 Subjects -> AERONAUTICS AND SPACE FLIGHT (Total: 124 journals)
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- Deep learning-based framework for vegetation hazard monitoring near
powerlines-
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Abstract: Abstract The increasing popularity of drones has led to their adoption by electric utility companies to monitor intrusive vegetation near powerlines. The study proposes a deep learning-based detection framework compatible with drones for monitoring vegetation encroachment near powerlines which estimates vegetation health and detects powerlines. Aerial image pairs from a drone camera and a commercial-grade multispectral sensor were captured and processed into training and validation datasets which were used to train a Generative Adversarial Network (Pix2Pix model) and a Convolutional Neural Network (YoLov5 model). The Pix2Pix model generated satisfactory synthetic image translations from coloured images to Look-Up Table (LUT) maps whiles the YoLov5 obtained good performance for detecting powerlines in aerial images with precision, recall, mean Average Precision (mAP) @0.5, and mAP0.5:0.95 values are 0.82, 0.76, 0.79 and 0.56 respectively. The proposed vegetation detection framework was able to detect locations of powerlines and generate NDVI estimates represented as LUT maps directly from RGB images captured from aerial images which could serve as a preliminary and affordable alternative to relatively expensive multispectral sensors which are not readily available in developing countries for monitoring and managing the presence and health of trees and dense vegetation within powerline corridors.
PubDate: 2023-03-20
- Examining the relationship between socioeconomic structure and urban
transport network efficiency: a circuity and spatial statistics based
approach-
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Abstract: Abstract Access to urban public transportation services is crucial for all city residents. Undoubtedly, more efficient public transportation services should be provided for the needy ones. The study aims to develop a simple yet efficient analytical approach to spatially determine the urban areas that receive inefficient public transportation services. In this study, the spatial distribution of the efficiency of public transportation and its relation to socioeconomic variables (per capita income level and population density) are examined at the neighborhood in Izmir, Turkey, level using circuity. The results from univariate and bivariate Global and Local Moran’s I analyses reveal that the high-efficiency levels are spatially clustered, Higher-income neighborhoods have better public transportation systems compared to lower-income ones. According to Bivariate Local Moran’s I analyses, among the 348 neighborhoods at least 31 and at most 81 neighborhoods are either in a High-High or Low-Low cluster for the four time periods considered. As for the relationship between circuity and density, at least 21 and at most 75 neighborhoods are a part of a cluster. The fact that there is a significant relationship between the efficiency of public transportation and socioeconomic variables calls for alteration in the planning policies regarding urban public transportation supply. Although the variation in public transport efficiency levels across the neighborhoods can partly be attributed to physical conditions, the city should provide equal accessibility and efficiency regardless of the socio-economic status of the neighborhoods. The findings can well be generalized for cities of similar sizes in developing countries.
PubDate: 2023-03-17
- Estimating thickness of Zemu glacier, Sikkim (India) using ice-flow
velocity approach: a geoinformatics based perspective-
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Abstract: Abstract In the present scenario of warming climate, overall health of the glaciers along with sea level rise/fall are directly impacted by glacial dynamics. However due to inaccessible high altitude regions and devastating climate, the in-situ observations are hindered via field excursions. The present study incorporated usability of geographical information system based ice-flow velocity approach using glacier surface velocity and slope for estimating thickness of Zemu glacier in Sikkim. The study revealed thickness of 80 ± 9.6 m to 160 ± 19.2 m near snout followed by 240 ± 28.8 m to 320 ± 38.4 m in upper reaches of accumulation zone of Zemu glacier. However due to gentle slope, thickness ranged between 320 ± 38.4 m and > 400 ± 49.2 m (~ 418 ± 50.16 m) was observed in the central trunk or middle reaches of the glacier. An uncertainty of 12% was observed while calculation the glacier thickness. Relationship between glacier velocity and depth has also been established which has shown inverse characteristics due to variability of bed topography and drag effects. Proper validation of results for each study with existing field observations and literatures depicted the utility and correctness of the present study via satellite based observations.
