Machine learning has been successfully used for object recognition within images. Due to the complexity of the spectrum and texture of construction and demolition waste (C&DW), it is difficult to construct an automatic identification method for C&DW based on machine learning and remote sensing data sources. Machine learning includes many types of algorithms; however, different algorithms and parameters have different identification effects on C&DW. Exploring the optimal method for automatic remote sensing identification of C&DW is an important approach for the intelligent supervision of C&DW. This study investigates the megacity of Beijing, which is facing high risk of C&DW pollution. To improve the classification accuracy of C&DW, buildings, vegetation, water, and crops were selected as comparative training samples based on the Google Earth Engine (GEE), and Sentinel-2 was used as the data source. Three classification methods of typical machine learning algorithms (classification and regression trees (CART), random forest (RF), and support vector machine (SVM)) were selected to classify the C&DW from remote sensing images. Using empirical methods, the experimental trial method, and the grid search method, the optimal parameterization scheme of the three classification methods was studied to determine the optimal method of remote sensing identification of C&DW based on machine learning. Through accuracy evaluation and ground verification, the overall recognition accuracies of CART, RF, and SVM for C&DW were 73.12%, 98.05%, and 85.62%, respectively, under the optimal parameterization scheme determined in this study. Among these algorithms, RF was a better C&DW identification method than were CART and SVM when the number of decision trees was 50. This study explores the robust machine learning method for automatic remote sensing identification of C&DW and provides a scientific basis for intelligent supervision and resource utilization of C&DW.

Ethnic minority architecture is a vital carrier of the cultural heritage of ethnic minorities in China, and its quick and accurate extraction from remote sensing images is highly important for promoting the application of remote sensing information in urban management and architectural heritage protection. Taking Buyi architecture in China as an example, this paper proposes a minority architectural heritage identification method that combines low-altitude unmanned aerial vehicle (UAV) remote sensing technology and an improved deep learning algorithm. First, UAV images are used as the data source to provide high-resolution images for research on ethnic architecture recognition and to solve the problems associated with the high costs, time consumption, and destructiveness of traditional methods for ethnic architecture recognition. Second, to address the lack of edge pixel features in the sample images and reduce repeated labeling of the same sample, the ethnic architecture in entire remote sensing images is labeled on the Arcgis platform, and the sliding window method is used to cut the image data and the corresponding label file with a 10% overlap rate. Finally, an attention mechanism SE module is introduced to improve the DeepLabV3+ network model structure and achieve superior ethnic building recognition results. The experimental data fully show that the model’s accuracy reaches as high as 0.9831, with an excellent recall rate of 0.9743. Moreover, the F1 score is stable at a high level of 0.9787, which highlights the excellent performance of the model in terms of comprehensive evaluation indicators. Additionally, the intersection/union ratio (IoU) of the model is 0.9582, which further verifies its high precision in pixel-level recognition tasks. According to an in-depth comparative analysis, the innovative method proposed in this paper solves the problem of insufficient feature extraction of sample edge pixels and substantially reduces interference from complex environmental factors such as roads, building shadows, and vegetation with the recognition results for ethnic architecture. This breakthrough greatly improves the accuracy and robustness of the identification of architecture in low-altitude remote sensing images and provides strong technical support for the protection and intelligent analysis of architectural heritage.

The change of inland lake area has a great impact on human life, ecological environment and animal and plant habitats. But, the systematic and quantitative research on the long time change of water area and its human activity and climate change causes is very difficult. Here, taking the largest and youngest natural inland freshwater lake Hongjian Nur (HJN) in China as study object, the long-term changes of water surface area of natural lakes and artificial reservoirs were retrieved. The impact of climate change on the change of HJN lake area is analyzed from the point of view of overall meteorological water shortage. The results showed that the natural lake area continued to decrease for 19 years from 1996 to 2015. On the contrary, the area of the reservoirs increased continuously. The determination coefficient of reservoir area and natural lake area is 0.357, which indicates that the impoundment of reservoirs has a significant impact on the decrease area of natural lake. The drought period for 13 years before 2011 also has a direct impact on the water area reduction of the lake. The future 1-2 years drought period or the transition from drought to humidity will threaten the maintenance of the existing water volume. Historical impact of multi-source precipitation changes on the HJN water area proved the important influence of precipitation on the change of lake area. It is concluded that human activities and climate change will be the key constraints for maintaining ecological balance in lakes in the desert.

