City Poetry and Poets' Views of Urban Scenes

The segmentation of power lines (PLs) from aerial images is a crucial task for the safe navigation of unmanned aerial vehicles (UAVs) operating at low altitudes. Despite the advances in deep learning-based approaches for PL segmentation, these models are still vulnerable to the class imbalance present in the data. The PLs occupy only a minimal portion (1–5%) of the aerial images as compared to the background region (95–99%). Generally, this class imbalance problem is addressed via the use of PL-specific detectors in conjunction with the popular class balanced cross entropy (BBCE) loss function. However, these PL-specific detectors do not work outside their application areas and a BBCE loss requires hyperparameter tuning for class-wise weights, which is not trivial. Moreover, the BBCE loss results in low dice scores and precision values and thus, fails to achieve an optimal trade-off between dice scores, model accuracy, and precision–recall values. In this work, we propose a generalized focal loss function based on the Matthews correlation coefficient (MCC) or the Phi coefficient to address the class imbalance problem in PL segmentation while utilizing a generic deep segmentation architecture. We evaluate our loss function by improving the vanilla U-Net model with an additional convolutional auxiliary classifier head (ACU-Net) for better learning and faster model convergence. The evaluation of two PL datasets, namely the Mendeley Power Line Dataset and the Power Line Dataset of Urban Scenes (PLDU), where PLs occupy around 1% and 2% of the aerial images area, respectively, reveal that our proposed loss function outperforms the popular BBCE loss by 16% in PL dice scores on both the datasets, 19% in precision and false detection rate (FDR) values for the Mendeley PL dataset and 15% in precision and FDR values for the PLDU with a minor degradation in the accuracy and recall values. Moreover, our proposed ACU-Net outperforms the baseline vanilla U-Net for the characteristic evaluation parameters in the range of 1–10% for both the PL datasets. Thus, our proposed loss function with ACU-Net achieves an optimal trade-off for the characteristic evaluation parameters without any bells and whistles. Our code is available at Github.

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Psychological Restoration and the Effect of People in Nature and Urban Scenes: A Laboratory Experiment

Sustainability ◽

10.3390/su13116464 ◽

2021 ◽

Vol 13 (11) ◽

pp. 6464

Author(s):

Chris Neale ◽

Stephanie Lopez ◽

Jenny Roe

Keyword(s):

Outcome Measures ◽

Amazon Mechanical Turk ◽

Natural Environments ◽

Urban Scenes ◽

Social Wellbeing ◽

Environmental Interactions ◽

Social Belonging ◽

Restorative Environment ◽

Social Environmental ◽

Natural Settings

It is well-evidenced that exposure to natural environments increases psychological restoration as compared to non-natural settings, increasing our ability to recover from stress, low mood, and mental fatigue and encouraging positive social interactions that cultivate social cohesion. However, very few studies have explored how the inclusion of people within a given environment—either urban or natural settings—affect restorative health outcomes. We present three laboratory-based studies examining, first, the effect of nature vs. urban scenes, and second, investigating nature ‘with’ vs. ‘without’ people—using static and moving imagery—on psychological restoration and social wellbeing. Our third study explores differences between urban and natural settings both with vs. without people, using video stimuli to understand potential restorative and social wellbeing effects. Outcome measures across all studies included perceived social belonging, loneliness, subjective mood, and perceived restorativeness. Studies 1 and 2 both used a within group, randomized crossover design. Study 1 (n = 45, mean age = 20.7) explored static imagery of environmental conditions without people; findings were consistent with restorative theories showing a positive effect of nature exposure on all outcome measures. Study 2 compared nature scenes with vs. without people (n = 47, mean age = 20.9) and we found no significant differences on our outcome measures between either social scenario, though both scenarios generated positive wellbeing outcomes. Study 3, conducted on Amazon Mechanical Turk, employed an independent group design with subjects randomly assigned to one of four conditions; an urban vs. nature setting, with vs. without people. We explored the effect of moving imagery on psychological restoration (n = 200, mean age = 35.7) and our findings showed no impact on belonging, loneliness, or mood between conditions, but did show that—regardless of the inclusion of people—the nature settings were more restorative than the urban. There were no differences in psychological restoration between nature conditions with vs. without people. We discuss the implications for restorative environment research exploring social-environmental interactions.

