Tracking the relationship between changing skyline and population growth of an Indian megacity using earth observation technology

Sanyal, Joy; Roychowdhury, Koel

doi:10.1080/10106049.2016.1213890

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…Another application of 3D city models in strategic planning is temporal detection of urban form changes [21], which can be attributed to the population changes as well [22]. Multi-dimensional urban modellings and analytics are instrumental in developing strategies for compact city and vertical urban development assessment [9,23].…”

Section: D City Models In Urban Planningmentioning

confidence: 99%

Three-Dimensional Rule-Based City Modelling to Support Urban Redevelopment Process

Agius

Sabri

Kalantari

2018

IJGI

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Multi-dimensional representation of urban settings has received a great deal of attention among urban planners, policy makers, and urban scholars. This is due to the fact that cities grow vertically and new urbanism strategies encourage higher density and compact city development. Advancements in computer technology and multi-dimensional geospatial data integration, analysis and visualisation play a pivotal role in supporting urban planning and design. However, due to the complexity of the models and technical requirements of the multi-dimensional city models, planners are yet to fully exploit such technologies in their activities. This paper proposes a workflow to support non-experts in using three-dimensional city modelling tools to carry out planning control amendments and assess their implications. The paper focuses on using a parametric three-dimensional (3D) city model to enable planners to measure the physical (e.g., building height, shadow, setback) and functional (e.g., mix of land uses) impacts of new planning controls. The workflow is then implemented in an inner suburb of Metropolitan Melbourne, where urban intensification strategies require the planners to carry out radical changes in regulations. This study demonstrates the power of the proposed 3D visualisation tool for urban planners at taking two-dimensional (2D) Geographic Information System (GIS) procedural modelling to construct a 3D model.

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Section: D City Models In Urban Planningmentioning

confidence: 99%

Three-Dimensional Rule-Based City Modelling to Support Urban Redevelopment Process

Agius

Sabri

Kalantari

2018

IJGI

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“…We also expect that semantic segmentation using deep networks could capture robust contextual features and provide a more accurate time series analysis of urban dynamics. Yet, to date, multi-annual reference data on vertical urban form are limited or simply not available (Koziatek and Dragićević 2017;Mahtta et al 2019;Sanyal and Roychowdhury 2017;Wurm et al 2013). This is probably one of the reasons why LCZ studies concentrated on a single slice in time (Bechtel et al 2016;Verdonck et al 2017;Yoo et al 2019), despite freely available Landsat time series.…”

Section: Introductionmentioning

confidence: 99%

Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: A semantic segmentation solution

Chen

Qiu

Schmitt

et al. 2020

Remote Sensing of Environment

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Landsat imagery is an unparalleled freely available data source that allows reconstructing land-cover and land-use change, including horizontal and vertical urban form. This paper addresses the challenge of using Landsat data, particularly its 30m spatial resolution, for monitoring three-dimensional urban densification. Unlike conventional convolutional neural networks (CNNs) for scene recognition resulting in resolution loss, the proposed semantic segmentation framework provides a pixel-wise classification and improves the accuracy of urban form mapping. We compare temporal and spatial transferability of an adapted DeepLab model with a simple fully convolutional network (FCN) and a texture-based random forest (RF) model to map urban density in the two morphological dimensions: horizontal (compact, open, sparse) and vertical (high rise, low rise). We test whether a model trained on the 2014 data can be applied to 2006 and 1995 for Denmark, and examine whether we could use the model trained on the Danish data to accurately map ten other European cities. Our results show that an implementation of deep networks and the inclusion of multi-scale contextual information greatly improve the classification and the model's ability to generalize across space and time. Between the two semantic segmentation models, DeepLab provides more accurate horizontal and vertical classifications than FCN when sufficient training data is available. By using DeepLab, the F1 score can be increased by 4 and 10 percentage points for detecting vertical urban growth compared to FCN and RF for Denmark. For mapping the ten other European cities with training data from Denmark, DeepLab also shows an advantage of 6 percentage points over RF for both horizontal and vertical dimensions. The resulting maps across the years 1985 to 2018 reveal different patterns of urban growth between Copenhagen and Aarhus, the two largest cities in Denmark, illustrating that those cities have used various planning policies in addressing population growth and housing supply challenges. In summary, we propose a transferable deep learning approach for automated, long-term mapping of urban form from Landsat images that is effective in areas experiencing a slow pace of urban growth or with small-scale changes.

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“…It controls the hydrological networks and their dynamics 4 . It depicts the spatial and vertical evolutions of our cities 5 . And lastly, its rapid changes over short time scales inform us of the ongoing deformations of the Earth surface (earthquake displacements 6,7 , ground subsidence due to water pumping 8 , evolution of glaciers 9 ), and of the extent of damaged areas due to natural and anthropogenic hazards 10 .…”

Section: Introductionmentioning

confidence: 99%

Digital surface model generation from multiple optical high-resolution satellite images

Mattéo

Tarabalka

Manighetti

2018

Image and Signal Processing for Remote Sensing XXIV

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The modern optical satellite sensors capture images in stereo and tri-stereo acquisition modes. This allows reconstruction of high-resolution (30-70 cm) topography from the satellite data. However, numerous areas on the Earth exhibit complex topography with a lot of "discontinuities". One case is tectonic fault sites, which form steep topographic escarpments enclosing narrow, deep corridors that mask parts of the ground. Built with common approaches (stereo or tri-stereo), a digital surface model (DSM) would not recover the topography in these masked zones. In this work, we have settled on a new methodology, based on the combination of multiple satellite Pleiades images taken with different geometries of acquisition (pitch and roll angles), with the purpose to generate fully-resolved DSMs at very high-resolution (50 cm). We have explored which configurations of satellites (i.e., number of images and ranges of pitch and roll angles) allow to best measure the topography inside deep and narrow canyons. We have collected seventeen Pleiades Images with different configurations over the Valley of Fire fault zone, USA, where the fault topography is complex. We have also measured sixteen ground control points (GCPs) in the zone. From all possible combinations of 2 to 17 Pleiades images, we have selected 150 combinations and have generated the corresponding DSMs. The calculations are done by solving an energy minimization problem that searches for a disparity map minimizing the energy, which depends on the likelihood for pixels to belong to a unique point in 3D as well as regularization terms. We have statistically studied which combinations of images deliver DSMs with the best surface coverage, as well as the lowest uncertainties on geolocalisation and elevation measures, by using the GCPs. Our first results suggest that an exceeding time between our acquisitions leads to DSM with a low covered area. We conclude that Stereo and Tri-Stereo acquisition in one-single pass of the satellite will systematically generate a better DSM than multi-date acquisition. We also conclude that in some cases, multi-date acquisitions with 7-8 images can improve the DSM robustness compared to multi-date acquisitions with fewer images. * matteo@geoazur.unice.fr;

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Tracking the relationship between changing skyline and population growth of an Indian megacity using earth observation technology

Cited by 4 publications

References 25 publications

Three-Dimensional Rule-Based City Modelling to Support Urban Redevelopment Process

Three-Dimensional Rule-Based City Modelling to Support Urban Redevelopment Process

Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: A semantic segmentation solution

Digital surface model generation from multiple optical high-resolution satellite images

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