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Urban Information Sensing for Sustainable Development

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (9 June 2023) | Viewed by 7273

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Dear Colleagues,

Cities are complex by nature, with high-dimensional information in the built environment, natural environment, people’s interaction, transportation system, and infrastructure utilization. The collection and mining of sensor-based urban information represent a new direction for planners and governments to help to achieve ambitious sustainability goals. However, the challenges that remain in accurately detecting and comprehensively integrating these different layers of cities and their interactions with one another rely on deep learning and computer vision to be tackled.

The aim of this Special Issue is to collect high-quality research articles and review papers on urban information sensing for sustainable development. Developments that improve the performance and efficiency of large-scale urban information autodetection, machine learning, and neural network approaches to further process the information and sensor-based framework for integrating high-dimensional information for urban sustainable development goals are also welcome.

Dr. Haoran Zhang
Dr. Yinqiang Zheng
Dr. Zhiling Guo
Guest Editors

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Keywords

Computer vision in built environment
Sensing urban natural environment changes
Advances in urban people interaction detection
Sensor fusion for complex transportation system
Dynamic monitering urban infrastructure utilization
Urban information intergration for sustainable development

Published Papers (2 papers)

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Research

18 pages, 30540 KiB

Open AccessArticle

Extracting Rectified Building Footprints from Traditional Orthophotos: A New Workflow

by Qi Chen, Yuanyi Zhang, Xinyuan Li and Pengjie Tao

Sensors 2022, 22(1), 207; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010207 - 29 Dec 2021

Cited by 3 | Viewed by 2638

Abstract

Deep learning techniques such as convolutional neural networks have largely improved the performance of building segmentation from remote sensing images. However, the images for building segmentation are often in the form of traditional orthophotos, where the relief displacement would cause non-negligible misalignment between the roof outline and the footprint of a building; such misalignment poses considerable challenges for extracting accurate building footprints, especially for high-rise buildings. Aiming at alleviating this problem, a new workflow is proposed for generating rectified building footprints from traditional orthophotos. We first use the facade labels, which are prepared efficiently at low cost, along with the roof labels to train a semantic segmentation network. Then, the well-trained network, which employs the state-of-the-art version of EfficientNet as backbone, extracts the roof segments and the facade segments of buildings from the input image. Finally, after clustering the classified pixels into instance-level building objects and tracing out the roof outlines, an energy function is proposed to drive the roof outline to maximally align with the building footprint; thus, the rectified footprints can be generated. The experiments on the aerial orthophotos covering a high-density residential area in Shanghai demonstrate that the proposed workflow can generate obviously more accurate building footprints than the baseline methods, especially for high-rise buildings. Full article

(This article belongs to the Special Issue Urban Information Sensing for Sustainable Development)

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22 pages, 14653 KiB

Open AccessArticle

Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World

by A. K. M. Mahbubur Rahman, Moinul Zaber, Qianwei Cheng, Abu Bakar Siddik Nayem, Anis Sarker, Ovi Paul and Ryosuke Shibasaki

Sensors 2021, 21(22), 7469; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227469 - 10 Nov 2021

Cited by 3 | Viewed by 3447

Abstract

This paper shows the efficacy of a novel urban categorization framework based on deep learning, and a novel categorization method customized for cities in the global south. The proposed categorization method assesses urban space broadly on two dimensions—the states of urbanization and the architectural form of the units observed. This paper shows how the sixteen sub-categories can be used by state-of-the-art deep learning modules (fully convolutional network FCN-8, U-Net, and DeepLabv3+) to categorize formal and informal urban areas in seven urban cities in the developing world—Dhaka, Nairobi, Jakarta, Guangzhou, Mumbai, Cairo, and Lima. Firstly, an expert visually annotated and categorized 50 × 50 km Google Earth images of the cities. Each urban space was divided into four socioeconomic categories: (1) highly informal area; (2) moderately informal area; (3) moderately formal area, and (4) highly formal area. Then, three models mentioned above were used to categorize urban spaces. Image encompassing

70 %

of the urban space was used to train the models, and the remaining

30 %

was used for testing and validation of each city. The DeepLabv3+ model can segment the test part with an average accuracy of

90.0 %

for Dhaka,

91.5 %

for Nairobi,

94.75 %

for Jakarta,

82.0 %

for Guangzhou city,

94.25 %

for Mumbai,

91.75 %

for Cairo, and

96.75 %

for Lima. These results are the best for the DeepLabv3+ model among all. Thus, DeepLabv3+ shows an overall high accuracy level for most of the measuring parameters for all cities, making it highly scalable, readily usable to understand the cities’ current conditions, forecast land use growth, and other computational modeling tasks. Therefore, the proposed categorization method is also suited for real-time socioeconomic comparative analysis among cities, making it an essential tool for the policymakers to plan future sustainable urban spaces. Full article

(This article belongs to the Special Issue Urban Information Sensing for Sustainable Development)

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