Understanding Rooftop PV Panel Semantic Segmentation of Satellite and Aerial Images for Better Using Machine Learning

Using aerial cameras, satellite remote sensing or unmanned aerial vehicles (UAV) equipped with cameras can facilitate search and rescue tasks after disasters. The traditional manual interpretation of huge aerial images is inefficient and could be replaced by machine learning-based methods combined with image processing techniques. Given the development of machine learning, researchers find that convolutional neural networks can effectively extract features from images. Some target detection methods based on deep learning, such as the single-shot multibox detector (SSD) algorithm, can achieve better results than traditional methods. However, the impressive performance of machine learning-based methods results from the numerous labeled samples. Given the complexity of post-disaster scenarios, obtaining many samples in the aftermath of disasters is difficult. To address this issue, a damaged building assessment method using SSD with pretraining and data augmentation is proposed in the current study and highlights the following aspects. (1) Objects can be detected and classified into undamaged buildings, damaged buildings, and ruins. (2) A convolution auto-encoder (CAE) that consists of VGG16 is constructed and trained using unlabeled post-disaster images. As a transfer learning strategy, the weights of the SSD model are initialized using the weights of the CAE counterpart. (3) Data augmentation strategies, such as image mirroring, rotation, Gaussian blur, and Gaussian noise processing, are utilized to augment the training data set. As a case study, aerial images of Hurricane Sandy in 2012 were maximized to validate the proposed method’s effectiveness. Experiments show that the pretraining strategy can improve of 10% in terms of overall accuracy compared with the SSD trained from scratch. These experiments also demonstrate that using data augmentation strategies can improve mAP and mF1 by 72% and 20%, respectively. Finally, the experiment is further verified by another dataset of Hurricane Irma, and it is concluded that the paper method is feasible.

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The Influence Measures of Light Intensity on Machine Learning for Semantic Segmentation

2020 International SoC Design Conference (ISOCC) ◽

10.1109/isocc50952.2020.9333018 ◽

2020 ◽

Author(s):

Cheng-Hsien Chen ◽

Yeong-Kang Lai

Keyword(s):

Machine Learning ◽

Light Intensity ◽

Semantic Segmentation

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Forecasting PV Panel Output Using Prophet Time Series Machine Learning Model

2020 IEEE REGION 10 CONFERENCE (TENCON) ◽

10.1109/tencon50793.2020.9293751 ◽

2020 ◽

Author(s):

Md. Mehedi Hasan Shawon ◽

Sumaiya Akter ◽

Md. Kamrul Islam ◽

Sabbir Ahmed ◽

Md. Mosaddequr Rahman

Keyword(s):

Machine Learning ◽

Time Series ◽

Learning Model ◽

Pv Panel ◽

Machine Learning Model

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Orchard Mapping with Deep Learning Semantic Segmentation

Sensors ◽

10.3390/s21113813 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3813

Author(s):

Athanasios Anagnostis ◽

Aristotelis C. Tagarakis ◽

Dimitrios Kateris ◽

Vasileios Moysiadis ◽

Claus Grøn Sørensen ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Semantic Segmentation ◽

Automated Detection ◽

Aerial Images ◽

Training Dataset ◽

Field Boundary ◽

Different Seasons ◽

Detection And Localization ◽

Different Levels

This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e., different seasons, different tree ages, different levels of weed coverage). The implemented dataset was composed of images from three different walnut orchards. The achieved variability of the dataset resulted in obtaining images that fell under seven different use cases. The best-trained model achieved 91%, 90%, and 87% accuracy for training, validation, and testing, respectively. The trained model was also tested on never-before-seen orthomosaic images or orchards based on two methods (oversampling and undersampling) in order to tackle issues with out-of-the-field boundary transparent pixels from the image. Even though the training dataset did not contain orthomosaic images, it achieved performance levels that reached up to 99%, demonstrating the robustness of the proposed approach.

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Semantic segmentation of PolSAR image data using advanced deep learning model

Scientific Reports ◽

10.1038/s41598-021-94422-y ◽

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.

