scholarly journals Development of a Spatiotemporal Database for Evolution Analysis of the Moscow Backbone Power Grid

Data ◽  
2021 ◽  
Vol 6 (12) ◽  
pp. 127
Author(s):  
Andrey Karpachevskiy ◽  
German Titov ◽  
Oksana Filippova
Keyword(s):  
Remote Sensing ◽  
Data Structure ◽  
Web Service ◽  
Spatial Data ◽  
Power Grid ◽  
Remote Sensing Data ◽  
Electrical Networks ◽  
Electric Networks ◽  
Evolution Analysis ◽  
Spatiotemporal Database

Currently in the field of transport geography, the spatial evolution of electrical networks remain globally understudied. Publicly available data sources, including remote sensing data, have made it possible to collect spatial data on electrical networks, but at the same time a suitable data structure for storing them has not been defined. The main purpose of this study was the collection and structuring of spatiotemporal data on electric networks with the possibility of their further processing and analysis. To collect data, we used publicly available remote sensing and geoinformation systems, archival schemes and maps, as well as other documents related to the Moscow power grid. Additionally, we developed a web service for data publication and visualization. We conducted a small morphological analysis of the evolution of the network to show the possibilities of working with the database using a Python script. For example, we found that the portion of new lines has been declining since 1950s and in the 2010s the portion of partial reconstruction reached its maximum. Thus, the developed data structure and the database itself provide ample opportunities for the analysis and interpretation of the spatiotemporal development of electric networks. This can be used as a basis to study other territories. The main results of the study are published on the web service where the user can interactively choose a year and two forms of power lines representation to visualize on a map.

scholarly journals Forest areas remote monitoring within the limits of transport objects impact

2021 ◽  
pp. 109-118
Author(s):  
Dmytro Liashenko ◽  
◽  
Dmytro Pavliuk ◽  
Vadym Belenok ◽  
Vitalii Babii ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Remote Monitoring ◽  
Forest Cover ◽  
Landsat Imagery ◽  
Remote Sensing Data ◽  
Transport Infrastructure ◽  
Nature Management ◽  
Collecting Methods ◽  
Remote Sensing Methods

The article studies the issues of using remote sensing data for the tasks of ensuring sustainable nature management in the territories within the influence of transport infrastructure objects. Peculiarities of remote monitoring for tasks of transport networks design and in the process of their operation are determined. The paper analyzes the development of modern remote sensing methods (satellite imagery, the use of mobile sensors installed on cars or aircraft). A brief overview of spatial data collecting methods for the tasks of managing the development of territories within the influence of transport infrastructure (roads, railways, etc.) has made. The article considers the experience of using remote sensing technologies to monitor changes in the parameters of forest cover in the Transcarpathian region (Ukraine) in areas near to highways, by use Landsat imagery.

scholarly journals A REVIEW OF 3D GIS FOR USE IN CREATING VIRTUAL HISTORIC DUBLIN

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 249-254
Author(s):  
A. Chenaux ◽  
M. Murphy ◽  
S. Pavia ◽  
S. Fai ◽  
T. Molnar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Complex Analysis ◽  
Remote Sensing Data ◽  
Public Works ◽  
3D Gis ◽  
Building Information ◽  
Urban Rural ◽  
Interactive 3D ◽  
Under Construction

<p><strong>Abstract.</strong> This paper illustrates how BIM integration with GIS is approached as part of the workflow in creating Virtual Historic Dublin. A design for a WEB based interactive 3D model of historic buildings and centres in Dublin City (Virtual Historic Dublin City) paralleling smart city initiates is now under construction and led by the National Monuments at the Office of Public Works in Ireland. The aim is to facilitate the conservation and maintenance of historic infrastructure and fabric and the dissemination of knowledge for education and cultural tourism using an extensive Historic Building Information Model. Remote sensing data is now processed with greater ease to create 3D intelligent models in Historic BIM. While the use of remote sensing, HBIM and game engine platforms are the main applications used at present, 3D GIS has potential to form part of the workflow for developing the Virtual Historic City. 2D GIS is now being replaced by 3D spatial data allowing more complex analysis to be carried out, 3D GIS can define and depict buildings, urban rural centres in relation to their geometry topological, semantic and visualisation properties. The addition of semantic attributes allows complex analysis and 3D spatial queries for modelling city and urban elements. This analysis includes fabric and structural elements of buildings, relief, vegetation, transportation, water bodies, city furniture and land use.</p>

scholarly journals DMF Data Format Usage for Change Detection

2012 ◽  
Vol 2012 ◽  
pp. 1-9
Author(s):  
Alejandro Cristo ◽  
David Valencia ◽  
Pablo J. Martínez ◽  
Rosa M. Pérez
Keyword(s):  
Remote Sensing ◽  
Data Structure ◽  
Data Fusion ◽  
Change Detection ◽  
Remote Sensing Data ◽  
Great Accuracy ◽  
New Techniques ◽  
Data Format ◽  
Specific Data ◽  
The Earth

