Hanoi are substantially different, especially in terms of imageability and complexity, reflecting the differences in urban design and cultural context between cities from various regions. Overall, the study contributes to our understanding of urban design circumstances in Hanoi, providing policymakers, planners, urban designers and architects with important insights for sustainable urban design policies, strategies and interventions.

show abstract

Preliminary Result of 3D City Modelling For Hanoi, Vietnam

Cited by 4 publications

References 2 publications

4D Building Reconstruction with Machine Learning and Historical Maps

4D Building Reconstruction with Machine Learning and Historical Maps

Exploration of Open Data in Southeast Asia to Generate 3d Building Models

Evaluation of Urban Design Qualities across Five Urban Typologies in Hanoi

Contact Info

Product

Resources

About