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Quantum–Classical Image Processing for Scene Classification
پردازش تصویر کوانتومی کلاسیک برای طبقه بندی صحنه-2022 Deep-learning-based convolutional neural network (CNN) models are prominent in processing and analyzing
sensor signal data, such as images for classification. Data augmentation is a powerful technique used in training
such models to avoid overfitting and to improve accuracy. This letter proposes a data augmentation technique using
a quantum circuit for image data. The proposed quantum circuit is suitable to implement on real hardware provided by
the IBM Quantum Experience platform. In comparison with other classical data augmentation techniques, the proposed
technique increased the prediction accuracy of the CNN from 68.65 to 76.03%. However, CNN models for image
classification use many parameters during the training process. Quantum computers can efficiently handle large-scale
data inputs using qubits for information processing. Hence, we also propose a hybrid quantum–classical convolutional
neural network model (HQCNN) for scene classification. The proposed model uses a combination of CNN layers and
quantum layers to process images. The proposed HQCNN reduces parameters used for training due to the use of quantum
layers in the model. Our experimental results show that the proposed HQCNN can classify the scenes in the UC Merced
land-use dataset with an accuracy of 85.28% compared to the other models.
Index Terms—Sensor signal processing | hybrid model | quantum–classical computing | scene classification | sensor signal processing. |
مقاله انگلیسی |
2 |
Lessons learned from development of natural capital accounts in the United States and European Union
درس های آموخته شده از توسعه حساب های سرمایه طبیعی در ایالات متحده و اتحادیه اروپا-2021 The United States and European Union (EU) face common challenges in managing natural capital and balancing
conservation and resource use with consumption of other forms of capital. This paper synthesizes findings from
11 individual application papers from a special issue of Ecosystem Services on natural capital accounting (NCA)
and their application to the public and private sectors in the EU and U.S. NCA is inherently a data-integration
centered exercise, aiming to draw new insights by realigning environmental and economic data into a consis-
tent framework. Drawing primarily on papers from the special issue and other key NCA literature, we identify
lessons learned and gaps remaining for NCA’s development and application to decision making. In doing so, we
identify eight key similarities and three major differences in NCA development, status, and application between
the U.S. and EU. NCA can be highly policy relevant: special issue papers address critical issues including agri-
culture, water, conservation/land-use planning, climate, and corporate decision making. In both the U.S. and EU,
further application is needed to drive demand for the accounts’ production. Based on these experiences, the U.S.
and EU can be important leaders in cross-sector, international collaboration toward next-generation environ-
mental economic accounts that advance global NCA practice. keywords: حسابداری طبیعی سرمایه | حسابداری بخش خصوصی | سیستم حسابداری محیطی-اقتصادی- | ING (رادیو) | چارچوب مرکزی Seea | حسابداری اکوسیستم Seea | Natural capital accounting | Private-sector accounting | System of Environmental-Economic Account- | ing (SEEA) | SEEA Central Framework | SEEA Ecosystem Accounting |
مقاله انگلیسی |
3 |
Identifying regionalized co-variate driving factors to assess spatial distributions of saturated soil hydraulic conductivity using multivariate and state-space analyses
شناسایی عوامل محرک متغیر منطقه ای برای ارزیابی توزیع مکانی هدایت هیدرولیکی خاک اشباع شده با استفاده از تجزیه و تحلیل چند متغیره فضای دولت-2020 Saturated soil hydraulic conductivity (Ksat) is a key factor in hydrological management projects and its variability
along the landscape hinders its correct use in the formulation of such projects. Ksat varies under different
climatic and hydrological conditions at spatial scales as reported in several studies. However, co-regionalization
of Ksat remains a challenging aspect with regard to identifying supportive co-variates and suitable spatial
models. The objectives of this study were to (i) identify factors that relate Ksat with soil and topographic attributes
and land-use systems along a 15-km transect using principal component analysis, and (ii) describe the
spatial continuum of Ksat across the transect through co-regionalization with autoregressive state-space models.
