No Perfect Outdoors: Towards a Deep Profiling of GNSS-Based Location Contexts

While both outdoor and indoor localization methods are flourishing, how to properly marry them to offer pervasive localizability in urban areas remains open. Recently, proposals on indoor–outdoor detection have made the first step towards such an integration, yet complicated urban environments render such a binary classification incompetent. Fortunately, the latest developments in Android have granted us access to raw GNSS measurements, which contain far more information than commonly derived GPS location indicators. In this paper, we explore these newly available measurements in order to better characterize diversified urban environments. Essentially, we tackle the challenges introduced by the complex GNSS data and apply a deep learning model to identify representations for respective location contexts. We further develop two preliminary applications of our deep profiling: one, we offer a more fine-grained semantic classification than binary indoor–outdoor detection; and two, we derive a GPS error indicator that is more meaningful than that provided by Google Maps. These results are all corroborated by our extensive data collection and trace-driven evaluations.

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Precise and Robust RTK-GNSS Positioning in Urban Environments with Dual-Antenna Configuration

Sensors ◽

10.3390/s19163586 ◽

2019 ◽

Vol 19 (16) ◽

pp. 3586 ◽

Cited By ~ 8

Author(s):

Fan ◽

Li ◽

Cui ◽

Keyword(s):

Urban Areas ◽

Attitude Determination ◽

Real Data ◽

Satellite System ◽

Urban Environments ◽

Multiple Antenna ◽

Gnss Positioning ◽

Single Antenna ◽

Gnss Measurements ◽

Global Navigation Satellite

Robust and centimeter-level Real-time Kinematic (RTK)-based Global Navigation Satellite System (GNSS) positioning is of paramount importance for emerging GNSS applications, such as drones and automobile systems. However, the performance of conventional single-rover RTK degrades greatly in urban environments due to signal blockage and strong multipath. The increasing use of multiple-antenna/rover configurations for attitude determination in the above precise positioning applications, just as well, allows more information involved to improve RTK positioning performance in urban areas. This paper proposes a dual-antenna constraint RTK algorithm, which combines GNSS measurements of both antennas by making use of the geometric constraint between them. By doing this, the reception diversity between two antennas can be taken advantage of to improve the availability and geometric distribution of GNSS satellites, and what is more, the redundant measurements from a second antenna help to weaken the multipath effect on the first antenna. Particularly, an Ambiguity Dilution of Precision (ADOP)-based analysis is carried out to explore the intrinsic model strength for ambiguity resolution (AR) with different kinds of constraints. Based on the results, a Dual-Antenna with baseline VEctor Constraint algorithm (RTK) is developed. The primary advantages of the reported method include: 1) Improved availability and success rate of RTK, even if neither of the two single-antenna receivers can successfully solve the AR problem; and 2) reduced computational burden by adopting the concept of measurement projection. Simulated and real data experiments are performed to demonstrate robustness and precision of the algorithm in GNSS-challenged environments.

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Diversity of organisms

10.1093/oso/9780198827238.003.0005 ◽

2018 ◽

Author(s):

Philip James

Keyword(s):

Life Cycle ◽

Urban Areas ◽

Native Species ◽

New Technologies ◽

Urban Environments ◽

Green Spaces ◽

The Common ◽

Living Organisms ◽

Micro Organisms ◽

Diversity Of Life

The focus of this chapter is an examination of the diversity of living organisms found within urban environments, both inside and outside buildings. The discussion commences with prions and viruses before moving on to consider micro-organisms, plants, and animals. Prions and viruses cause disease in plants and animals, including humans. Micro-organisms are ubiquitous and are found in great numbers throughout urban environments. New technologies are providing new insights into their diversity. Plants may be found inside buildings as well as in gardens and other green spaces. The final sections of the chapter offer a discussion of the diversity of animals that live in urban areas for part or all of their life cycle. Examples of the diversity of life in urban environments are presented throughout, including native and non-native species, those that are benign and deadly, and the common and the rare.

