![]() In this paper, a two-step terrain simulation method based on sparse and discrete river sections is proposed by comprehensively considering the river trends and the lack of monitoring sections. When studying river hydrodynamics and water quality evolution laws on the basis of numerical simulation analysis, it is necessary to carry out topographic interpolation along the bend direction of the river on the basis of the measured river section, as this can provide accurate and reliable topographic data for river numerical modeling. With the development of society, the influence of economic interests will become more and more prominent. (4) The land use change in Pyongyang is mainly driven by the three aspects of consumer demand, economic benefits and government decision-making. (3) In 2020, cultivated land in Pyongyang is still the main type of land use, but the area will be significantly reduced, and the artificial surface will continue to expand, becoming the main land area for area transfer. (2) In the first decade of 21st Century, the comprehensive utilization of land use in Pyongyang increased, the information entropy declined weakly, and the land use system remained in a stable and orderly state. Finally, the paper summarizes representative applications of disentangled representation learning in the field of remote sensing and discusses its future development.Using Global Land30 standard product as data source, combined with CA-Markov model to quantitatively study the land use evolution of Pyongyang municipality in North Korea from 2000 to 2010 and simulate the land use situation of the region in 2020, the research found: (1) From 2000 to 2010, the area of forest land and bare land in Pyongyang was greatly reduced, and the cultivated land, grassland, water body and artificial surface increased to varying degrees, and the expansion of cultivated land area was the most significant. ![]() Subsequently, the loss functions and objective evaluation metrics commonly used in existing work on disentangled representation are classified. Then, disentangled representation learning algorithms are classified into four categories and outlined in terms of both mathematical description and applicability. ![]() In this paper, we first introduce and analyze the current status of research on disentangled representation and its causal mechanisms and summarize three crucial properties of disentangled representation. Disentangled representation learning can capture information about a single change factor and control it by the corresponding potential subspace, providing a robust representation for complex changes in the data. Disentangled representation learning aims to learn a low-dimensional interpretable abstract representation that can identify and isolate different potential variables hidden in the high-dimensional observations. However, the current representations are usually highly entangled, i.e., all information components of the input data are encoded into the same feature space, thus affecting each other and making it difficult to distinguish. The transition of input representations for machine learning algorithms from handcraft features, which dominated in the past, to the potential representations learned through deep neural networks nowadays has led to tremendous improvements in algorithm performance. Representation learning is one of the core problems in machine learning research.
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