The transect was established in the Fragata River Watershed (FRW), Southern Brazil. One hundred soil sampling
points were distributed along the transect at equal distances (150 m). Clay and sand fractions, soil organic
carbon content, soil bulk density, soil macroporosity, Ksat, and the soil water retention curve were determined
for the 0–20 cm layer at each point. Topographic attributes were derived from the digital elevation model and a
land-use map was derived from satellite images. The highest and lowest spatial variabilities were exhibited by
Ksat and soil organic carbon content, respectively. Applying the state-space approach, spatial relationships
among Ksat and soil and topographic attributes, and land-use systems along the transect, could be found.
Principal component analysis used jointly with state-space showed that macroporosity could be used as a proxy
to estimate the spatial variation of Ksat in the FRW watershed, assessing surface and subsurface runoff potentials
at areas of different land-use. Further studies should be carried out to investigate the use of the type of land-use
system as a soil structural predictor of the spatial variations of Ksat at the watershed scale since it is nowadays an
“easy-to-measure” variable from satellite images. Keywords: Ksat | Soil and topographic attributes | Spatial variability | Land-use system |
مقاله انگلیسی |
4 |
Restoration of calcareous grasslands: The early successional stage promotes biodiversity
ترمیم مراتع آهکی: مرحله اول موفقیت باعث تنوع زیستی می شود-2020 Land-use change has been identified as the most important factor responsible for the recent loss of biodiversity.
One major problem is the abandonment of management, especially in semi-natural grassland ecosystems.
Numerous restoration projects were, therefore, launched to counteract this development. However, the effects of
restoration are not yet fully understood. Especially the early successional stage, i.e. the composition of the
vegetation in the first years after the restoration measures, has received little attention probably due to its
supposedly low conservation value.
As study area, we selected the largest area of calcareous grasslands at the northern edge of the German
uplands. About 35 ha of formerly abandoned calcareous grasslands have been restored by cutting shrubs here in
the last eight years. Within the restored sites, 50 randomly chosen vegetation surveys were made and the results
were compared to 50 control plots.
Our study revealed that the value of the early successional stage for biodiversity conservation was previously
underestimated. Even though the target state – calcareous grassland – is far from being reached, the early
successional stage enhances the conservation value of calcareous grasslands by (i) increasing diversity at the
landscape scale, (ii) hosting numerous target species as well as (iii) contributing to a higher habitat quality and
heterogeneity.
Future restoration of calcareous grasslands should focus on sites with low nutrient content of the soil, a
shallow topsoil, and a warm microclimate. At such sites, the chances are greatest that species with a high nature
conservation value, i.e. characteristic species of calcareous grasslands as well as thermophilous fringe and
ruderal species, will re-establish. Keywords: Habitat quality | Heterogeneity | Host plant | Pollen source | Ruderal vegetation | Species richness |
مقاله انگلیسی |
5 |
Mapping spatio-temporal patterns and detecting the factors of traffic congestion with multi-source data fusion and mining techniques
نقشه برداری از الگوهای مکانی-زمانی و تشخیص عوامل ازدحام ترافیک با تکنیک های تلفیق داده ها و استخراج داده های چند منبع-2019 The study focuses on mapping spatiotemporal patterns and detecting the potential drivers of traffic congestion
with multi-source data. First, based on real-time traffic data retrieved from an online map, the k-means clustering
algorithm was applied to classify the spatiotemporal distribution of congested roads. Then, we applied a
geographical detector (Geo-detector) to mine the potential factors for each spatiotemporal pattern. The results
showed six congestion patterns for intra-regional roads and inter-regional roads on weekdays. On both intraregional
and inter-regional roads, congestion density reflected by building height was the strongest indicator
during the morning peak period. Public facilities such as hospitals, tourist sites and green spaces located near