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CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors

Sensors ◽

10.3390/s21010052 ◽

2020 ◽

Vol 21 (1) ◽

pp. 52

Author(s):

Tianyi Zhang ◽

Abdallah El Ali ◽

Chen Wang ◽

Alan Hanjalic ◽

Pablo Cesar

Keyword(s):

Emotion Recognition ◽

Electrodermal Activity ◽

Short Form ◽

Recognition Performance ◽

Binary Classification ◽

Wearable Sensors ◽

Recognition Algorithm ◽

Fine Grained ◽

Physiological Sensors ◽

Valence And Arousal

Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion recognition algorithm (CorrNet) to recognize the valence and arousal (V-A) of each instance (fine-grained segment of signals) using only wearable, physiological signals (e.g., electrodermal activity, heart rate). CorrNet takes advantage of features both inside each instance (intra-modality features) and between different instances for the same video stimuli (correlation-based features). We first test our approach on an indoor-desktop affect dataset (CASE), and thereafter on an outdoor-mobile affect dataset (MERCA) which we collected using a smart wristband and wearable eyetracker. Results show that for subject-independent binary classification (high-low), CorrNet yields promising recognition accuracies: 76.37% and 74.03% for V-A on CASE, and 70.29% and 68.15% for V-A on MERCA. Our findings show: (1) instance segment lengths between 1–4 s result in highest recognition accuracies (2) accuracies between laboratory-grade and wearable sensors are comparable, even under low sampling rates (≤64 Hz) (3) large amounts of neutral V-A labels, an artifact of continuous affect annotation, result in varied recognition performance.

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GNSS Profile from the Greenland Korth Expeditions in the Context of Satellite Data

Applied Sciences ◽

10.3390/app11031115 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1115

Author(s):

Aleš Bezděk ◽

Jakub Kostelecký ◽

Josef Sebera ◽

Thomas Hitziger

Keyword(s):

Ice Sheet ◽

Satellite Observations ◽

Elevation Change ◽

Gradual Decline ◽

Satellite Gravimetry ◽

Surface Elevation Change ◽

Satellite Radar ◽

Gnss Measurements ◽

Gnss Data ◽

Satellite Radar Altimetry

Over the last two decades, a small group of researchers repeatedly crossed the Greenland interior skiing along a 700-km long route from east to west, acquiring precise GNSS measurements at exactly the same locations. Four such elevation profiles of the ice sheet measured in 2002, 2006, 2010 and 2015 were differenced and used to analyze the surface elevation change. Our goal is to compare such locally measured GNSS data with independent satellite observations. First, we show an agreement in the rate of elevation change between the GNSS data and satellite radar altimetry (ERS, Envisat, CryoSat-2). Both datasets agree well (2002–2015), and both correctly display local features such as an elevation increase in the central part of the ice sheet and a sharp gradual decline in the surface heights above Jakobshavn Glacier. Second, we processed satellite gravimetry data (GRACE) in order for them to be comparable with local GNSS measurements. The agreement is demonstrated by a time series at one of the measurement sites. Finally, we provide our own satellite gravimetry (GRACE, GRACE-FO, Swarm) estimate of the Greenland mass balance: first a mild decrease (2002–2007: −210 ± 29 Gt/yr), then an accelerated mass loss (2007–2012: −335 ± 29 Gt/yr), which was noticeably reduced afterwards (2012–2017: −178 ± 72 Gt/yr), and nowadays it seems to increase again (2018–2019: −278 ± 67 Gt/yr).

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Semantic segmentation of PolSAR image data using advanced deep learning model

Scientific Reports ◽

10.1038/s41598-021-94422-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rajat Garg ◽

Anil Kumar ◽

Nikunj Bansal ◽

Manish Prateek ◽

Shashi Kumar

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Deep Learning ◽

Urban Area ◽

Urban Areas ◽

Learning Algorithms ◽

Semantic Segmentation ◽

Learning Model ◽

Machine Learning Algorithms ◽

Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

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High-Resolution Modelling of Thermal Exposure during a Hot Spell: A Case Study Using PALM-4U in Prague, Czech Republic

Atmosphere ◽

10.3390/atmos12020175 ◽

2021 ◽

Vol 12 (2) ◽

pp. 175

Author(s):

Jan Geletič ◽

Michal Lehnert ◽

Pavel Krč ◽

Jaroslav Resler ◽

Eric Scott Krayenhoff

Keyword(s):

