scholarly journals Deep learning for radio propagation: Using image-driven regression to estimate path loss in urban areas

ICT Express ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 160-165 ◽  
Author(s):  
Sotirios P. Sotiroudis ◽  
Sotirios K. Goudos ◽  
Katherine Siakavara
Keyword(s):  
Deep Learning ◽  
Urban Areas ◽  
Path Loss ◽  
Radio Propagation

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

scholarly journals Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home Charging

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 135-149
Author(s):  
James Flynn ◽  
Cinzia Giannetti
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Urban Areas ◽  
Proof Of Concept ◽  
Major Step ◽  
Dominant Form ◽  
Residential Properties ◽  
The Uk ◽  
Ev Charging ◽  
Unique Dataset

With Electric Vehicles (EV) emerging as the dominant form of green transport in the UK, it is critical that we better understand existing infrastructures in place to support the uptake of these vehicles. In this multi-disciplinary paper, we demonstrate a novel end-to-end workflow using deep learning to perform automated surveys of urban areas to identify residential properties suitable for EV charging. A unique dataset comprised of open source Google Street View images was used to train and compare three deep neural networks and represents the first attempt to classify residential driveways from streetscape imagery. We demonstrate the full system workflow on two urban areas and achieve accuracies of 87.2% and 89.3% respectively. This proof of concept demonstrates a promising new application of deep learning in the field of remote sensing, geospatial analysis, and urban planning, as well as a major step towards fully autonomous artificially intelligent surveying techniques of the built environment.

scholarly journals Fusing Diverse Input Modalities for Path Loss Prediction: A Deep Learning Approach

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 30441-30451
Author(s):  
Sotirios P. Sotiroudis ◽  
Panagiotis Sarigiannidis ◽  
Sotirios K. Goudos ◽  
Katherine Siakavara
Keyword(s):  
Deep Learning ◽  
Path Loss ◽  
Learning Approach ◽  
Loss Prediction ◽  
Path Loss Prediction

scholarly journals BUILDING CHANGE DETECTION FROM BITEMPORAL AERIAL IMAGES USING DEEP LEARNING

2020 ◽  
Vol V-2-2020 ◽  
pp. 565-571
Author(s):  
S. Su ◽  
T. Nawata ◽  
T. Fuse
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Urban Areas ◽  
Network Architecture ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Detection Model ◽  
Wide Range ◽  
Surface Models ◽  
Data Source

Abstract. Automatic building change detection has become a topical issue owing to its wide range of applications, such as updating building maps. However, accurate building change detection remains challenging, particularly in urban areas. Thus far, there has been limited research on the use of the outdated building map (the building map before the update, referred to herein as the old-map) to increase the accuracy of building change detection. This paper presents a novel deep-learning-based method for building change detection using bitemporal aerial images containing RGB bands, bitemporal digital surface models (DSMs), and an old-map. The aerial images have two types of spatial resolutions, 12.5 cm or 16 cm, and the cell size of the DSMs is 50 cm × 50 cm. The bitemporal aerial images, the height variations calculated using the differences between the bitemporal DSMs, and the old-map were fed into a network architecture to build an automatic building change detection model. The performance of the model was quantitatively and qualitatively evaluated for an urban area that covered approximately 10 km2 and contained over 21,000 buildings. The results indicate that it can detect the building changes with optimum accuracy as compared to other methods that use inputs such as i) bitemporal aerial images only, ii) bitemporal aerial images and bitemporal DSMs, and iii) bitemporal aerial images and an old-map. The proposed method achieved recall rates of 89.3%, 88.8%, and 99.5% for new, demolished, and other buildings, respectively. The results also demonstrate that the old-map is an effective data source for increasing building change detection accuracy.

scholarly journals Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Quadri Ramon Adebowale ◽  
Nasir Faruk ◽  
Kayode S. Adewole ◽  
Abubakar Abdulkarim ◽  
Lukman A. Olawoyin ◽  
...  
Keyword(s):  
Computational Intelligence ◽  
Path Loss ◽  
Channel Modeling ◽  
Radio Propagation ◽  
Future Research ◽  
Computational Techniques ◽  
Research Issues ◽  
Main Research ◽  
Loss Prediction ◽  
Path Loss Prediction

The importance of wireless path loss prediction and interference minimization studies in various environments cannot be over-emphasized. In fact, numerous researchers have done massive work on scrutinizing the effectiveness of existing path loss models for channel modeling. The difficulties experienced by the researchers determining or having the detailed information about the propagating environment prompted for the use of computational intelligence (CI) methods in the prediction of path loss. This paper presents a comprehensive and systematic literature review on the application of nature-inspired computational approaches in radio propagation analysis. In particular, we cover artificial neural networks (ANNs), fuzzy inference systems (FISs), swarm intelligence (SI), and other computational techniques. The main research trends and a general overview of the different research areas, open research issues, and future research directions are also presented in this paper. This review paper will serve as reference material for researchers in the field of channel modeling or radio propagation and in particular for research in path loss prediction.

Challenges in Channel Measurement and Modeling for RF Localization Inside the Human Body

2012 ◽  
Vol 3 (3) ◽  
pp. 18-37 ◽  
Author(s):  
Kaveh Pahlavan ◽  
Yunxing Ye ◽  
Ruijun Fu ◽  
Umair Khan
Keyword(s):  
Human Body ◽  
Path Loss ◽  
Signal Strength ◽  
Radio Propagation ◽  
Time Of Arrival ◽  
Precise Localization ◽  
Channel Measurement ◽  
Modeling Techniques ◽  
Endoscopy Capsule ◽  
Loss Models

In this invited paper, the authors introduce an overview of the fundamentals of radio frequency (RF) channel measurement and modeling techniques needed for localization inside the human body. To address these fundamentals, the authors use capsule endoscopy as an example application. The authors first provide the results of the Cramer Rao Lower Bound (CRLB) for received signal strength (RSS) based endoscopy capsule localization, inside the human body, using existing path-loss models for radio propagation. Then challenges demanding further research are highlighted for attaining more precise localization using the time-of-arrival (TOA) based ranging techniques.

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