Analysis of facade solutions as an alternative to demolition for architectures with visual impact in historical urban scenes

Considerable effort is expended measuring pasture parameters that relate to animal perform mance. Traditionally farmers have used visual impact and experience as their method of pasture assessment to make management decisions. The simplest measurement collected by researchers, pasture height, is developed to the point where it can be used as a guide to predict pasture mass and animal performance. This paper presents relationships between height and mass for improved pastures in summer dry North Island hill country. A pasture 5cm tall will give hogget growth of 60 gjday in summer, 90 in autumn, 100 in winter and 200 in spring. Similarly 5cm will be 2250 kg DM/ha in summer, 1900 in autumn, 1520 in winter, 1640 in spring and 2200 in late spring. Keywords: Height, mass, predictor, grazing, quality, animal performance, feed levels, parameters, pasture.

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VISUAL IMPACT ASSESSMENT OF COLOUR AND SCALE OF BUILDINGS ON THE RURAL LANDSCAPE

Environmental Engineering and Management Journal ◽

10.30638/eemj.2016.166 ◽

2016 ◽

Vol 15 (7) ◽

pp. 1537-1550 ◽

Cited By ~ 8

Author(s):

Maria Jesus Montero-Parejo ◽

Lorenzo Garcia-Moruno ◽

Sergio Lopez-Casares ◽

Julio Hernandez-Blanco

Keyword(s):

Impact Assessment ◽

Rural Landscape ◽

Visual Impact ◽

Visual Impact Assessment

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Cross-Domain Semantic Segmentation of Urban Scenes via Multi-Level Feature Alignment

2020 25th International Conference on Pattern Recognition (ICPR) ◽

10.1109/icpr48806.2021.9411915 ◽

2021 ◽

Author(s):

Bin Zhang ◽

Shengjie Zhao ◽

Rongqing Zhang

Keyword(s):

Semantic Segmentation ◽

Urban Scenes ◽

Cross Domain ◽

Multi Level ◽

Feature Alignment

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The case for skin camouflage in the management of upper limb scarring – A case series

Hand Therapy ◽

10.1177/17589983211007875 ◽

2021 ◽

pp. 175899832110078

Author(s):

Laura Adamson ◽

Anna Selby

Keyword(s):

Upper Limb ◽

Case Series ◽

Primary Objective ◽

Self Confidence ◽

Published Evidence ◽

Case Series Study ◽

Visual Impact ◽

Series Study ◽

Increasing Function

Introduction For some people the appearance of their hands is as important as function. Upper limb scarring can cause some patients distress. Skin camouflage is an intervention that can be used to reduce the visual impact of a scar but there is limited published evidence for its use for hand scarring. Methods This is a case series study with a primary objective to determine whether skin camouflage reduces distress in patients with an upper limb scar and to evaluate this new service. Patients experiencing distress from an upper limb scar were recruited from a hand therapy outpatient clinic. The intervention delivered was a one hour skin camouflage session. Photographs of the upper limb pre and post skin camouflage intervention were taken. The patient-rated Michigan Hand Questionnaire (MHQ) and Derriford Appearance Scale (DAS24) were completed before treatment, at 1 week and 1 month after treatment. Results Six participants reporting distress from an upper limb scar received skin camouflage intervention. Only three out of six participants completed all follow-up. All three showed improvement in at least two domains of the MHQ (function and aesthetics) at one month post treatment. Increased confidence during functional and work-based activities was also reported on the DAS24. Participants reported increased engagement in daily activities as a result of being able to camouflage their scars. Conclusions This small case series shows that skin camouflage intervention may be beneficial for some patients who are experiencing distress related to an upper limb scar by increasing function and self-confidence.

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A Novel Focal Phi Loss for Power Line Segmentation with Auxiliary Classifier U-Net

Sensors ◽

10.3390/s21082803 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2803

Author(s):

Rabeea Jaffari ◽

Manzoor Ahmed Hashmani ◽

Constantino Carlos Reyes-Aldasoro

Keyword(s):

Loss Function ◽

Class Imbalance ◽

Power Line ◽

Aerial Images ◽

Class Imbalance Problem ◽

Trade Off ◽

Urban Scenes ◽

Imbalance Problem ◽

A Minor ◽

Evaluation Parameters

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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Human–environment interactions at a short-lived Arctic mine and the long-term response of the local tundra vegetation

Polar Record ◽

10.1017/s0032247420000418 ◽

2021 ◽

Vol 57 ◽

Author(s):

Frigga Kruse ◽

Gary R. Nobles ◽

Martha de Jong ◽

Rosanne M. K. van Bodegom ◽

G. J. M. (Gert) van Oortmerssen ◽

...

Keyword(s):

Isotope Analysis ◽

Isotopic Analysis ◽

The Arctic ◽

Human Environment ◽

Habitat Creation ◽

Waste Dumps ◽

Threatened Plant Species ◽

Visual Impact ◽

Local Ecosystem

Abstract Arctic mining has a bad reputation because the extractive industry is often responsible for a suite of environmental problems. Yet, few studies explore the gap between untouched tundra and messy megaproject from a historical perspective. Our paper focuses on Advent City as a case study of the emergence of coal mining in Svalbard (Norway) coupled with the onset of mining-related environmental change. After short but intensive human activity (1904–1908), the ecosystem had a century to respond, and we observe a lasting impact on the flora in particular. With interdisciplinary contributions from historical archaeology, archaeozoology, archaeobotany and botany, supplemented by stable isotope analysis, we examine 1) which human activities initially asserted pressure on the Arctic environment, 2) whether the miners at Advent City were “eco-conscious,” for example whether they showed concern for the environment and 3) how the local ecosystem reacted after mine closure and site abandonment. Among the remains of typical mining infrastructure, we prioritised localities that revealed the subtleties of long-term anthropogenic impact. Significant pressure resulted from landscape modifications, the import of non-native animals and plants, hunting and fowling, and the indiscriminate disposal of waste material. Where it was possible to identify individual inhabitants, these shared an economic attitude of waste not, want not, but they did not hold the environment in high regard. Ground clearances, animal dung and waste dumps continue to have an effect after a hundred years. The anthropogenic interference with the fell field led to habitat creation, especially for vascular plants. The vegetation cover and biodiversity were high, but we recorded no exotic or threatened plant species. Impacted localities generally showed a reduction of the natural patchiness of plant communities, and highly eutrophic conditions were unsuitable for liverworts and lichens. Supplementary isotopic analysis of animal bones added data to the marine reservoir offset in Svalbard underlining the far-reaching potential of our multi-proxy approach. We conclude that although damaging human–environment interactions formerly took place at Advent City, these were limited and primarily left the visual impact of the ruins. The fell field is such a dynamic area that the subtle anthropogenic effects on the local tundra may soon be lost. The fauna and flora may not recover to what they were before the miners arrived, but they will continue to respond to new post-industrial circumstances.

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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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