Transformer-Based Semantic Segmentation for Extraction of Building Footprints from Very-High-Resolution Images.

Semantic segmentation with deep learning networks has become an important approach to the extraction of objects from very high-resolution remote sensing images. Vision Transformer networks have shown significant improvements in performance compared to traditional convolutional neural networks (CNNs) in semantic segmentation. Vision Transformer networks have different architectures to CNNs. Image patches, linear embedding, and multi-head self-attention (MHSA) are several of the main hyperparameters. How we should configure them for the extraction of objects in VHR images and how they affect the accuracy of networks are topics that have not been sufficiently investigated. This article explores the role of vision Transformer networks in the extraction of building footprints from very-high-resolution (VHR) images. Transformer-based models with different hyperparameter values were designed and compared, and their impact on accuracy was analyzed. The results show that smaller image patches and higher-dimension embeddings result in better accuracy. In addition, the Transformer-based network is shown to be scalable and can be trained with general-scale graphics processing units (GPUs) with comparable model sizes and training times to convolutional neural networks while achieving higher accuracy. The study provides valuable insights into the potential of vision Transformer networks in object extraction using VHR images.

Satellites reveal hotspots of global river extent change.

Rivers are among the most diverse, dynamic, and productive ecosystems on Earth. River flow regimes are constantly changing, but characterizing and understanding such changes have been challenging from a long-term and global perspective. By analyzing water extent variations observed from four-decade Landsat imagery, we here provide a global attribution of the recent changes in river regime to morphological dynamics (e.g., channel shifting and anabranching), expansion induced by new dams, and hydrological signals of widening and narrowing. Morphological dynamics prevailed in ~20% of the global river area. Booming reservoir constructions, mostly skewed in Asia and South America, contributed to ~32% of the river widening. The remaining hydrological signals were characterized by contrasting hotspots, including prominent river widening in alpine and pan-Arctic regions and narrowing in the arid/semi-arid continental interiors, driven by varying trends in climate forcing, cryospheric response to warming, and human water management. Our findings suggest that the recent river extent dynamics diverge based on hydroclimate and socio-economic conditions, and besides reflecting ongoing morphodynamical processes, river extent changes show close connections with external forcings, including climate change and anthropogenic interference.

Variance Analysis in China's Coal Mine Accident Studies Based on Data Mining.

The risk of coal mine accidents rises significantly with mining depth, making it urgent for accident prevention to be supported by both scientific analysis and advanced technologies. Hence, a comprehensive grasp of the research progress and differences in hotspots of coal mine accidents in China serves as a guide to find the shortcomings of studies in the field, promote the effectiveness of coal mine disaster management, and enhance the prevention and control ability of coal mine accidents. This paper analyzes Chinese and foreign literature based on data mining algorithms (LSI + Apriori), and the findings indicate that: (1) 99% of the available achievements are published in Chinese or English-language journals, with the research history conforming to the stage of Chinese coal industry development, which is characterized by "statistical description, risk evaluation, mechanism research, and intelligent reasoning". (2) Chinese authors are the primary contributors that lead and contribute to the continued development of coal mine accident research in China globally. Over 81% of the authors and over 60% of the new authors annually are from China. (3) The emphasis of the Chinese and English studies is different. Specifically, the Chinese studies focus on the analysis of accident patterns and causes at the macroscale, while the English studies concentrate on the occupational injuries of miners at the small-scale and the mechanism of typical coal mine disasters (gas and coal spontaneous combustion). (4) The research process in Chinese is generally later than that in English due to the joint influence of the target audience, industrial policy, and scientific research evaluation system. After 2018, the Chinese studies focus significantly on AI technology in deep mining regarding accident rules, regional variation analysis, risk monitoring and early warning, as well as knowledge intelligence services, while the hotspots of English studies remain unchanged. Furthermore, both Chinese and English studies around 2019 focus on "public opinion", with Chinese ones focusing on serving the government to guide the correct direction of public opinion while English studies focus on critical research of news authenticity and China's safety strategy.

A novel rapid web investigation method for ecological agriculture patterns in China.

The investigation of Ecological Agriculture (EA) patterns can reveal the differences, aggregation, and diversity of agricultural development, providing specific paths in agricultural development and environmental protection to achieve the Sustainable Development Goals. Although field surveys, literature analysis, and the method using administrative statistics can be employed to investigate EA records and determine EA distributions comprehensively, they still rely on manual operations that are generally unable to support the rapid and large-scale identification of EA patterns required by current agricultural sustainable researches. To address this issue, this paper proposes a novel and rapid approach for Ecological Agriculture Pattern Investigation Based on Web-text (WEAPI), with the ability to automatically acquire EA pattern records, including pattern type, occurrence time, precise location, and other relevant information. The proposed method is employed in a national-scale case study to investigate trends in Chinese Ecological Agriculture (CEA). Results of the study reveal WEAPI's ability to detect new trends in CEA via the latest news and the corresponding distributions. The WEAPI method can also exhibit the unknown patterns of the current Chinese agricultural development. Further validation experiments demonstrate that the proposed method achieves over 95 % precision in the pattern parse processes and an 87 % coverage rate at the town level of the official CEA pattern list. Moreover, WEAPI can provide dynamic changing analyses on the annual evolution of the EA patterns in each type. Despite limitations under sparse records in partial classes, the results reveal WEAPI as a promising and powerful tool for agricultural research and agricultural development planning.