As a unique cultural heritage of Chinese ethnic minorities, Dong architecture provides rich historical and cultural information. Rapid and accurate extraction of ethnic building information from remote sensing images in complex terrain and high-density settlement environments is highly important for the protection of architectural heritage and the management of rural space. Huanggang Dong Village in Liping County, Guizhou Province, China, is taken as a case study. This paper develops a multifeature fusion machine learning framework for the automatic recognition of Dong ethnic architecture based on centimeter-level visible images captured by UAV. First, the vegetation index, HSI color features and texture features based on the gray level co-occurrence matrix are extracted from the UAV visible light orthophoto image. Through the random forest feature importance ranking and correlation test, six key features, namely, the VDVI, HSI-S, HSI-I, mean, variance and contrast, are selected to construct a multifeature space. This step constitutes the feature construction stage of the proposed methodology and provides the basis for subsequent classification. Second, on the basis of a support vector machine (SVM) and random forest (RF), classification models are constructed. The effects of different feature combinations and different algorithms on classification accuracy are systematically compared, and the results are evaluated in terms of overall accuracy (OA), the kappa coefficient, user accuracy (UA) and producer accuracy (PA). This second part highlights the classification phase of the methodology, which tests the feature space using different algorithms and evaluates the performance of the models. The experimental data fully show that under the condition of a single feature, the SVM model dominated by texture features performs best, with an OA of 85.33% and a kappa of 0.799; under the condition of multifeature fusion, the RF algorithm has a stronger ability to integrate multisource features. The accuracy of building category recognition based on the total feature and dimensionality reduction feature space is particularly prominent. The total feature and overall accuracy reach 89.00%, and the kappa coefficient is 0.850. The UA and PA reached 89.66% and 94.55%, respectively. Through in-depth comparative analysis, the vegetation index–color–texture multifeature fusion and machine learning classification framework based on UAV visible light images can achieve high-precision extraction of Dong architecture without relying on high-cost sensors. It can effectively alleviate the confusion between water bodies and shadows and between dark roofs and vegetation and effectively separate traditional Dong architecture from roads, vegetation and other elements. It provides a low-cost and feasible way for digital archiving, dynamic monitoring and protection management of the traditional village architectural heritage of ethnic minorities.

Studies have shown that a higher GSH level is related to more drug-resistant and invasiveness of a tumor. However, it is a great challenge to accurately imaging the GSH level in vivo, for its imaging intensity will interfered by different accumulation of probes in the tumor. Thus, we hypothesized ratiometric photoacoustic imaging that can be used to predict the drug-resistant and invasiveness of tumors by accurate GSH level imaging. In this study, we synthesized MnO₂/Indocyanine Green (MnO₂/ICG). It can be used as ratiometric photoacoustic (PA) imaging probe, for its absorption at 780 nm (Ab₇₈₀) can be decreased and 680 nm (Ab₆₈₀) remain unchanged upon GSH reduction. And our results confirmed that a lower PA absorption ratio (Ab₇₈₀/Ab₆₈₀) corresponded to a higher GSH level of the tumor. Besides, the near-infrared fluorescence (FI) imaging was used as an assistant, for it can be quenched upon GSH. Therefore, MnO₂/ICG can predict the tumor invasiveness and drug resistance by imaging the GSH level. This study provides reference to predict the prognosis of tumors by imaging the metabolic biomarker of tumors.

Landslide hazard assessment provides an important basis for regional geological disaster prevention and mitigation. Landslide hazard is commonly characterized in terms of three fundamental dimensions: spatial occurrence, temporal occurrence, and potential magnitude. Based on this conceptual framework, Guiyang, a typical mountainous city in western China, was selected as the study area, and slope units were adopted as the basic assessment units. First, eight topographic, geological, and hydrological conditioning factors were incorporated into an information-value model to evaluate the landslide susceptibility of each slope unit. The resulting min–max-normalized susceptibility index was used as the spatial susceptibility component. Second, the historical landslide inventory was combined with kernel density estimation, and a Poisson-based exceedance probability model was used to estimate the model-based exceedance probability of one or more landslides occurring in each slope unit under a 10-year scenario time window. This probability was used as the temporal component. Third, the potential landslide volume of each slope unit was predicted using machine-learning algorithms. The predicted small-, medium-, and large-volume classes were assigned ordinal magnitude weights of 1, 2, and 3, respectively, and were used as the magnitude component. Finally, the three components were integrated using a multiplicative composite index to obtain the relative landslide hazard of each slope unit. The results were as follows. The overall landslide hazard in Guiyang was dominated by medium and low hazard levels. Furthermore, 422 high-hazard slope units were identified, accounting for 15.58% of all slope units. These units were mainly concentrated in the central and northern regions. In terms of administrative divisions, Xifeng County, Kaiyang County, Xiuwen County, Qingzhen city, and Huaxi district ranked among the top five administrative regions with the most high-hazard slope units. Therefore, these regions should be considered key areas for disaster prevention and mitigation. Based on the hazard zonation, 21 selected high-hazard slopes were examined using UAV imagery and on-site inspection to document their representative geomorphological and deformation characteristics. The observations provided qualitative field support for the geological plausibility of selected high-hazard predictions. The findings of this study provide an important decision-making basis for landslide mitigation efforts by the Guiyang Municipal Government.