PubDate: 2023-03-15
- Short-term exposure to air pollution and COVID-19 in India:
spatio-temporal analysis of relative risk from 20 metropolitan cities-
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Abstract: Abstract The present study investigates the possible association between major air pollutants and COVID-19. We hypothesized that the post-lockdown surge in air pollution is the major cause of the increment in COVID-19 cases and deaths. The statistical results showed that pollutant concentrations of PM2.5 (20%), PM10(24%), SO2 (12%), and O3 (19%) were raised. So, we attempted to quantify the relative risk due to all major air pollutants by fitting generalized additive models. The results suggest that the pollution concentration escalated the COVID-19 cases and deaths. The pooled study suggests that for every 10 μg/m3 increment in pollutant concentration, an increment of COVID-19 cases is observed for PM2.5 (3%), PM10 (1%), SO2 (7.7%), and O3 (10%). Similarly, there is an increment in COVID-19 deaths for PM2.5 (2.8%), PM10 (1%), SO2 (4.5%), and O3 (7.2%). The spatial maps of relative risk revealed the most vulnerable regions due to each pollutant, thus steering the policymakers to implement region-specific mitigation strategies.
PubDate: 2023-03-14
- Leveraging GIS to deploy demand-driven public charging infrastructure in
an Indian Metropolitan city-
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Abstract: Abstract The rise of electric vehicle (EV) usage in India is hindered by insufficient public charging infrastructure, which is considered a key hindrance to widespread EV adoption. To overcome this, it is essential to deploy public charging infrastructure in a demand-driven approach to meet the needs of EV users. This study provides a framework for developing a public charging infrastructure plan using geographic information systems, where demand for public charging is calculated based on ten indirect parameters such as population density, petrol stations, public spaces, and others. The proposed methodology was demonstrated through a case study in the city of Surat, where geospatial data was used to determine local charging demand. The analysis results produced a public charging demand map for 2025, which showed high EV charging demand in the core city region and along key transport corridors. The study highlights the need for policy interventions to effectively utilize installed charging infrastructure. The geospatial analysis is a useful tool for determining optimal locations for public charging infrastructure deployment, which can help decision-makers use the city's resources optimally.
PubDate: 2023-03-14
- A sea lion optimized microstrip patch antenna for enhancing the energy
efficiency of the WSN-
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Abstract: Abstract There is a growing trend toward the use of intelligent Wireless Sensor Networks for a variety of applications (WSNs). One of the main difficulties with WSNs is energy efficiency (EE). For energy saving, the directional antennas (Das) utilization is radiated in a specified direction but they reduce beam width by focusing power in that direction. Consequently, for enhancing EE and the longevity of the network, a low-powered double T slotted Micro Strip Patch Antenna (MSPA) was proposed in this paper. In the ‘4’ phases, the antenna is designed. Along with this, a new directional ‘2’ T-shaped MSPA with the aim of enhancing EE is designed. The initial parameters of the designed antenna are fed into the Sea Lion Optimization Algorithm to acquire an optimal parameter for obtaining a better frequency range. The proposed model attains the return loss of −19.2db, a gain of 2.5db, and directivity of 5.79db when contrasted with prevailing models. By achieving this, the paper exhibits the developed system’s superiority and could be a well-energy-efficient antenna in WSN. To encompass building an optimal antenna for WSNs, the work can be enlarged in the future by utilizing meta-material structure gain.
PubDate: 2023-02-27
- Exploring LULC changes in Pakhal Lake area, Telangana, India using QGIS
MOLUSCE plugin-
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Abstract: Abstract Dynamic processes such as environmental, economic, and social factors influence land use and land cover (LULC) changes, with temporal and spatial variations. This study aims to identify changes in LULC and predict future trends in the Pakhal Lake area in Peninsular India. Satellite images for the years from 2016 to 2022 were used for LULC classification using deep learning with Sentinel − 2 imagery in Google Earth Engine (GEE). Dynamic World dataset is used to classify the LULC changes of the study area with a 10 m near-real-time dataset. Images were classified based on six different LULC classes, namely water, vegetation, flooded vegetation, agriculture, built-up area, and bare land. The Cellular Automata–Artificial Neural Network (CA − ANN) technique was used to predict LULC changes. QGIS plugin MOLUSCE with Multi-Layer Perception (MLP), was used to predict and determine potential LULC changes for 2025 and 2028. The overall Kappa coefficient value of 0.78, and an accuracy of 82% indicated good results for LULC changes and projected maps for 2025. Prediction of LULC changes using MLP − ANN for the years 2025 and 2028 showed increase in agriculture, built-up areas, and barren land. The results of the study will be useful to develop better management techniques of natural resources.