Because of the availability of an overwhelming amount of remote sensing data obtained by different instruments, new techniques and applications have been developed in order to pursue the objective of detecting changes that occur in a particular area of the Earth or that affect a large part of the Earth. These studies have used datasets covering different wavelength ranges (visible, IR, radar, and so on), but common to all of them is the necessity for great accuracy to ensure that no bias is introduced due to data correction. Otherwise, a result may be the generation of false positives. Also, many studies have used several different datasets for the same area to detect changes (this is usually called data fusion), but there exists no specific data structure designed for this purpose. In this paper, we propose a data structure to be used for accurate change detection. This structure is transparent to the user and can be used for data fusion to improve those studies.

scholarly journals USING SYSTEMS OF PARALLEL AND DISTRIBUTED DATA PROCESSING TO BUILD HYDROLOGICAL MODELS BASED ON REMOTE SENSING DATA

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 111-116
Author(s):  
A. A. Kolesnikov ◽  
P. M. Kikin ◽  
E. A. Panidi ◽  
A. G. Rusina
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Spatial Data ◽  
Distributed Processing ◽  
Remote Sensing Data ◽  
Hydroelectric Power Plant ◽  
Point Clouds ◽  
Hydroelectric Power ◽  
Raster Data ◽  
Sensing Data

Abstract. The article describes the possibilities and advantages of using distributed systems in the processing and analysis of remote sensing data. The preparation and processing of various types of remote sensing data (multispectral satellite images, values of climatic indicators, elevation data), which will then be used to build a simulation model of a hydroelectric power plant, was chosen as the basic task for testing the chosen approach. The existing approaches with distributed processing of spatial data of various types (vector cartographic objects, raster data, point clouds, graphs) are analyzed. The description of the developed approach is given and the rationale for the choice of its components is made. The preprocessing operations that were performed on the used raster data are described. An approach to the problems of raster data segmentation based on libraries for distributed machine learning is considered. Comparison of the speed of working with data for various algorithms of machine learning and processing is given.

scholarly journals A Precision Evaluation Method for Remote Sensing Data Sampling Based on Hexagon Discrete Grid

2020 ◽  
Author(s):  
Yue Ma ◽  
Guoqing Li ◽  
Xiaochuang Yao ◽  
Jin Ben ◽  
Qianqian Cao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Evaluation System ◽  
Evaluation Method ◽  
Remote Sensing Data ◽  
Accuracy Evaluation ◽  
Remote Sensing Images ◽  
Sensing Data ◽  
Hexagonal Grids ◽  
Discrete Grid

With the rapid development of earth observation, satellite navigation, mobile communication and other technologies, the order of magnitude of the spatial data we acquire and accumulate is increasing, and higher requirements are put forward for the application and storage of spatial data. Under this circumstance, a new form of spatial data organization emerged-the global discrete grid. This form of data management can be used for the efficient storage and application of large-scale global spatial data, which is a digital multi-resolution the geo-reference model that helps to establish a new model of data association and fusion. It is expected to make up for the shortcomings in the organization, processing and application of current spatial data. There are different types of grid system according to the grid division form, including global discrete grids with equal latitude and longitude, global discrete grids with variable latitude and longitude, and global discrete grids based on regular polyhedrons. However, there is no accuracy evaluation index system for remote sensing images expressed on the global discrete grid to solve this problem. This paper is dedicated to finding a suitable way to express remote sensing data on discrete grids, and establishing a suitable accuracy evaluation system for modeling remote sensing data based on hexagonal grids to evaluate modeling accuracy. The results show that this accuracy evaluation method can evaluate and analyze remote sensing data based on hexagonal grids from multiple levels, and the comprehensive similarity coefficient of the images before and after conversion is greater than 98%, which further proves that the availability hexagonal grid-based remote sensing data of remote sensing images. And among the three sampling methods, the image obtained by the nearest interpolation sampling method has the highest correlation with the original image.

scholarly journals REMOTE SENSING ANALYTICAL GEOSPATIAL OPERATIONS DIRECTLY IN THE WEB BROWSER

2018 ◽  
Vol XLII-4 ◽  
pp. 403-410 ◽  
Author(s):  
J. Masó ◽  
A. Zabala ◽  
I. Serral ◽  
X. Pons
Keyword(s):  
Remote Sensing ◽  
Web Service ◽  
Remote Sensing Data ◽  
Web Browsers ◽  
Spectral Indices ◽  
Spatial Filters ◽  
Web Browser ◽  
Server Side ◽  
Client Side ◽  
Fine Tune

