Zobrazeno 1 - 10
of 898
pro vyhledávání: '"Land use classification"'
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
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
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:
Land, Vol 13, Iss 9, p 1393 (2024)
In the face of persistent global environmental challenges, evaluating ecological environment quality and understanding its driving forces are crucial for maintaining the ecological balance and achieving sustainable development. Based on a case study
Externí odkaz:
https://doaj.org/article/f681cc5acd654fca835a29cc35b54682
Publikováno v:
Applied Sciences, Vol 14, Iss 16, p 7235 (2024)
In the context of accelerated urbanization, assessing the quality of the existing built environment plays a crucial role in urban renewal. In the existing research and use of deep learning models, most categories are urban construction areas, forest
Externí odkaz:
https://doaj.org/article/894ad46c91d14edb9c2299c481005d66
Autor:
Dario Perregrini, Vittorio Casella
Publikováno v:
Remote Sensing, Vol 16, Iss 13, p 2273 (2024)
The past decade has seen remarkable advancements in Earth observation satellite technologies, leading to an unprecedented level of detail in satellite imagery, with ground resolutions nearing an impressive 30 cm. This progress has significantly broad
Externí odkaz:
https://doaj.org/article/53cb10ec9038462ca85e53c1b539d4c3