areas of employment or residential areas contributed to congestion during and off-peak hours. On intra-regional
roads, the sparse road network and greater distance from the city center contribute to congestion during peak
hours. On inter-regional roads, the number of bus stops contributed most to the early evening peak congestion,
while the design of the entrances to large buildings in mixed business areas and public service areas increased
the level of congestion. The results suggest that land use should be more mixed in high-density areas as this
would reduce the number of trips made to the city center. However, mixed land-use planning should also be
combined with a detailed design of the microenvironment to improve accessibility for different travel modes in
order to increase the efficiency of traffic and reduce congestion. The innovative approach can be potentially
applied in traffic congestion and land use planning studies elsewhere based on real-time multi-source data. Keywords: Traffic congestion | Land use | Spatiotemporal pattern | Multi-source data |
مقاله انگلیسی |
6 |
A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area
یک رویکرد شبکه عصبی یادگیری عمیق برای پیش بینی حساسیت به سیل سریع: یک مطالعه موردی در یک منطقه طوفان گرمسیری با فرکانس بالا-2019 This research proposes and evaluates a new approach for flash flood susceptibility mapping
based on Deep Learning Neural Network (DLNN)) algorithm, with a case study at a highfrequency
tropical storm area in the northwest mountainous region of Vietnam. Accordingly, a
DLNN structure with 192 neurons in 3 hidden layers was proposed to construct an inference
model that predicts different levels of susceptibility to flash flood. The Rectified Linear Unit
(ReLU) and the sigmoid were selected as the activate function and the transfer function,
respectively, whereas the Adaptive moment estimation (Adam) was used to update and
optimize the weights of the DLNN. A database for the study area, which includes factors of
elevation, slope, curvature, aspect, stream density, NDVI, soil type, lithology, and rainfall, was
established to train and validate the proposed model. Feature selection was carried out for these
factors using the Information gain ratio. The results show that the DLNN attains a good
prediction accuracy with Classification Accuracy Rate = 92.05%, Positive Predictive Value =
94.55% and Negative Predictive Value = 89.55%. Compared to benchmarks, Multilayer
Perceptron Neural Network and Support Vector Machine, the DLNN performs better; therefore,
it could be concluded that the proposed hybridization of GIS and deep learning can be a
promising tool to assist the government authorities and involving parties in flash flood
mitigation and land-use planning. Keywords: Flash flood | Deep Learning | Adaptive Moment Estimation | Geographic Information System (GIS) | Vietnam |
مقاله انگلیسی |
7 |
Variation and changes in land-use intensities behind nickel mining: Coupling operational and satellite data
تغییرات و تغییرات در میزان بهره برداری از زمین در معادن نیکل: اتصال داده های عملیاتی و ماهواره ای-2018 Case studies of nickel mines in New Caledonia revealed significant differences among mining sites even though
the mines host the same type of nickel laterite ore deposit and employ the same open-cut mining method. The
intensity of land use change was evaluated as the area of land use change per unit of metal contained in extracted
ores. Among the six mines studied, the lowest intensity of 0.00177 [m2/kg] is very close to the reference value of
0.0018 [m2/kg] provided by the nickel industry, whereas the highest-impact mine has an intensity approxi
mately ten times greater, at 0.0191 [m2/kg]. This wide variation is attributed to the different operational stage of
each mine. It means careful and continuous monitoring of such changes is necessary. We also evaluated his
torical changes in land use intensity, and found a decreasing trend, which may be the result of technological
developments in the downstream sector. Our results show that the use of a single, representative intensity value
can be misleading. Instead, it is necessary to analyze mine-specific changes. Historical data are also useful for
analyzing the impacts of technological improvements over time. Both forms of analysis are feasible by coupling
satellite imagery with operational data.
