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With the rapid development of related technologies in the field of natural language processing, as an upstream task of natural language processing, improving the accuracy of named entity recognition is of great significance for subsequent text processing tasks. However, due to the differences between Chinese and English languages, it is difficult to transfer the research results of English named entity recognition into Chinese research effectively. Therefore, the key issues in the current research of Chinese named entity recognition are analyzed from the following four aspects: Firstly, the development of named entity recognition is taken as the main clue, the advantages and disadvantages, common methods and research results of each stage are comprehensively discussed. Secondly, the Chinese text preprocessing methods are summarized from the perspective of sequence annotation, evaluation index, Chinese word segmentation methods and datasets. Then, aiming at the Chinese character and word feature fusion method, the current research is summarized from the perspective of character fusion and word fusion, and the optimization direction of the current Chinese named entity recognition model is discussed. Finally, the practical applications of Chinese named entity recognition in various fields are analyzed. This paper discusses the current research on Chinese named entity recognition, aiming to help researchers understand the research direction and significance of this task more comprehensively, so as to provide a certain reference for proposing new methods and new improvements.

Coal is one of the most important fossil energy sources and is ensuring global energy security. Annual maximum NDVI (Normalized Difference Vegetation Index) data is an important indicator for the research in balancing coal mining and vegetation conservation. However, the existing annual maximum NDVI data displayed lower values with temporally inconsistent and a noticeable mosaic line. Here we propose an algorithm for automatically generating the annual maximum NDVI of China's coal bases in Google Earth Engine called: Auto-NDVIcb. The accuracy of the Auto-NDVIcb algorithm has been verified with an average RMSE of 0.087 for the 14 coal bases from 2013 to 2022. Based on the proposed Auto-NDVIcb algorithm, an annual maximum NDVI dataset for all 14 coal bases in China from 2013 to 2022 was publicly released. This dataset can be fast and automatically updated online. Hence, the public dataset will continuously serve to monitor the vegetation change induced by coal mining, exploring the mechanism of vegetation degradation, and providing scientific data for developing vegetation protection policies in coal mines. [ABSTRACT FROM AUTHOR]

This document is an editorial from the journal "Remote Sensing" titled "Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities." It discusses the importance of monitoring environmental changes caused by human activities, such as urban expansion, agriculture, deforestation, and mining. The editorial highlights the value of satellite and airborne remote sensing, as well as geospatial approaches, in studying the impact of human activities on the environment. The document provides an overview of 12 published manuscripts that cover various ecological scenarios and human activities, offering new theories, methods, and findings in the field. The editorial concludes by suggesting the need for further research on different types of ecological environments and human activities, as well as improving the accuracy of analyzing the combined effects of natural disturbances and human alterations on the environment. [Extracted from the article]

To practice the development concept of "lucid waters and lush mountains are invaluable assets", and balance mineral development and ecological environment protection, it is urgent to carry out dynamic monitoring and scientific evaluation of the ecological environment in mining areas.Based on the characteristics of the ecological environment in mining areas, this paper analyzes the temporal and spatial change characteristics and differences of the ecological environment elements in the mining area, the impact mechanism of the mining and restoration activities on the ecological environment elements, and the cooperative evolution law of each element.Guided by the requirements on ecological environment monitoring and evaluation in mining areas in the new era, a technology framework for quantitative remote sensing-based monitoring and evaluation of the ecological environment in mining areas is proposed from the perspective of 'data-monitoring-evaluation-application'.The framework makes full use of multi-source big data in mining areas with remote sensing images as the main body and takes advantage of the emerging technologies such as artificial intelligence and quantitative remote sensing.This research archives quantitative monitoring and evaluation of the ecological environment elements in the mining areas with the features of high frequency, large-extent, long-term, continuous, all-factor observation and quantitative inversion, which can support the operational applications including mining activity monitoring, ecological environment diagnosis and early warning and restoration effect evaluation in mining areas.Finally, two real-world cases are introduced to illustrate the effectiveness and application flow of the proposed framework.