Zobrazeno 1 - 10
of 719
pro vyhledávání: '"training sample"'
Autor:
Zhipeng Cao, Liangcun Jiang, Peng Yue, Jianya Gong, Xiangyun Hu, Shuaiqi Liu, Haofeng Tan, Chang Liu, Boyi Shangguan, Dayu Yu
Publikováno v:
Geo-spatial Information Science, Vol 27, Iss 5, Pp 1489-1508 (2024)
Artificial Intelligence (AI) Machine Learning (ML) technologies, particularly Deep Learning (DL), have demonstrated significant potential in the interpretation of Remote Sensing (RS) imagery, covering tasks such as scene classification, object detect
Externí odkaz:
https://doaj.org/article/35b755753428416f8762e33772c09a68
Autor:
Artem Andreiev, Leonid Artiushyn
Publikováno v:
Радіоелектронні і комп'ютерні системи, Vol 2024, Iss 2, Pp 66-72 (2024)
The subject of this article is land cover classification based on geospatial data. The supervised classification methods are appropriate for most of the thematic tasks of remote sensing because they provide the opportunity to set the characteristics
Externí odkaz:
https://doaj.org/article/089377aed1804c688d6cee5ecf8bbbb2
Publikováno v:
Reports on Geodesy and Geoinformatics, Vol 118, Iss 1 (2024)
The article presents an analysis of the effectiveness of selected machine learning methods: Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM) in the classification of land use and cover in satellite images. Several
Externí odkaz:
https://doaj.org/article/dd9ec86503c1451fa7d29253b7ad9f73
Autor:
Alexey А. Gavrishev
Publikováno v:
Вестник Кемеровского государственного университета. Серия: гуманитарные и общественные науки, Vol 8, Iss 1, Pp 69-74 (2024)
Specialists in technical information security deal with the physical security of various industrial objects. This article introduces a new method for approximating costs of installing a concertina razor wire obstacle. The method can be implemented as
Externí odkaz:
https://doaj.org/article/2241281b80f64fde8ff002ac7380ad87
Publikováno v:
Sensors, Vol 24, Iss 20, p 6571 (2024)
The fractional cover of native grass species (NGS) and noxious weeds (NW) provides a more comprehensive understanding of grassland health in the alpine grasslands. However, coverage extraction of NGS and NW from satellite hyperspectral imagery can be
Externí odkaz:
https://doaj.org/article/247877be3a69478a92a96fdae22e34da
Publikováno v:
Gong-kuang zidonghua, Vol 49, Iss 11, Pp 160-166 (2023)
Traditional object detection algorithms require manual feature extraction when recognizing abnormal behavior of miners queuing, resulting in long detection time and low detection precision. The object detection algorithm based on convolutional neural
Externí odkaz:
https://doaj.org/article/0df57fadd0534c49a9d2554f69e7ff9d
Autor:
Shengzhe Hong, Yu Lou, Xinguo Chen, Quanzhong Huang, Qianru Yang, Xinxin Zhang, Haozhi Li, Guanhua Huang
Publikováno v:
Remote Sensing, Vol 16, Iss 13, p 2274 (2024)
Accurate identification of the spatio-temporal planting structure and analysis of its driving factors in an irrigation district are the important bases for scientific and reasonable utilization of irrigation water resources. In pursuit of this goal,
Externí odkaz:
https://doaj.org/article/b7801a54bc474299a0d84be2164bfcfa
Publikováno v:
Ecological Indicators, Vol 154, Iss , Pp 110904- (2023)
The timely, accurate, and automatic acquisition of land cover (LC) information is a prerequisite for detecting LC dynamics and performing ecological analyses. Cloud computing platforms, such as the Google Earth Engine, have substantially improved the
Externí odkaz:
https://doaj.org/article/d4466d67aa704f39a4a62364339c3091
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Akademický článek
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