Czech Republic ◽

Urban Areas ◽

Heat Exposure ◽

Thermal Exposure ◽

Urban Environments ◽

Cooling Effect ◽

Spatiotemporal Variability ◽

Climate Index ◽

Street Canyons ◽

General Terms

The modelling of thermal exposure in outdoor urban environments is a highly topical challenge in modern climate research. This paper presents the results derived from a new micrometeorological model that employs an integrated biometeorology module to model Universal Thermal Climate Index (UTCI). This is PALM-4U, which includes an integrated human body-shape parameterization, deployed herein for a pilot domain in Prague, Czech Republic. The results highlight the key role of radiation in the spatiotemporal variability of thermal exposure in moderate-climate urban areas during summer days in terms of the way in which this directly affects thermal comfort through radiant temperature and indirectly through the complexity of turbulence in street canyons. The model simulations suggest that the highest thermal exposure may be expected within street canyons near the irradiated north sides of east–west streets and near streets oriented north–south. Heat exposure in streets increases in proximity to buildings with reflective paints. The lowest heat exposure during the day may be anticipated in tree-shaded courtyards. The cooling effect of trees may range from 4 °C to 9 °C in UTCI, and the cooling effect of grass in comparison with artificial paved surfaces in open public places may be from 2 °C to 5 °C UTCI. In general terms, this study illustrates that the PALM modelling system provides a new perspective on the spatiotemporal differentiation of thermal exposure at the pedestrian level; it may therefore contribute to more climate-sensitive urban planning.

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An Eco-Friendly Multimodal Route Guidance System for Urban Areas Using Multi-Agent Technology

Applied Sciences ◽

10.3390/app11052057 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2057

Author(s):

Abdallah Namoun ◽

Ali Tufail ◽

Nikolay Mehandjiev ◽

Ahmed Alrehaili ◽

Javad Akhlaghinia ◽

...

Keyword(s):

Traffic Flow ◽

Urban Areas ◽

Urban Environments ◽

Ease Of Use ◽

Transport Infrastructure ◽

Guidance System ◽

Planning System ◽

Flow Density ◽

Route Guidance ◽

Agent Based

The use and coordination of multiple modes of travel efficiently, although beneficial, remains an overarching challenge for urban cities. This paper implements a distributed architecture of an eco-friendly transport guidance system by employing the agent-based paradigm. The paradigm uses software agents to model and represent the complex transport infrastructure of urban environments, including roads, buses, trolleybuses, metros, trams, bicycles, and walking. The system exploits live traffic data (e.g., traffic flow, density, and CO2 emissions) collected from multiple data sources (e.g., road sensors and SCOOT) to provide multimodal route recommendations for travelers through a dedicated application. Moreover, the proposed system empowers the transport management authorities to monitor the traffic flow and conditions of a city in real-time through a dedicated web visualization. We exhibit the advantages of using different types of agents to represent the versatile nature of transport networks and realize the concept of smart transportation. Commuters are supplied with multimodal routes that endeavor to reduce travel times and transport carbon footprint. A technical simulation was executed using various parameters to demonstrate the scalability of our multimodal traffic management architecture. Subsequently, two real user trials were carried out in Nottingham (United Kingdom) and Sofia (Bulgaria) to show the practicality and ease of use of our multimodal travel information system in providing eco-friendly route guidance. Our validation results demonstrate the effectiveness of personalized multimodal route guidance in inducing a positive travel behavior change and the ability of the agent-based route planning system to scale to satisfy the requirements of traffic infrastructure in diverse urban environments.

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Facilitating fine-grained intra-urban dengue forecasting by integrating urban environments measured from street-view images

Infectious Diseases of Poverty ◽

10.1186/s40249-021-00824-5 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Kang Liu ◽

Ling Yin ◽

Meng Zhang ◽

Min Kang ◽

Ai-Ping Deng ◽

...

