There is a growing interest in the interoperability between building information models (BIM) and geographic information systems (GIS). BIM can provide rich information for the generation of a GIS model, while the analysis tools in GIS can be used by BIM applications, such as the management of material supply chains and site selection. A comprehensive integration of the BIM and GIS is critical for interoperability between the two domains. However, many integration methods including manual inspection of complex and heterogeneous model files are time-consuming and have weak generalisation capability. This paper proposes a new linguistic-based semantic mapping method that uses the letter trigram-based word hashing method (WHSMM) to construct the feature vector, and then maps the Industry Foundation Classes (IFC) and City Geography Markup Language (CityGML) schemas through text mining techniques at schema level. The semantic mapping precision and recall obtained using WHSMM are compared with those of another linguistic-based method. The mapping result shows that performance of WHSMM is superior to that of another method in most cases. Therefore, the mapping candidates of WHSMM are used to transform the IFC datasets into CityGML models. The transformation results show that most of mapping candidates of the WHSMM can be used to practical data integration. The semantic mapping method presented in this paper can help map the IFC and CityGML schemas by effectively reducing the search space of the manual mapping method.

Measuring maize grain moisture content (GMC) variability at maturity provides an essential piece of information for the formulation of maize harvesting sequences and the applications of precision agriculture. Canopy chlorophyll content (CCC) is an important parameter that describes crop growth, photosynthetic rate, health, and senescence. The main goal of this study was to estimate maize GMC at maturity through CCC retrieved from multi-spectral UAV images using a PROSAIL model inversion and compare its performance with GMC estimation through simple vegetation indices (VIs) approaches. This study was conducted in two separate maize fields of 50.3 and 56 ha located in Hailun County, Heilongjiang Province, China. Each of the fields was cultivated with two maize varieties. One field was used as reference data for constructing the model, and the other field was applied to validate. The leaf chlorophyll content (LCC) and leaf area index (LAI) of maize were collected at three critical stages of crop growth, and meanwhile, the GMC of maize at maturity was also obtained. During the collection of field data, a UAV flight campaign was performed to obtain multi-spectral images from two fields at three main crop growth stages. In order to calibrate and evaluate the PROSAIL model for obtaining maize CCC, crop canopy spectral reflectance was simulated using crop-specific parameters. In addition, various VIs were computed from multi-spectral images to estimate maize GMC at maturity and compare the results with CCC estimations. When the CCC-retrieved results were compared to measured data, the R² value was 0.704, the RMSE was 34.58 μg/cm², and the MAE was 26.27 μg/cm². The estimation accuracy of the maize GMC based on the normalized red edge index (NDRE) was demonstrated to be the greatest among the selected VIs in both fields, with R² values of 0.6 and 0.619, respectively. Although the VIs of UAV inversion GMC accuracy are lower than those of CCC, their rapid acquisition, high spatial and temporal resolution, suitability for empirical models, and capture of growth differences within the field are still helpful techniques for field-scale crop monitoring. We found that maize varieties are the main reason for the maturity variation of maize under the same geographical and environmental conditions. The method described in this article enables precision agriculture based on UAV remote sensing by giving growers a spatial reference for crop maturity at the field scale.

From 2020 to 2022, hydrological extremes such as severe floods and droughts occurred successively in Jiujiang city, Poyang Lake Basin, posing a threat to regional water quality safety. The chlorophyll-a (Chl-a) concentration is a key indicator of river eutrophication. Until now, there has been a lack of empirical research exploring the Chl-a trend in inland water in Jiujiang in the context of hydrological extremes. In this study, Sentinel-2 satellite remote sensing data sourced from the Google Earth Engine (GEE) cloud platform, along with hourly water quality data collected from monitoring stations in Jiujiang city, Jiangxi Province, China, are utilized to develop a quantitative inversion model for the Chl-a concentration. The Chl-a concentrations for various inland water types were estimated for each quarter from 2020 to 2022, and the spatiotemporal distribution was analyzed. The main findings are as follows: (1) the quantitative inversion model for the Chl-a concentration was validated via in situ measurements, with a coefficient of determination of 0.563; (2) the spatial estimates of the Chl-a concentration revealed a slight increasing trend, increasing by 0.1193 μg/L from 2020 to 2022, closely aligning with the monitoring-station data; (3) an extreme drought in 2022 led to less water in inland water bodies, and consequently, the Chl-a concentration displayed a significant upward trend, especially in Poyang Lake, where the mean Chl-a concentration increased by approximately 1 μg/L from Q1 to Q2 in 2022. These findings revealed the seasonal changes in the Chl-a concentrations in inland waters in the context of extreme hydrological events, thus providing valuable information for the sustainable management of water quality in Jiujiang city.

Economic well-being is an important indicator for measuring the happiness of national residents and is of great significance for poverty assessment in the United Nations 2030 Sustainable Development Goals. Since economic well-being is a multidimensional indicator involving multiple aspects of economic development level and individual residents' perception, its influencing factors are complex and lack empirical research. In order to explore the influencing factors of economic well-being, this study proposed an economic well-being factor analysis framework combining nighttime light remote sensing and household survey data. Taking Bazhou City, Hebei Province, China as an example, a household survey of economic well-being indicators was conducted in towns, and the spatial distribution feature of economic well-being in each town were statistically analyzed. Further, the Sustainable Development Satellite (SDGSAT-1) nighttime light remote sensing data and socioeconomic statistical data were used to conduct an analysis of the influencing factors of economic well-being. The results showed that: (1) there is spatial heterogeneity in economic well-being among towns, among which Dongduan has the highest economic well-being and Wangzhuangzi has the lowest economic well-being. The average economic well-being value of Bazhou City is 9.04; (2) A preliminary analysis of economic well-being and nighttime light remote sensing feature shows that the economic well-being of Dongduan and Tang'erli and other towns are consistent with the nighttime light remote sensing feature, but not in Bazhou and Shengfang. This indicates that the impact of economic well-being is multi-factorial, and there is no significant relationship between economic development level and economic well-being in local scale areas. This study is the first to use nighttime light remote sensing data and household survey data to analyze the factors affecting socioeconomic well-being, providing important support for subsequent large-scale global socioeconomic well-being modeling and poverty assessment.