دانلود و نمایش مقالات مرتبط با Satellite imagery::صفحه 1
دانلود بهترین مقالات isi همراه با ترجمه فارسی 2

با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد). 

نتیجه جستجو - Satellite imagery

تعداد مقالات یافته شده: 7
ردیف عنوان نوع
1 Power to the people: Applying citizen science and computer vision to home mapping for rural energy access
قدرت به مردم: به کارگیری علم شهروندی و بینش رایانه در نقشه‌برداری خانه برای دسترسی به انرژی روستایی-2022
To implement effective rural electricity access systems, it is fundamental to identify where potential consumers live. Here, we test the suitability of citizen science paired with satellite imagery and computer vision to map remote off-grid homes for electrical system design. A citizen science project called “Power to the People” was completed on the Zooniverse platform to collect home annotations in Uganda, Kenya, and Sierra Leone. Thou- sands of citizen scientists created a novel dataset of 578,010 home annotations with an average mapping speed of 7 km2/day. These data were post-processed with clustering to determine high-consensus home annotations. The raw annotations achieved a recall of 93% and precision of 49%; clustering the annotations increased precision to 69%. These were used to train a Faster R-CNN object detection model, producing detections useful as a first pass for home-level mapping with a feasible mapping rate of 42,938 km2/day. Detections achieved a precision of 67% and recall of 36%. This research shows citizen science and computer vision to be a promising pipeline for accelerated rural home-level mapping to enable energy system design.
keywords: دانش شهروندی | بینایی کامپیوتر | دسترسی به برق | نقشه برداری روستایی | تصویربرداری ماهواره ای | سنجش از دور | Citizen science | Computer vision | Electricity access | Rural mapping | Satellite imagery | Remote sensing
مقاله انگلیسی
2 The importance of accounting-integrated information systems for realising productivity and sustainability in the agricultural sector
اهمیت سیستم های اطلاعاتی حسابداری یکپارچه برای تحقق بهره وری و پایداری در بخش کشاورزی-2021
Agricultural information systems are an integral part of modern farming and are helping to make a significant contribution to improved farm productivity and profitability. To date, however, there has been a failure to integrate accounting information systems with onfarm data, despite today’s farmers facing unprecedented and interconnected economic and resource pressures. This study explores this problem in more detail, defines the objectives of the solution and develops a model of integrated accounting and agricultural information systems, drawing on a ‘fads and fashions’ framework and advancing our understanding of bundled innovations. Using data from a participatory case study in Australian potato farming, the study integrates accounting data with soil moisture and climate data to track, alert and inform irrigation decisions. Development of preliminary digital software based on the model demonstrates how cost-informed tracking, alerts and forecasting can be supported by bundling accounting information systems and sensing technology. In doing so, the model extends the fads and fashions framework for agricultural information systems and demonstrates how accounting information can be the key for improved water productivity, profitability and agricultural sustainability.
keywords: تصمیم گیری کشاورزی | سیستم های حسابداری یکپارچه | نوآوری های همراه | سنسور | اطلاعات دیجیتال | ایستگاه های آب و هوا | تصویربرداری ماهواره ای | Agricultural decision-making | Integrated accounting systems | Bundled innovations | Sensors | Digital information | Weather stations | Satellite imagery
مقاله انگلیسی
3 Leveraging Google Earth Engine (GEE) and machine learning algorithms to incorporate in situ measurement from different times for rangelands monitoring
اهرم موتور زمین گوگل و الگوریتم های یادگیری ماشین برای ترکیب در اندازه گیری درجا از زمان های مختلف برای نظارت بر مراتع-2020
Mapping and monitoring of indicators of soil cover, vegetation structure, and various native and non-native species is a critical aspect of rangeland management. With the advancement in satellite imagery as well as cloud storage and computing, the capability now exists to conduct planetary-scale analysis, including mapping of rangeland indicators. Combined with recent investments in the collection of large amounts of in situ data in the western U.S., new approaches using machine learning can enable prediction of surface conditions at times and places when no in situ data are available. However, little analysis has yet been done on how the temporal relevancy of training data influences model performance. Here, we have leveraged the Google Earth Engine (GEE) platform and a machine learning algorithm (Random Forest, after comparison with other candidates) to identify the potential impact of different sampling times (across months and years) on estimation of rangeland indicators from the Bureau of Land Managements (BLM) Assessment, Inventory, and Monitoring (AIM) and Landscape Monitoring Framework (LMF) programs. Our results indicate that temporally relevant training data improves predictions, though the training data need not be from the exact same month and year for a prediction to be temporally relevant. Moreover, inclusion of training data from the time when predictions are desired leads to lower prediction error but the addition of training data from other times does not contribute to overall model error. Using all of the available training data can lead to biases, toward the mean, for times when indicator values are especially high or low. However, for mapping purposes, limiting training data to just the time when predictions are desired can lead to poor predictions of values outside the spatial range of the training data for that period. We conclude that the best Random Forest prediction maps will use training data from all possible times with the understanding that estimates at the extremes will be biased.