Keyword(s):

Machine Learning ◽

High Risk ◽

Feature Vector ◽

Urban Environments ◽

Environmental Feature ◽

Fine Grained ◽

Environmental Features ◽

Street View ◽

The City ◽

Dengue Epidemic

Abstract Background Dengue fever (DF) is a mosquito-borne infectious disease that has threatened tropical and subtropical regions in recent decades. An early and targeted warning of a dengue epidemic is important for vector control. Current studies have primarily determined weather conditions to be the main factor for dengue forecasting, thereby neglecting that environmental suitability for mosquito breeding is also an important factor, especially in fine-grained intra-urban settings. Considering that street-view images are promising for depicting physical environments, this study proposes a framework for facilitating fine-grained intra-urban dengue forecasting by integrating the urban environments measured from street-view images. Methods The dengue epidemic that occurred in 167 townships of Guangzhou City, China, between 2015 and 2019 was taken as a study case. First, feature vectors of street-view images acquired inside each township were extracted by a pre-trained convolutional neural network, and then aggregated as an environmental feature vector of the township. Thus, townships with similar physical settings would exhibit similar environmental features. Second, the environmental feature vector is combined with commonly used features (e.g., temperature, rainfall, and past case count) as inputs to machine-learning models for weekly dengue forecasting. Results The performance of machine-learning forecasting models (i.e., MLP and SVM) integrated with and without environmental features were compared. This indicates that models integrating environmental features can identify high-risk urban units across the city more precisely than those using common features alone. In addition, the top 30% of high-risk townships predicted by our proposed methods can capture approximately 50–60% of dengue cases across the city. Conclusions Incorporating local environments measured from street view images is effective in facilitating fine-grained intra-urban dengue forecasting, which is beneficial for conducting spatially precise dengue prevention and control.

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Wetland Roofs as an Attractive Option for Decentralized Water Management and Air Conditioning Enhancement in Growing Cities—A Review

Water ◽

10.3390/w11091845 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1845

Author(s):

Andreas Zehnsdorf ◽

Keani C. U. Willebrand ◽

Ralf Trabitzsch ◽

Sarah Knechtel ◽

Michael Blumberg ◽

...

Keyword(s):

Water Management ◽

Urban Areas ◽

Ground Level ◽

Urban Environments ◽

Positive Side ◽

Climate Regulation ◽

Decentralized Treatment ◽

Attractive Option ◽

Stormwater Retention ◽

Positive Side Effects

While constructed wetlands have become established for the decentralized treatment of wastewater and rainwater, wetland roofs have only been built in isolated cases up to now. The historical development of wetland roofs is described here on the basis of a survey of literature and patents, and the increasing interest in this ecotechnology around the world is presented. In particular, this article describes the potential for using wetland roofs and examines experience with applications in decentralized water management in urban environments and for climate regulation in buildings. Wetland roofs are suitable as a green-blue technology for the future—particularly in cities with an acute shortage of unoccupied ground-level sites—for the decentralized treatment of wastewater streams of various origins. Positive “side effects” such as nearly complete stormwater retention and the improvement of climates in buildings and their surroundings, coupled with an increase in biodiversity, make wetland roofs an ideal multi-functional technology for urban areas.

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Biomimetic Urban and Architectural Design: Illustrating and Leveraging Relationships between Ecosystem Services

Biomimetics ◽

10.3390/biomimetics6010002 ◽

2020 ◽

Vol 6 (1) ◽

pp. 2

Author(s):

Maibritt Pedersen Zari

Keyword(s):

Ecosystem Services ◽

Urban Design ◽

Urban Areas ◽

Architectural Design ◽

Urban Environments ◽

Published Data ◽

Ecological Knowledge ◽

Policy Makers ◽

Spatial Design ◽

Key Barrier

Redesigning and retrofitting cities so they become complex systems that create ecological and cultural–societal health through the provision of ecosystem services is of critical importance. Although a handful of methodologies and frameworks for considering how to design urban environments so that they provide ecosystem services have been proposed, their use is not widespread. A key barrier to their development has been identified as a lack of ecological knowledge about relationships between ecosystem services, which is then translated into the field of spatial design. In response, this paper examines recently published data concerning synergetic and conflicting relationships between ecosystem services from the field of ecology and then synthesises, translates, and illustrates this information for an architectural and urban design context. The intention of the diagrams created in this research is to enable designers and policy makers to make better decisions about how to effectively increase the provision of various ecosystem services in urban areas without causing unanticipated degradation in others. The results indicate that although targets of ecosystem services can be both spatially and metrically quantifiable while working across different scales, their effectiveness can be increased if relationships between them are considered during design phases of project development.

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