scholarly journals Lateral Localization via LIDAR-Based Road Boundary Extraction on Community Roads

2020 ◽  
Vol 11 (3) ◽  
pp. 116-123
Author(s):  
Wataru Furuse ◽  
Takuma Ito ◽  
Kyoichi Tohriyama ◽  
Minoru Kamata
Keyword(s):  
Boundary Extraction ◽  
Lateral Localization

Automated 3D Road Boundary Extraction and Vectorization Using MLS Point Clouds

2021 ◽  
pp. 1-11
Author(s):  
Xiaoxin Mi ◽  
Bisheng Yang ◽  
Zhen Dong ◽  
Chi Chen ◽  
Jianxiang Gu
Keyword(s):  
Point Clouds ◽  
Boundary Extraction

scholarly journals Automatic Boundary Extraction for Photovoltaic Plants Using the Deep Learning U-Net Model

2021 ◽  
Vol 11 (14) ◽  
pp. 6524
Author(s):  
Andrés Pérez-González ◽  
Álvaro Jaramillo-Duque ◽  
Juan Bernardo Cano-Quintero
Keyword(s):  
Research Work ◽  
Solar Photovoltaic ◽  
Boundary Extraction ◽  
Pv Systems ◽  
Periodic Maintenance ◽  
Planning Algorithm ◽  
Extraction Processes ◽  
Inspection And Maintenance ◽  
Photovoltaic Plants ◽  
Path Planning Algorithm

Nowadays, the world is in a transition towards renewable energy solar being one of the most promising sources used today. However, Solar Photovoltaic (PV) systems present great challenges for their proper performance such as dirt and environmental conditions that may reduce the output energy of the PV plants. For this reason, inspection and periodic maintenance are essential to extend useful life. The use of unmanned aerial vehicles (UAV) for inspection and maintenance of PV plants favor a timely diagnosis. UAV path planning algorithm over a PV facility is required to better perform this task. Therefore, it is necessary to explore how to extract the boundary of PV facilities with some techniques. This research work focuses on an automatic boundary extraction method of PV plants from imagery using a deep neural network model with a U-net structure. The results obtained were evaluated by comparing them with other reported works. Additionally, to achieve the boundary extraction processes, the standard metrics Intersection over Union (IoU) and the Dice Coefficient (DC) were considered to make a better conclusion among all methods. The experimental results evaluated on the Amir dataset show that the proposed approach can significantly improve the boundary and segmentation performance in the test stage up to 90.42% and 91.42% as calculated by IoU and DC metrics, respectively. Furthermore, the training period was faster. Consequently, it is envisaged that the proposed U-Net model will be an advantage in remote sensing image segmentation.

Patterns of Cognitive Test Performance As Functions of the Lateral Localization of Cerebral Abnormalities in the Temporal Lobe

1957 ◽  
Vol 103 (433) ◽  
pp. 758-772 ◽  
Author(s):  
Victor Meyer ◽  
H. Gwynne Jones
Keyword(s):  
Test Performance ◽  
Right Hemisphere ◽  
Cognitive Test ◽  
Temporal Relationships ◽  
Verbal Tasks ◽  
Specific Locus ◽  
Left And Right ◽  
Spatio Temporal ◽  
Cognitive Test Performance ◽  
Lateral Localization

Various investigations into the effects of brain injury on psychological test performance (Weisenburg and McBride, 1935; Patterson and Zangwill, 1944; Anderson, 1951; McFie and Piercy, 1952; Bauer and Becka, 1954; Milner, 1954) suggest the overall conclusion that patients with left hemisphere lesions are relatively poor at verbal tasks, while those with right-sided lesions do worst at practical tasks, particularly the manipulation of spatial or spatio-temporal relationships. Heilbfun's (1956) study confirmed that verbal deficits result from left-sided lesions but his left and right hemisphere groups produced almost identical scores on spatial tests. In so far as these workers paid attention to the specific sites of the lesions, their findings indicate that the pattern of test performance is a function of the hemisphere in which the lesion occurs rather than of its specific locus.

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