<p><strong>Abstract.</strong> Current map viewers that run on modern web browsers are mainly requesting images generated on the fly in the server side and transferred in pictorial format that they can display (PNG or JPEG). In OGC WMS standard this is done for the whole map view while in WMTS is done per tiles. The user cannot fine tune personalized visualization or data analysis in the client side. Remote sensing data is structured in bands that are visualize individually (manually adjusting contrast), create RGB combinations or present spectral indices. When these operations are not available in map browsers professional are forced to download hundreds of gigabytes of remote sensing imagery to take a good look at the data before deciding if it fits for a purpose. A possible solution is to create a web service that is able to perform these operations on the server side (https://www.sentinel-hub.com). This paper proposes that the server should communicate the data values to the client in a format that the client can directly process using two new additions in HTML5: canvas edition and array buffers. In the client side, the user can interact with a JavaScript interface changing symbolizations and doing some analytical operations without having to request any data again to the server. As a bonus, the user is able to perform queries to the data in a more dynamic way, applying spatial filters, creating histograms, generating animations of a time series or performing complex calculations among bands of the different loaded datasets.</p>

An Introduction to Spatial Data Analysis

2020 ◽  
Author(s):  
Martin Wegmann ◽  
Jakob Schwalb-Willmann ◽  
Stefan Dech
Keyword(s):  
Remote Sensing ◽  
Data Analysis ◽  
Open Source ◽  
Spatial Data ◽  
Remote Sensing Data ◽  
Spatial Data Analysis ◽  
Data Sets ◽  
Data Handling ◽  
Book Covers

This is a book about how ecologists can integrate remote sensing and GIS in their research. It will allow readers to get started with the application of remote sensing and to understand its potential and limitations. Using practical examples, the book covers all necessary steps from planning field campaigns to deriving ecologically relevant information through remote sensing and modelling of species distributions. An Introduction to Spatial Data Analysis introduces spatial data handling using the open source software Quantum GIS (QGIS). In addition, readers will be guided through their first steps in the R programming language. The authors explain the fundamentals of spatial data handling and analysis, empowering the reader to turn data acquired in the field into actual spatial data. Readers will learn to process and analyse spatial data of different types and interpret the data and results. After finishing this book, readers will be able to address questions such as “What is the distance to the border of the protected area?”, “Which points are located close to a road?”, “Which fraction of land cover types exist in my study area?” using different software and techniques. This book is for novice spatial data users and does not assume any prior knowledge of spatial data itself or practical experience working with such data sets. Readers will likely include student and professional ecologists, geographers and any environmental scientists or practitioners who need to collect, visualize and analyse spatial data. The software used is the widely applied open source scientific programs QGIS and R. All scripts and data sets used in the book will be provided online at book.ecosens.org. This book covers specific methods including: what to consider before collecting in situ data how to work with spatial data collected in situ the difference between raster and vector data how to acquire further vector and raster data how to create relevant environmental information how to combine and analyse in situ and remote sensing data how to create useful maps for field work and presentations how to use QGIS and R for spatial analysis how to develop analysis scripts

scholarly journals Mapping Flood-Based Farming Systems with Bayesian Networks

Land ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 369 ◽  
Author(s):  
Issoufou Liman Harou ◽  
Cory Whitney ◽  
James Kung’u ◽  
Eike Luedeling
Keyword(s):  
Remote Sensing ◽  
Bayesian Network ◽  
Spatial Data ◽  
Probabilistic Reasoning ◽  
Expert Knowledge ◽  
Agricultural Research ◽  
Remote Sensing Data ◽  
Farming Systems ◽  
Spatially Explicit ◽  
Sensing Data

Many actors in agricultural research, development, and policy arenas require accurate information on the spatial extents of cropping and farming practices. While remote sensing provides ways for obtaining such information, it is often difficult to distinguish between different types of agricultural practices or identify particular farming systems. Stochastic system behavior or similarity in the spectral signatures of different system components can lead to misclassification. We addressed this challenge by using a probabilistic reasoning engine informed by expert knowledge and remote sensing data to map flood-based farming systems (FBFS) across Kisumu County in Kenya and the Tigray region in Ethiopia. Flood-based farming is an important form of agricultural production employed in regions with seasonal water surplus, which can be harvested and used to irrigate crops. Geographic settings for FBFS vary widely in terms of hydrology, vegetation, and local practices of agronomic flooding. Agronomic success is often difficult to anticipate, because the timing and amount of flooding usually cannot be precisely predicted. We generated a Bayesian network model to describe the FBFS settings of the study regions. We acquired three years (2014–2016) of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra spectral data as eight-day composite time series and elevation data from the Shuttle Radar Topography Mission (SRTM) to compute 10 spatial data metrics corresponding to 10 of the 17 Bayesian network nodes. We used the spatial data metrics in a fully probabilistic framework to generate the 10 spatial data nodes. We then used these as inputs for the probabilistic model to generate prior and posterior spatial estimates for specific metrics along with their spatially explicit uncertainties. We show how such an approach can be used to predict plausible areas for FBFS based on several scenarios. We demonstrate how spatially explicit information can be derived from remote sensing data as fuzzy quantifiers for incorporating uncertainties when mapping complex systems. The approach achieved a remarkably accurate result in both study areas, where 84–90% of various FBFS fields sampled were correctly mapped as having a high chance of being suitable for the practice.

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