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Hybrid Multiple Attention Network for Semantic Segmentation in Aerial Images

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2021.3065112 ◽

2021 ◽

pp. 1-18

Author(s):

Ruigang Niu ◽

Xian Sun ◽

Yu Tian ◽

Wenhui Diao ◽

Kaiqiang Chen ◽

...

Keyword(s):

Semantic Segmentation ◽

Aerial Images ◽

Attention Network

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Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning

Energies ◽

10.3390/en14133800 ◽

2021 ◽

Vol 14 (13) ◽

pp. 3800

Author(s):

Sebastian Krapf ◽

Nils Kemmerzell ◽

Syed Khawaja Haseeb Khawaja Haseeb Uddin ◽

Manuel Hack Hack Vázquez ◽

Fabian Netzler ◽

...

Keyword(s):

Deep Learning ◽

System Analysis ◽

State Of The Art ◽

Critical Role ◽

Semantic Segmentation ◽

Energy System ◽

Aerial Images ◽

Potential Analysis ◽

3D Data ◽

Challenges And Opportunities

Roof-mounted photovoltaic systems play a critical role in the global transition to renewable energy generation. An analysis of roof photovoltaic potential is an important tool for supporting decision-making and for accelerating new installations. State of the art uses 3D data to conduct potential analyses with high spatial resolution, limiting the study area to places with available 3D data. Recent advances in deep learning allow the required roof information from aerial images to be extracted. Furthermore, most publications consider the technical photovoltaic potential, and only a few publications determine the photovoltaic economic potential. Therefore, this paper extends state of the art by proposing and applying a methodology for scalable economic photovoltaic potential analysis using aerial images and deep learning. Two convolutional neural networks are trained for semantic segmentation of roof segments and superstructures and achieve an Intersection over Union values of 0.84 and 0.64, respectively. We calculated the internal rate of return of each roof segment for 71 buildings in a small study area. A comparison of this paper’s methodology with a 3D-based analysis discusses its benefits and disadvantages. The proposed methodology uses only publicly available data and is potentially scalable to the global level. However, this poses a variety of research challenges and opportunities, which are summarized with a focus on the application of deep learning, economic photovoltaic potential analysis, and energy system analysis.

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RSNet: Rail semantic segmentation network for extracting aerial railroad images

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210349 ◽

2021 ◽

pp. 1-18

Author(s):

R.S. Rampriya ◽

Sabarinathan ◽

R. Suganya

Keyword(s):

Real Time ◽

Visual Processing ◽

Feature Fusion ◽

Semantic Segmentation ◽

Vital Role ◽

Obstacle Detection ◽

Aerial Images ◽

Computationally Efficient ◽

Fusion Algorithm ◽

Uav Images

In the near future, combo of UAV (Unmanned Aerial Vehicle) and computer vision will play a vital role in monitoring the condition of the railroad periodically to ensure passenger safety. The most significant module involved in railroad visual processing is obstacle detection, in which caution is obstacle fallen near track gage inside or outside. This leads to the importance of detecting and segment the railroad as three key regions, such as gage inside, rails, and background. Traditional railroad segmentation methods depend on either manual feature selection or expensive dedicated devices such as Lidar, which is typically less reliable in railroad semantic segmentation. Also, cameras mounted on moving vehicles like a drone can produce high-resolution images, so segmenting precise pixel information from those aerial images has been challenging due to the railroad surroundings chaos. RSNet is a multi-level feature fusion algorithm for segmenting railroad aerial images captured by UAV and proposes an attention-based efficient convolutional encoder for feature extraction, which is robust and computationally efficient and modified residual decoder for segmentation which considers only essential features and produces less overhead with higher performance even in real-time railroad drone imagery. The network is trained and tested on a railroad scenic view segmentation dataset (RSSD), which we have built from real-time UAV images and achieves 0.973 dice coefficient and 0.94 jaccard on test data that exhibits better results compared to the existing approaches like a residual unit and residual squeeze net.

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