Zobrazeno 1 - 10
of 916
pro vyhledávání: '"Land use classification"'
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
Urban land use information can be effectively extracted from high-resolution satellite images for many urban applications. A significant challenge remains the accurate partition of fine-grained land-use units from these images. This paper presents a
Externí odkaz:
https://doaj.org/article/3b9e5df7a447446e90f7c23b0ce726b8
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
Mapping Land Use (LU) is crucial for monitoring and managing the dynamic evolution of the human activities of a given area and their consequential environmental impacts. In this study, a multimodal machine learning framework, using the XGBoost classi
Externí odkaz:
https://doaj.org/article/50bf250f14304c9e8793dc8541e4541d
Publikováno v:
Geo-spatial Information Science, Pp 1-17 (2024)
To enhance the accuracy of land use classification in mining areas, the Object-based Convolutional Neural Network (OCNN) method has been widely used. However, existing researches tend to neglect the importance of decision-level fusion, focusing only
Externí odkaz:
https://doaj.org/article/aec18898bc35477e89d1f34a06259a1e
Autor:
Anila Kausar, Salman Zubair, Hadeeqa Sohail, Muhammad Mushahid Anwar, Asad Aziz, Sergij Vambol, Viola Vambol, Nadeem A. Khan, Serhii Poteriaiko, Vasyl Tyshchenko, Rustam Murasov, Fizza Ejaz, Owais Iqbal Khan
Publikováno v:
Discover Sustainability, Vol 5, Iss 1, Pp 1-20 (2024)
Abstract Introduction Modern development is patented by rapid urbanization, which largely negatively affects the quality of life. Over the past few decades in the World; in the field of urban planning and the real estate market, Mixed-use development
Externí odkaz:
https://doaj.org/article/f113bc7633d04b8aa93f6402253e9b74
Autor:
Harold N. Eyster, Brian Beckage
Publikováno v:
PeerJ Computer Science, Vol 10, p e2003 (2024)
Land use and land cover (LULC) classification is becoming faster and more accurate thanks to new deep learning algorithms. Moreover, new high spectral- and spatial-resolution datasets offer opportunities to classify land cover with greater accuracy a
Externí odkaz:
https://doaj.org/article/b5387ba34a004abd9aada19fac0f8d26
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 10051-10066 (2024)
Current land use classification models based on very high-resolution (VHR) remote sensing images often suffer from high sample dependence and poor transferability. To address these challenges, we propose an unsupervised multisource domain adaptation
Externí odkaz:
https://doaj.org/article/0743c9a63aaa46ad980407c015c23355
Autor:
Mohammed Aljebreen, Hanan Abdullah Mengash, Mohammad Alamgeer, Saud S. Alotaibi, Ahmed S. Salama, Manar Ahmed Hamza
Publikováno v:
IEEE Access, Vol 12, Pp 11147-11156 (2024)
Currently, remote sensing images (RSIs) are often exploited in the explanation of urban and rural areas, change recognition, and other domains. As the majority of RSI is high-resolution and contains wide and varied data, proper interpretation of RSIs
Externí odkaz:
https://doaj.org/article/acb525c16ddc42ecbb974c3d01fd670c
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 13, Iss 11, p 378 (2024)
Urban land use classification plays a significant role in urban studies and provides key guidance for urban development. However, existing methods predominantly rely on either raster structure deep features through convolutional neural networks (CNNs
Externí odkaz:
https://doaj.org/article/df5382ac178b40f28edc5348cc34dcc5
Publikováno v:
Agriculture, Vol 14, Iss 10, p 1693 (2024)
Accurate monitoring of irrigation areas is of great significance to ensure national food security and rational utilization of water resources. The low resolution of the Moderate Resolution Imaging Spectroradiometer and Landsat data makes the monitori
Externí odkaz:
https://doaj.org/article/904346f260f248139da3a0f539e1928d