scholarly journals The “Paris-End” of Town? Deriving Urban Typologies Using Three Imagery Types

Urban Science ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 27
Author(s):  
Kerry A. Nice ◽  
Jason Thompson ◽  
Jasper S. Wijnands ◽  
Gideon D. P. A. Aschwanden ◽  
Mark Stevenson
Keyword(s):  
Neural Networks ◽  
Satellite Imagery ◽  
Urban Form ◽  
Urban Areas ◽  
Global Scale ◽  
Lessons Learned ◽  
Targeted Interventions ◽  
Advantages And Disadvantages ◽  
Street View ◽  
Social Demographic

Urban typologies allow areas to be categorised according to form and the social, demographic, and political uses of the areas. The use of these typologies and finding similarities and dissimilarities between cities enables better targeted interventions for improved health, transport, and environmental outcomes in urban areas. A better understanding of local contexts can also assist in applying lessons learned from other cities. Constructing urban typologies at a global scale through traditional methods, such as functional or network analysis, requires the collection of data across multiple political districts, which can be inconsistent and then require a level of subjective classification. To overcome these limitations, we use neural networks to analyse millions of images of urban form (consisting of street view, satellite imagery, and street maps) to find shared characteristics between the largest 1692 cities in the world. The comparison city of Paris is used as an exemplar and we perform a case study using two Australian cities, Melbourne and Sydney, to determine if a “Paris-end” of town exists or can be found in these cities using these three big data imagery sets. The results show specific advantages and disadvantages of each type of imagery in constructing urban typologies. Neural networks trained with map imagery will be highly influenced by the structural mix of roads, public transport, and green and blue space. Satellite imagery captures a combination of both urban form and decorative and natural details. The use of street view imagery emphasises the features of a human-scaled visual geography of streetscapes. However, for both satellite and street view imagery to be highly effective, a reduction in scale and more aggressive pre-processing might be required in order to reduce detail and create greater abstraction in the imagery.

scholarly journals Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 312
Author(s):  
Barbara Wiatkowska ◽  
Janusz Słodczyk ◽  
Aleksandra Stokowska
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Rate Of Increase ◽  
Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

Identification of Potential Freight Parking Locations in Urban Areas

2021 ◽  
pp. 036119812110130
Author(s):  
Glenn Vorhes ◽  
Ernest Perry ◽  
Soyoung Ahn
Keyword(s):  
United States ◽  
Information System ◽  
Satellite Imagery ◽  
Urban Areas ◽  
The United States ◽  
Truck Drivers ◽  
Short List ◽  
Automated Processes ◽  
Parking Facilities ◽  
Urban Sites

Truck parking is a crucial element of the United States’ transportation system as it provides truckers with safe places to rest and stage for deliveries. Demand for truck parking spaces exceeds supply and shortages are especially common in and around urban areas. Freight operations are negatively affected as truck drivers are unable to park in logistically ideal locations. Drivers may resort to unsafe practices such as parking on ramps or in abandoned lots. This report seeks to examine the potential parking availability of vacant urban parcels by establishing a methodology to identify parcels and examining whether the identified parcels are suitable for truck parking. Previous research has demonstrated that affordable, accessible parcels are available to accommodate truck parking. When used in conjunction with other policies, adaptation of urban sites could help reduce the severity of truck parking shortages. Geographic information system parcel and roadway data were obtained for one urban area in each of the 10 Mid America Association of Transportation Officials region states. Area and proximity filters were applied followed by spectral analysis of satellite imagery to identify candidate parcels for truck parking facilities within urban areas. The automated processes created a ranked short list of potential parcels from which those best suited for truck parking could be efficiently identified for inspection by satellite imagery. This process resulted in a manageable number of parcels to be evaluated further by local knowledge metrics such as availability and cost, existing infrastructure and municipal connections, and safety.

scholarly journals Assessing the Impact of Street-View Greenery on Fear of Neighborhood Crime in Guangzhou, China

2021 ◽  
Vol 18 (1) ◽  
pp. 311
Author(s):  
Fengrui Jing ◽  
Lin Liu ◽  
Suhong Zhou ◽  
Jiangyu Song ◽  
Linsen Wang ◽  
...  
Keyword(s):  
Social Factors ◽  
Urban Areas ◽  
Fear Of Crime ◽  
Green Space ◽  
Learning Approach ◽  
Perceived Safety ◽  
Street View ◽  
The Impact ◽  
The Relationship ◽  
Physical Incivilities

Previous literature has examined the relationship between the amount of green space and perceived safety in urban areas, but little is known about the effect of street-view neighborhood greenery on perceived neighborhood safety. Using a deep learning approach, we derived greenery from a massive set of street view images in central Guangzhou. We further tested the relationships and mechanisms between street-view greenery and fear of crime in the neighborhood. Results demonstrated that a higher level of neighborhood street-view greenery was associated with a lower fear of crime, and its relationship was mediated by perceived physical incivilities. While increasing street greenery of the micro-environment may reduce fear of crime, this paper also suggests that social factors should be considered when designing ameliorative programs.

