GIS and Remote Sensing for Renewable Energy Assessment and Maps

Nastasi, Benedetto; Nezhad, Meysam Majidi

doi:10.3390/en15010014

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…To enhance the performance of models, several researchers have focused on maximizing their integration. Integrating GIS software and BIM models can save time and allow for more precise monitoring of construction and post-construction processes [70,71]. Numerous studies have demonstrated the successful utilization of GIS software and BIM model integration for developing and visualizing a range of functions [72].…”

Section: Gis and Bim Integrationmentioning

confidence: 99%

Digital Twins for Built Environment. A Review on Key Enablers

Piras¹,

Agostinelli²

2023

Preprint

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The emergence of Digital Twin (DT) technology is creating unique opportunities for human so-ciety in terms of real-time data transfer from the physical environment to its digital replica. DT technology has two-way interaction capabilities that enable data exchange between physical and virtual environments, creating bidirectional data flows. Progress has been made in different in-dustry sectors, but the Architecture, Engineering, Construction, and Operation (AECO) sector still requires further development. Recent studies have shown the potential for DT-based platform implementation in the lifecycle management of buildings and infrastructure. This paper examines the development and implementation of DT technology in the construction sector, identifying key definitions, enablers and use cases, while comparing its use with other industries and evaluating benefits and future potential.

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Section: Gis and Bim Integrationmentioning

confidence: 99%

Digital Twins for Built Environment. A Review on Key Enablers

Piras¹,

Agostinelli²

2023

Preprint

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Marine energy digitalization digital twin's approaches

Majidi Nezhad,

Neshat,

Sylaios

et al. 2024

Renewable and Sustainable Energy Reviews

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RETRACTED: An improved interval type-2 possibilistic C-means clustering algorithm for interclass maximization

Xing,

Zhang,

Tong

et al. 2024

IFS

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Cluster analysis is an important method for data analysis, which is also widely used in remote sensing image classification. An effective clustering algorithm is proposed for the uncertainty caused by the overlap between categories in pattern data and remote sensing images. First, an improved PCM (Possibilistic C-means) algorithm based on distance of cluster center (dc-PCM) is proposed to solve the clustering consistency problem of PCM algorithm. Then, based on dc-PCM algorithm, an improved interval type-2 probabilistic c-means clustering algorithm (IT2-dcPCM) is proposed for interclass maximization. The computational complexity of the proposed algorithm is analyzed, and its performance is evaluated by three data sets, two groups of images segmentation and two groups of remote sensing image classification experiments. The results show that IT2-dcPCM algorithm can assign proper membership degrees to clustering samples, and it has good performance in image segmentation and remote sensing image classification with serious spectral aliasing.

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GIS and Remote Sensing for Renewable Energy Assessment and Maps

Abstract: Geographic Information Systems (GIS) and Remote Sensing (RS) techniques are of great interest for the renewable energy field [...]

Cited by 3 publications

References 13 publications

Digital Twins for Built Environment. A Review on Key Enablers

Digital Twins for Built Environment. A Review on Key Enablers

Marine energy digitalization digital twin's approaches

RETRACTED: An improved interval type-2 possibilistic C-means clustering algorithm for interclass maximization

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