PubDate: 2023-02-27
- Correction: Spatio-temporal growth of a traditional urban centre in
Nigeria-
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PubDate: 2023-02-27
- Cost–benefit analysis of remote sensing data types for mapping
mosquito breeding sites-
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Abstract: Abstract Environmentally friendly biological mosquito control by Bacillus thuringiensis var. israelensis formulations needs appropriate breeding maps. The mapping accuracy depends on the quality of the used remote sensing data. Further, the mapping is expected to be cost-effective. Our aim was to study the effect of the quality of various remote sensing data on the applicability of the maps. We depicted larval habitats by manual interpretation in Quantum GIS 3.16.1 software using remote sensing data of SENTINEL, Google Earth, commercial geoTIFF RGB orthophoto, individual unoccupied aerial systems (UAS) RGB, and multispectral mosaics. Based on our results, after classification of the target area by sorting, mixed-use of remote sensing data is required to achieve a highly cost-efficient mapping: RGB aerial photographs with 0.5 m per pixel resolution can be used efficiently in areas dominated by grassland habitats, while forest areas need customised footage taken by UAS or drones during the foliage-free period (15 cm per pixel resolution, multispectral technique). Our cost–benefit analysis showed that the aim-optimised method could reduce investment to 6–8% and the cost of data collection to 20–50% of the highest budget. This result is significant for all participants of biological mosquito control.
PubDate: 2023-02-23
- Migration in propinquity with development: a spatial analysis of Kashmir
Valley, India-
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Abstract: Abstract Migration is the primary demographic process that shapes where people live and plays an essential role in how humans have changed over time. People migrate for various reasons, but the predicted economic disparity between industrialized and developing regions is a powerful motivator for people to relocate. As a result, migration has an impact on development, and development also affects migration. Because of the vast differences in development among India's regions, the future of population dynamics will rely more on migration than fertility and mortality processes. The present paper analyzes the spatial patterns of internal migration, variations in the level of development, and the propinquity between the two. Secondary data were used from the census of India, government reports, and research publications. Data were analysed using Karl Pearson's correlation coefficient and a t-test to test the observed correlation's significance. The results revealed a high positive correlation between internal migration and levels of development (r = 0.791). Out of thirty-three development variables, only sixteen have a higher significant relationship with internal migration, which mainly determines population characteristics, education facilities, employment opportunities, and infrastructural facilities.
PubDate: 2023-02-11
- Comparative Evaluation of Attribute-Enabled Supervised Classification in
Predicting the Air Quality-
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Abstract: Abstract Air pollution demonstrates the appearance of toxins into the air which is blocking human prosperity and the earth. It will portray as potentially the riskiest threats that humanity anytime faced. It makes hurt animals, harvests to thwart these issues in transportation territories need to expect air quality from pollutions utilizing AI systems and IoT. Along these lines, air quality evaluation and assumption has become a huge target for human health factors and also affect internal organs related to respiratory. The accuracy of Air Pollution prediction has been involved with the machine learning techniques and the best accuracy model is identified. The air quality prediction dataset is used for identifying the meteorology air pollution data while the predicted model is involved the decision tree computation for predicting the toxin contents in the region, the Air quality indicator is used to assess the pollution level and monitoring the air quality. The performance analysis shows that the decision tree technique has produced the better results in the performance metrics of Accuracy, precision, recall, and F1-score with the minimized error values while the comparative evaluation of Attribute-enabled classification has identified the best technique for predicting the air quality.
PubDate: 2023-02-06
- Spatiotemporal patterns of homicide rates in Tehran metropolitan area,
Iran-
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Abstract: Abstract The aim of this study was to detect significant temporal, spatial, and space–time patterns in homicide incidents in Tehran, Iran. Tehran is one of the cities with the highest rates of violent crime, with an average of 60 intentional homicides annually. This study examined and analysed the spatiotemporal patterns of intentional homicide in 22 municipality regions of Tehran metropolis using data from 590 victims (from 2008 to 2018). To detect statistically significant areas with a high rate of homicide incidences, an integrated scan-statistics and geographic information systems methodology was used. The study's findings indicate that during the period of rising crime from 2009 to 2011, the central and southern regions of the city (regions 9, 12, 16, and 20) with average rates of 10 per 100,000 experienced the highest rates of homicide. According to this study’s space–time cluster analysis findings, regions 9, 16, and 20 were the city hotspots with the highest homicide risk, with local relative risk values exceeding 2.56. These regions have a poor quality of life and numerous environmental problems. In high-risk regions, these findings may lead to useful preventative measures combined with appropriate socioeconomic and environmental improvement. The findings’ implications and directions for future research are addressed.