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Eight-Channel Multispectral Image Database for Saliency Prediction

Sensors ◽

10.3390/s21030970 ◽

2021 ◽

Vol 21 (3) ◽

pp. 970

Author(s):

Miguel Ángel Martínez-Domingo ◽

Juan Luis Nieves ◽

Eva M. Valero

Keyword(s):

Imaging Techniques ◽

Image Database ◽

Multispectral Image ◽

Urban Scenes ◽

Saliency Maps ◽

Saliency Prediction ◽

New Models ◽

Rgb Images ◽

Salient Regions ◽

Rgb Image

Saliency prediction is a very important and challenging task within the computer vision community. Many models exist that try to predict the salient regions on a scene from its RGB image values. Several new models are developed, and spectral imaging techniques may potentially overcome the limitations found when using RGB images. However, the experimental study of such models based on spectral images is difficult because of the lack of available data to work with. This article presents the first eight-channel multispectral image database of outdoor urban scenes together with their gaze data recorded using an eyetracker over several observers performing different visualization tasks. Besides, the information from this database is used to study whether the complexity of the images has an impact on the saliency maps retrieved from the observers. Results show that more complex images do not correlate with higher differences in the saliency maps obtained.

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Model-Based Estimation of Road Direction in Urban Scenes Using Virtual LiDAR Signals

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) ◽

10.1109/smc42975.2020.9282925 ◽

2020 ◽

Author(s):

Miho Adachi ◽

Ryusuke Miyamoto

Keyword(s):

Urban Scenes ◽

Model Based

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Aesthetic preferences and the attribution of meaning: Environmental categorization processes in the evaluation of urban scenes

International Journal of Psychology ◽

10.1080/00207590444000104 ◽

2005 ◽

Vol 40 (1) ◽

pp. 19-27 ◽

Cited By ~ 33

Author(s):

Ma Paz Galindo ◽

Ma Carmen Hidalgo

Keyword(s):

Urban Scenes ◽

Aesthetic Preferences

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Perceptual Rules for Building Enhancements in 3D Virtual Worlds

i-com ◽

10.1515/icom-2017-0022 ◽

2017 ◽

Vol 16 (3) ◽

pp. 205-213 ◽

Cited By ~ 2

Author(s):

Patrick Tutzauer ◽

Susanne Becker ◽

Norbert Haala

Keyword(s):

Virtual Worlds ◽

Mobile Apps ◽

Research Question ◽

Feature Space ◽

Information Modeling ◽

Visual Appearance ◽

Urban Scenes ◽

Additional Information ◽

Building Models ◽

Building Information

AbstractWhile the generation of geometric 3D virtual models has become feasible to a great extent, the enrichment of the resulting urban building models with semantics remains an open research question in the field of geoinformation and geovisualisation. This additional information is not only valuable for applications like Building Information Modeling (BIM) but also offers possibilities to enhance the visual insight for humans when interacting with that kind of data. Depending on the application, presenting users the highest level of detail of building models is often neither the most informative nor feasible way. For example when using mobile apps, resources and display sizes are quite limited. A concrete use case is the imparting of building use types in urban scenes to users. Within our preliminary work, user studies helped to identify important features for the human ability to associate a building with its correct usage type. In this work we now embed this knowledge into building category-specific grammars to automatically modify the geometry of a building to align its visual appearance to its underlying use type. If the building category for a model is not known beforehand, we investigate its feature space and try to derive its use type from there. Within the context of this work, we developed a Virtual Reality (VR) framework that gives the user the possibility to switch between different building representation types while moving in the VR world, thus enabling us in the future to evaluate the potential and effect of the grammar-enhanced building model in an immersive environment.

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