Keywords: Google earth engine | Big data | Machine learning | Domain adaptation | Transfer learning | Feature selection | Rangeland monitoring
مقاله انگلیسی
4 Research on image processing of intelligent building environment based on pattern recognition technology
تحقیق در مورد پردازش تصویر از محیط ساختمان هوشمند بر اساس فناوری تشخیص الگو-2019
With the continuous development of urbanization, urban population, economy and other factors have a close impact on the geometry and distribution of urban buildings. Obtaining information of urban buildings from aerial images or satellite images quickly and accurately is not only conducive to updating geospatial data, but also of great significance for effective monitoring of new thematic information such as new buildings. Moreover, in recent years, the research and improvement of building recognition and contour extraction algorithms based on satellite images or aerial images are helpful to the recognition and classification of urban buildings. It is of great significance to the acquisition of GIS data, the understanding of images, large-scale mapping and many other applications of remote sensing data. With the development of artificial intelligence and computer technology, the image processing of intelligent building environment based on pattern recognition technology has become an important research direction in the field of intelligent building image recognition. Based on the concept, principle and technology analysis of pattern recognition technology, this paper studies the application of pattern recognition technology in the image processing of intelligent building environment. In this paper, based on image processing of intelligent building as the basic theoretical platform, with the pattern recognition technology as the basic research means, three problems of image processing, image extraction and image recognition in image processing of building intelligent environment are studied respectively, and corresponding reasonable solutions are put forward
Keywords: Intelligent building | Image processing | Satellite imagery | Building contours | Edge detection
مقاله انگلیسی
5 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
6 Space technology meets policy: An overview of Earth Observation sensors for monitoring of cultural landscapes within policy framework for Cultural Heritage
سیاست تطابق تکنولوژی فضایی: یک مرور کلی از سنسورهای مشاهده زمین برای نظارت بر چشم اندازهای فرهنگی در چارچوب سیاست های میراث فرهنگی-2017
A wide range of satellite sensors that provide potentially useful imagery for digital documentation, mapping and monitoring of archaeological sites and cultural landscapes. Although some satellites have stopped acquiring new data, their archived satellite imagery can still be accessed, downloaded and exploited for monitoring of changes and therefore useful for research domain of archaeology and cultural landscapes. The aim of this paper is 1) to make an overview of past and current satellite earth observation optical sensors useful for land monitoring, with focus on cultural landscapes and 2) to illustrate a policy framework that goes beyond recommendations, suggesting the need of valuable information possibly provided by the in satellite imagery. Paper will put focus on Copernicus programme as the most recent mission that provides imagery on the global scale and free of charge. Paper, furthermore, highlights the need for a more structured consideration of the contribution that space technologies services and products can offer to the non-space sectors. The actions for implementation of strategies regarding the currently renewed attention towards cultural heritage protection and management, could soon benefit from the technological achievements of satellite technologies in terms of dedicated operational services and applications, tailored to the needs of end-users such as archaeologists, landscape professionals, public administration, researchers and students.
Keywords: Cultural landscapes | Earth Observation | Copernicus | Landscape management | Remote sensing | Sentinel-2 | Cultural policy
مقاله انگلیسی
7 Constructing spatiotemporal poverty indices from big data
ساخت شاخصهای فقر فضایی و زمانی از داده های بزرگ-2017
Big data offers the potential to calculate timely estimates of the socioeconomic development of a region. Mobile telephone activity provides an enormous wealth of information that can be utilized alongside household surveys. Estimates of poverty and wealth rely on the calculation of features from call detail records (CDRs), however, mobile network operators are reluctant to provide access to CDRs due to commercial and privacy concerns. As a compromise, this study shows that a sparse CDR dataset combined with other publicly available datasets based on satellite imagery can yield competitive results. In particular, a model is built using two CDR-based features, mobile ownership per capita and call volume per phone, combined with normalized satellite nightlight data and population density, to estimate the multi-dimensional poverty index (MPI) at the sector level in Rwanda. This model accurately estimates the MPI for sectors in Rwanda that contain mobile phone cell towers (cross-validated correlation of 0.88)
Keywords:Call detail record (CDR)|Poverty index|Machine learning|Big data|Socioeconomic level|Rwanda
مقاله انگلیسی
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی
logo-samandehi
بازدید امروز: 8339 :::::::: بازدید دیروز: 0 :::::::: بازدید کل: 8339 :::::::: افراد آنلاین: 56