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This book puts forward the concept of “subjective anthropology” and outlines a theoretical system that will allow subjective anthropology to qualify as a new academic discipline in its own right. In an effort to respond to the field’s proper role as the science of humanity, subjective analysis has been introduced into the study of anthropology. The book fills two distinct gaps in our knowledge and understanding of modern man, offering detailed descriptions of personality and of groups, while also advancing the theory of “structure and choice.” The book formulates seven basic principles of subjective anthropology and divides anthropology into three major branches: subjective anthropology, cultural anthropology, and biological (or physical) anthropology, which can be further divided into sub-branches. The book pursues three key goals: advancing and developing the theoretical system of subjective anthropology, reconstructing the discipline of anthropology, and establishing a Chinese anthropology with Chinese characteristics, Chinese visions, and Chinese styles.
Philosophical anthropology. --- Anthropology. --- Anthropology—Research. --- Ethnology. --- Anthropological Theory. --- Research Methods in Anthropology. --- Sociocultural Anthropology. --- Philosophy of Anthropology. --- Cultural anthropology --- Ethnography --- Races of man --- Social anthropology --- Anthropology --- Human beings --- Primitive societies --- Anthropology, Philosophical --- Man (Philosophy) --- Civilization --- Life --- Ontology --- Humanism --- Persons --- Philosophy of mind --- Philosophy --- Social sciences --- Philosophy.
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High resolution datasets of population density which accurately map sparsely distributed human populations do not exist at a global scale. Typically, population data is obtained using censuses and statistical modeling. More recently, methods using remotely-sensed data have emerged, capable of effectively identifying urbanized areas. Obtaining high accuracy in estimation of population distribution in rural areas remains a very challenging task due to the simultaneous requirements of sufficient sensitivity and resolution to detect very sparse populations through remote sensing as well as reliable performance at a global scale. Here, the authors present a computer vision method based on machine learning to create population maps from satellite imagery at a global scale, with a spatial sensitivity corresponding to individual buildings and suitable for global deployment.
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