scholarly journals Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home Charging

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 135-149
Author(s):  
James Flynn ◽  
Cinzia Giannetti
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Urban Areas ◽  
Proof Of Concept ◽  
Major Step ◽  
Dominant Form ◽  
Residential Properties ◽  
The Uk ◽  
Ev Charging ◽  
Unique Dataset

With Electric Vehicles (EV) emerging as the dominant form of green transport in the UK, it is critical that we better understand existing infrastructures in place to support the uptake of these vehicles. In this multi-disciplinary paper, we demonstrate a novel end-to-end workflow using deep learning to perform automated surveys of urban areas to identify residential properties suitable for EV charging. A unique dataset comprised of open source Google Street View images was used to train and compare three deep neural networks and represents the first attempt to classify residential driveways from streetscape imagery. We demonstrate the full system workflow on two urban areas and achieve accuracies of 87.2% and 89.3% respectively. This proof of concept demonstrates a promising new application of deep learning in the field of remote sensing, geospatial analysis, and urban planning, as well as a major step towards fully autonomous artificially intelligent surveying techniques of the built environment.

scholarly journals Meeting the Challenges of Longitudinal Cluster-Based Trials in Schools: Lessons From the Chicago Trial of Positive Action

2016 ◽  
Vol 40 (4) ◽  
pp. 450-482 ◽  
Author(s):  
Kendra M. Lewis ◽  
David L. DuBois ◽  
Peter Ji ◽  
Joseph Day ◽  
Naida Silverthorn ◽  
...  
Keyword(s):  
Public Schools ◽  
Low Income ◽  
Urban Areas ◽  
Statistical Power ◽  
Student Mobility ◽  
Controlled Trial ◽  
Lessons Learned ◽  
Study Planning ◽  
Positive Action ◽  
Cluster Randomized

We describe challenges in the 6-year longitudinal cluster randomized controlled trial (CRCT) of Positive Action (PA), a social–emotional and character development (SECD) program, conducted in 14 low-income, urban Chicago Public Schools. Challenges pertained to logistics of study planning (school recruitment, retention of schools during the trial, consent rates, assessment of student outcomes, and confidentiality), study design (randomization of a small number of schools), fidelity (implementation of PA and control condition activities), and evaluation (restricted range of outcomes, measurement invariance, statistical power, student mobility, and moderators of program effects). Strategies used to address the challenges within each of these areas are discussed. Incorporation of lessons learned from this study may help to improve future evaluations of longitudinal CRCTs, especially those that involve evaluation of school-based interventions for minority populations and urban areas.

scholarly journals Modelling the impact of megacities on local, regional and global tropospheric ozone and the deposition of nitrogen species

2013 ◽  
Vol 13 (24) ◽  
pp. 12215-12231 ◽  
Author(s):  
Z. S. Stock ◽  
M. R. Russo ◽  
T. M. Butler ◽  
A. T. Archibald ◽  
M. G. Lawrence ◽  
...  
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Climate Model ◽  
Surface Ozone ◽  
Global Scale ◽  
Chemical Environment ◽  
Ozone Production ◽  
The Uk ◽  
The Impact ◽  
Local Emissions

Abstract. We examine the effects of ozone precursor emissions from megacities on present-day air quality using the global chemistry–climate model UM-UKCA (UK Met Office Unified Model coupled to the UK Chemistry and Aerosols model). The sensitivity of megacity and regional ozone to local emissions, both from within the megacity and from surrounding regions, is important for determining air quality across many scales, which in turn is key for reducing human exposure to high levels of pollutants. We use two methods, perturbation and tagging, to quantify the impact of megacity emissions on global ozone. We also completely redistribute the anthropogenic emissions from megacities, to compare changes in local air quality going from centralised, densely populated megacities to decentralised, lower density urban areas. Focus is placed not only on how changes to megacity emissions affect regional and global NOx and O3, but also on changes to NOy deposition and to local chemical environments which are perturbed by the emission changes. The perturbation and tagging methods show broadly similar megacity impacts on total ozone, with the perturbation method underestimating the contribution partially because it perturbs the background chemical environment. The total redistribution of megacity emissions locally shifts the chemical environment towards more NOx-limited conditions in the megacities, which is more conducive to ozone production, and monthly mean surface ozone is found to increase up to 30% in megacities, depending on latitude and season. However, the displacement of emissions has little effect on the global annual ozone burden (0.12% change). Globally, megacity emissions are shown to contribute ~3% of total NOy deposition. The changes in O3, NOx and NOy deposition described here are useful for quantifying megacity impacts and for understanding the sensitivity of megacity regions to local emissions. The small global effects of the 100% redistribution carried out in this study suggest that the distribution of emissions on the local scale is unlikely to have large implications for chemistry–climate processes on the global scale.

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