Biological nitrogen removal capability and pathways analysis of a novel low C/N ratio heterotrophic nitrifying and aerobic denitrifying bacterium (Bacillus thuringiensis strain WXN-23).

A novel heterotrophic nitrification and aerobic denitrification (HNAD) bacteria, identified as Bacillus thuringiensis strain WXN-23, was isolated from husk feed filtrate of a pig farm. It was the first report of Bacillus thuringiensis with the capability for HNAD and could adapt to the condition of low Carbon/Nitrogen (C/N) ratio. Nitrogen could be efficiently removed by the strain WXN-23 in simulated wastewater, be it in single or mixed form nitrogen sources. The nitrogen balance revealed that 63.5% of the initial nitrogen (5.32 mg) was lost in the form of N2. The conditions for maximum total nitrogen (TN) removal efficiency (95.996%) were shaking speed of 126.89 r/min, a carbon C/N ratio of 5.91, the temperature of 32.81 °C, and a pH value of 8.17. The nitrification-denitrification metabolic pathway (NH4+-N→NH2OH→NO2--N→NO3--N→NO2--N→NO→N2O→N2) under aerobic conditions was determined on the basic of characteristic of N removal, N balance analysis, enzyme assay and functional genes amplification results. Strain WXN-23 was effective at wastewater treatment, with TN, NH4+-N, NO3--N and NO2--N removal efficiencies of 82.12%, 86.74%, 90.74% and 100%, respectively.

Modelling and Analysis of Hydrodynamics and Water Quality for Rivers in the Northern Cold Region of China.

In this study, the Mudan River, which is the most typical river in the northern cold region of China was selected as the research object; Environmental Fluid Dynamics Code (EFDC) was adopted to construct a new two-dimensional water quality model for the urban sections of the Mudan River, and concentrations of COD(Cr) and NH3N during ice-covered and open-water periods were simulated and analyzed. Results indicated that roughness coefficient and comprehensive pollutant decay rate were significantly different in those periods. To be specific, the roughness coefficient in the ice-covered period was larger than that of the open-water period, while the decay rate within the former period was smaller than that in the latter. In addition, according to the analysis of the simulated results, the main reasons for the decay rate reduction during the ice-covered period are temperature drop, upstream inflow decrease and ice layer cover; among them, ice sheet is the major contributor of roughness increase. These aspects were discussed in more detail in this work. The model could be generalized to hydrodynamic water quality process simulation researches on rivers in other cold regions as well.

Evaluation of agricultural nonpoint source pollution potential risk over China with a Transformed-Agricultural Nonpoint Pollution Potential Index method.

Agricultural nonpoint source (NPS) pollution has been the most important threat to water environment quality. Understanding the spatial distribution of NPS pollution potential risk is important for taking effective measures to control and reduce NPS pollution. A Transformed-Agricultural Nonpoint Pollution Potential Index (T-APPI) model was constructed for evaluating the national NPS pollution potential risk in this study; it was also combined with remote sensing and geographic information system techniques for evaluation on the large scale and at 1 km2 spatial resolution. This model considers many factors contributing to the NPS pollution as the original APPI model, summarized as four indicators of the runoff, sediment production, chemical use and the people and animal load. These four indicators were analysed in detail at 1 km2 spatial resolution throughout China. The T-APPI model distinguished the four indicators into pollution source factors and transport process factors; it also took their relationship into consideration. The studied results showed that T-APPI is a credible and convenient method for NPS pollution potential risk evaluation. The results also indicated that the highest NPS pollution potential risk is distributed in the middle-southern Jiangsu province. Several other regions, including the North China Plain, Chengdu Basin Plain, Jianghan Plain, cultivated lands in Guangdong and Guangxi provinces, also showed serious NPS pollution potential. This study can provide a scientific reference for predicting the future NPS pollution risk throughout China and may be helpful for taking reasonable and effective measures for preventing and controlling NPS pollution.

A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery.

Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover based on satellite data is a challenging task, and an efficient classification method is needed. In this study, an automatic scheme is proposed for the classification of land use using multispectral remote sensing images based on change detection and a semi-supervised classifier. The satellite image can be automatically classified using only the prior land cover map and existing images; therefore human involvement is reduced to a minimum, ensuring the operability of the method. The method was tested in the Qingpu District of Shanghai, China. Using Environment Satellite 1(HJ-1) images of 2009 with 30 m spatial resolution, the areas were classified into five main types of land cover based on previous land cover data and spectral features. The results agreed on validation of land cover maps well with a Kappa value of 0.79 and statistical area biases in proportion less than 6%. This study proposed a simple semi-automatic approach for land cover classification by using prior maps with satisfied accuracy, which integrated the accuracy of visual interpretation and performance of automatic classification methods. The method can be used for land cover mapping in areas lacking ground reference information or identifying rapid variation of land cover regions (such as rapid urbanization) with convenience.