Keywords: Land use change ، Site-by-site differences ، Satellite image analysis ، New Caledonia ، Nickel ore mining |
مقاله انگلیسی |
8 |
Big data and urban system model - Substitutes or complements? A case study of modelling commuting patterns in Beijing
داده های بزرگ و مدل سیستم شهری - جایگزین یا تکمیل؟ مطالعه موردی مدل سازی الگوهای رفت و آمد در پکن-2018 The emergence of urban big data is transforming the existing research paradigms in urban studies. New theories
and analytical methods are required to meet the methodological challenges. This paper empirically compares a
data-driven approach and an urban-system-model approach through a case study of modelling the commuting
patterns in Beijing. For the data-driven approach, the novel location-based-services (LBS) data are explored to
identify the employment-residence location of the service users. For the modelling approach, a spatial equili
brium model is calibrated for base year 2010 and is used to simulate the commuting patterns for Beijing 2015
based on exogenous development projections. The results of the two approaches are then compared against the
benchmark statistics for Beijing 2015. The comparison shows that the LBS data perform better in detecting
residence locations than employment locations. The model prediction fits better with the benchmark, while the
errors of the LBS data tend to vary significantly across space. For amplifying the LBS sample data to represent the
full population, uniform scale factor thus should be avoided. In addition, the ineffectiveness of representing
short-distance commuting for the LBS data is revealed by the comparison with the model predicted flows. In light
of the strength and weakness of the respective approach, the prospect of a collaborative use of big data and
urban system models is explored in the conclusion.
Keywords: Big data ، Spatial equilibrium ، Land-use and transport model ، Commuting |
مقاله انگلیسی |
9 |
Implicit and explicit knowledge in flood evacuations with a case study of Takamatsu, Japan
دانش ضمنی و آشکار در تخلیه سیل با یک مطالعه موردی ازتوکاماتسو ژاپن-2018 Preparedness of communities to natural disasters is a key to mitigating more immediate impacts, whilst im
proving social resilience for longer-term recovery. The neighbourhood-level implicit knowledge and its asso
ciation with residents’ awareness, preparedness and reaction to disasters remain imperfectly understood in the
literature. A multi-disciplinary research perspective is taken in this research to enhance the understanding of the
role of implicit knowledge in disaster management. The methodology is based on a literature review and de
scriptive analysis of knowledge management, communities of practice, explicit and implicit knowledge and
evacuation behaviour. A qualitative interview on implicit knowledge was designed and administered to selected
community members in the Japanese city of Takamatsu where typhoons are common and coastal flooding
prevalent, as demonstrated by our historical analysis from the 17th century onwards. After reviewing the current
City Disaster Management Plans, we argue that both explicit and implicit information is needed to formulate
more effective, local-area evacuation plans and that the land-use planning profession in Japan has an important
role in disaster mitigation. Practical implications and future research directions are identified in concluding the
paper.
Keywords: Typhoons and flooding ، Knowledge management ، Communities of Practice ، Japan ، Qualitative interviews ، Disaster management planning |
مقاله انگلیسی |
10 |
Land-Use Degree and Spatial Autocorrelation Analysis in Kunming City Based on Big Data
کاربرد زمین شناسی و تجزیه و تحلیل خودکار فضایی در شهر کونمینگ بر اساس داده های بزرگ-2018 This paper applies the theory of spatial correlation, basing on big data of ENVI remote sensing image interpretation and the land use data of 14 counties in Kunming, to analyze the spatial autocorrelation of land use structure and land use degree in Kunming in 2005, 2011 and 2015 with Moran Index I. The research shows: (1) The share of cultivated land and vegetation coverage in the land use structure of Kunming are large, but the area of cultivated land is decreasing while the area of construction land is increasing. (2)There is a global spatial autocorrelation of land use in Kunming, but the correlation is weakening. (3)There is local spatial autocorrelation clustering in land use degree in Kunming, including four types: high - high agglomerations(HH), high - low agglomerations(LL), low - low agglomerations, and low - high agglomerations(LH). But the agglomerations are weakening.
Keywords : remote sensing interpretation, land use degree, spatial autocorrelation,Kunming city, big data |
مقاله انگلیسی |