PubDate: 2023-02-01
- An overview of backbone technology behind the latest advanced gadgets in
use: 4G & 5G-
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Abstract: Abstract Technology has a significant impact on human behavior. Wireless technology has changed business, living conditions, infrastructure, and many other aspects of human life. Mankind is constantly attempting to come up with attractive solutions to many problems and seeking new methods to progress. With growing humankind’s ambition, wireless technology has developed from 1G to 5G. This evolution, on the other hand, has not slowed down. In this paper, discussion is given about the technologies that form a part of 4G and 5G communication. In addition, the paper highlights the review of the technologies implemented for 4G and 5G mobile networks. Cellular networks have grown and improved immensely in recent years, in terms of customers, data speeds, outreached and other aspects. In both 4G and 5G technology, the mobile consumer has taken precedence above all others. The objective of this paper is to provide a comprehensive review of the essential enabling technologies for 4G and 5G, and a variation between these for its versatility and connectivity.
PubDate: 2023-02-01
- Social vulnerability and COVID-19 in Maringá, Brazil
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Abstract: Abstract This research explores the relationship between COVID-19 and social vulnerability on an intra-urban scale. For this, two composite indicators of social vulnerability have been constructed. The composite indicator constructed by the Benefit-of-the-Doubt considers spatial heterogeneity. It weakly captures the conceptually most significant individual indicator of social vulnerability (R=-0.39), as it overestimates the above-average performance sub-indicators. The composite indicator constructed by the Principal Component Analysis considers that the sub-indicators have the same weights in different census tracts, resulting in a highly consistent composite indicator as a multidimensional phenomenon concept (R=-0.93). These findings allow reaching four conclusions. First, the direction and strength of correlations associated with COVID-19 are sensitive to the method employed to construct the composite indicator and not just the geographic scale and space. Second, Medium and High social vulnerability census tracts concentrate 97% of the population but only 93% of COVID-19 cases and deaths. Third, people living in census tracts of None and Low social vulnerability are 3.87 and 2.13 times more likely to be infected or die from COVID-19. Fourth, policies to combat COVID-19 in the study area should prioritize older populations regardless of their social conditions.
PubDate: 2023-02-01
- Economic empowerment of rural and urban women in India: A comparative
analysis-
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Abstract: Abstract The status of women is intimately connected with their economic situation depending upon the opportunity for participation in economic activities. The census data, 2011 shows a vast inequality between rural and urban women work participation as urban women associated with economic activities is just about half of the rural women. Available pieces of the literature revealed how the employment status of women makes them empower, but limited research has been conducted on the comparison of women empowerment in the rural-urban area in different dimensions. In this perspective, assuming that women’s economic empowerment is dependent on work participation, the present study attempts to compare the magnitude of women’s economic empowerment in urban India with its rural counterpart, focusing on various dimensions of work participation. This study is entirely based on secondary databases collected from the Census of India, 2011 and Periodic Labour Force Survey (PLFS) 2019-20. Economic Empowerment Index (EEI) of women has been measured with the help of women work participation, literate women work share, educational level-wise women work participation, work share by married women and job profile wise women work share using the widely adopted normalization technique. The result of the study is showing that the rural women are more engaged in the workforce in all the selected dimensions. The overall analysis is reflected in EEI, which proves that rural women are more economically empowered in comparison with their urban counterparts.
PubDate: 2023-02-01
- Investigating the spatial collision factors involved in bikeshare crashes
at Washington, D.C-
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Abstract: Abstract The objective of the study is to explore and characterize the spatial collision factors for bikeshare crashes using spatial and mathematical modeling. First, the nine most influential components behind the bikeshare crashes in Washington, D.C (179 census tracts) were selected as study variables (i.e., population density, number of bikeshare trips, etc.). Next, a spatial weight matrix was used to quantify the spatial relationships among the study variables with the bikeshare crashes. Finally, three models (i.e., Classic Regression, Spatial Lag, and Spatial Error) were used to investigate the essence of the interaction between these variables and bikeshare crashes. Finally, the spatial collision factors involved in bikeshare crashes were identified. According to model results, two causal factors (i.e., no. of cafe and no. of bikeshare points) significantly influence the bikeshare crashes in the Washington, D.C. area. The findings regarding spatial factors involved in bikeshare crashes can be useful in making optimum decisions regarding planning for bikeshare safety.
PubDate: 2023-02-01
- Spatial justice in relation to the urban amenities distribution in Austin,
Texas-
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Abstract: Abstract In addition to enhancing our theoretical grasp of justice, thinking spatially about it can also reveal important new insights that broaden our practical understanding in order to advance justice and democracy. On the other hand, these opportunities won’t be as obvious if the spatial equities aren't made apparent and strong. Austin city has experienced a fast-urban growing in the past decades. As urban areas grow, the public facilities should increase. The purpose of this paper investigates Facilities in terms of public facilities. Even though we said that the concept of justice is very complex, it is possible to get an understanding of it by using a quantitative method. This paper explores the condition of urban justice and opportunities for accessibility to public facilities for all residents in Austin by using GIS data and the Fuzzy logic model. The facilities and services maps were made in GIS and after the Euclidean Distance and Reclassify function in Arc Map, the Fuzzy Logic model was used to analyze spatial justice. The result shows the facilities are distributed properly. Spatial justice is in the context of Austin and residents enjoy spatial justice.
PubDate: 2023-02-01
- An effective deep learning model for ship detection from satellite images
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Abstract: Abstract Detecting ships from satellite images is a challenging task in the domain of remote sensing. It is very important for security, traffic management and to avoid smuggling etc. SAR (Synthetic Aperture Radar) is mostly used technology for Maritime monitoring but now researchers are increasingly studying Optical Satellite Images based technologies. Image processing and Computer Vision techniques were previously used to detect ships. In this work, Convolutional Neural Network based approach is used to detect ships from the satellite imagery. Several Deep Learning models have been used and tested for this kind of task. We used state of art model Inception-Resnet that is pre trained on Image-Net dataset. We used the dataset "Ships in Satellite Imagery" to detect the presence of ships in an image. The dataset is publicly available on Kaggle. The results indicate adoption of transfer learning and data augmentation yields a successful detection of ships with an accuracy of more than 99%. Similarly, exploring different deep learning models for this task provide results with high accuracy for less training time.
PubDate: 2023-02-01
- Indexing hs code- a hybrid indexer for an optimized search of geotagged
data-
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Abstract: Abstract In case of unstructured data processing current technologies have developed a lot of solutions to process and provide insight into it, which has become paramount today due to the permeation of social media. Social media analytics extract data points by searching the most relevant textual reference and isolating textual data with location information. This increase the complexity of modern social media search engines. The indexer is imperative towards the performance of these engines. Geotagged data generated via modern social media technologies has augmented the need to enhance such search mechanisms designed for spatial data.Conventional spatial indexers designed to handle such data can accurately search spatial objects but with a considerable increase in seek time. This paper presents a hybrid spatial indexer based on “Hs-I” tree for the “Social Media Spatial Analytical (SMSA)” model. The purposed indexer is 17.768% faster when compared with the “Geo-hash” indexer. The model refers “CBDFI” model for base architecture and deploys the advantage of the “hs” code. The paper presents the comparison of the purposed indexer with various other spatial indexers and highlights its key points in terms of execution time.
PubDate: 2023-02-01
- Exploring key drivers of forest fires in the Mole National Park of Ghana
using geospatial tools-
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Abstract: Abstract Ghana’s forest and wildlife reserves are depleting at an alarming rate and hence, the Mole National Park which is considered the oldest and largest wildlife protected area in Ghana is no exception. Bush fires are a major contributor to this menace. Fire hotspot prediction and mapping using satellite data provides a better alternative to monitoring and addressing wildfires compared to the observation of smoke emission which is the conventional tool used to detect forest fires in most parts of the country. This study sought to probe into fire incidences within the park using geospatial tools with an emphasis on climatic variables and anthropogenic activities as drivers. The research shows a weak positive correlation for fire counts to climatic variables (temperature and precipitation), giving credence to human-induced factors as majorly responsible while climatic conditions serve as a background catalyst to these fire incidences. Forest fire counts and their associated burnt area statistics between 2002 and 2019 are also presented. To reduce human-induced forest fires, this study recommends several interventions to help manage the challenge of wildfires at the reserve while highlighting the essential role of GIS and remote-sensing tools in monitoring and managing wildfires within protected areas.
PubDate: 2023-02-01
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