Evaluating and exploring regional eco-environmental quality (EEQ), economic development equality (EDE) and the coupling coordination degree (CCD) at multiple scales is important for realizing regional sustainable development goals. The CCD can reflect both the development level and the interaction relationship of two or more systems. However, relevant previous studies have ignored non-statistical data, lacked multiscale analyses, misused the coupling coordination degree model or have not sufficiently considered economic development equality. In response to these problems, this study integrated multisource remote sensing datasets to calculate and analyse the remote sensing ecological index (RSEI) and then used nighttime light data and population density data to calculate the proposed nighttime difference index (NTDI). Next, a modified coupling coordination degree (MCCD) index was proposed to analyse the MCCD between EEQ and EDE. Then, spatiotemporal and multiscale analyses at the county, city, province, urban agglomeration and country levels were performed. Global and local spatial autocorrelation and trend analyses were performed to evaluate the spatial aggregation degree and change trends from 2001 to 2020. The main conclusions are as follows: (1) The EEQ of China displayed a fluctuating upwards trend (0.0048 a⁻¹), with average RSEI values of 0.5950, 0.6277, 0.6164, 0.6311 and 0.6173; the EDE of China showed an upwards trend (0.0298 a⁻¹), with average NTDI values of 0.1271, 0.1635, 0.1642, 0.2181 and 0.2490; and China’s MCCD indicated an upwards trend (0.0220 a⁻¹), with values of 0.4614, 0.5027, 0.4978, 0.5401 and 0.5525. (2) The highest global Moran’s I of NTDI and MCCD was achieved at the city scale, while the highest RSEI was achieved at the county scale. From 2001 to 2020, the spatial agglomeration effect of the RSEI decreased, while that of the NTDI and MCCD increased. (3) A power function relationship occurred between NTDI and MCCD at different scales. Furthermore, the NTDI had a higher contribution to improving the MCCD than the RSEI and the R² of the fitted curve at different scales ranged from 0.8183 to 0.9915.

Landslidesusceptibility mapping (LSM) is the primary link of geological disaster risk evaluation, which is significant for postearthquake emergency response and rebuilding after disasters. Existing LSM studies applying deep learning (DL) methods have shortcomings such as easy overfitting, slow convergence, and insufficient hyperparametric optimization. In response to these problems, this study proposes an ensemble model based on ant colony optimization strategy and deep belief network (ACO-DBN). In ACO-DBN, DL optimization strategies were added to DBN and their combined parameters were optimized with ACO. Next, Pearson's correlation coefficient and random forest importance ranking methods were utilized to optimize landslide causative factors. Then, the Jiuzhaigou earthquake region was selected as an example to explore the applicability of this model. Besides, we conducted the Wilcoxon signed rank test in order to verify that the differences were statistically significant. In a comprehensive comparative all indexes and landslide density, the model proposed in this article shows good rationality, scientificity, and interpretability. The newly occurred landslide site further demonstrates that heuristically optimized DL could make scientific and accurate evaluation of landslide susceptibility.

Landslide susceptibility mapping (LSM) is significant for disaster prevention and mitigation, land use management, and as a reference for decision-making. Convolutional neural networks (CNNs) in deep learning have been successfully applied to LSM studies and have been shown to improve the accuracy of LSM. Although optimizing the quality of negative samples at the input step of a deep learning model can improve the accuracy of the model, the risk of model overfitting may increase. In this study, an LSM method based on the Gaussian heatmap sampling technique and a CNN was developed from the perspective of LSM dataset sampling. A Gaussian heatmap sampling technique was used to enrich the variety of landslide inventory at the input step of the deep learning model to improve the accuracy of the LSM results. This sampling technique involved the construction of a landslide susceptibility Gaussian heatmap neural network model, LSGH-Net, by combining a CNN. A series of optimization strategies such as attention mechanism, dropout, etc., were applied to improve the model structure and training process. The results demonstrated that the proposed approach outperformed the benchmark CNN-based algorithm in terms of metrics (Accuracy = 95.30%, F1 score = 95.13%, and Sensitivity = 91.79%). The Gaussian heatmap sampling technique effectively improved the accuracy of frequency histograms of the landslide susceptibility index, which provided finer-grained mapping details and more reasonable landslide density. By analyzing Gaussian heatmap at different scales, the approach proposed in this paper is an important reference for different regions and other disaster susceptibility studies as well.

Quickly and accurately extracting buildings from remote sensing images is essential for urban planning, change detection, and disaster management applications. In particular, extracting buildings that cannot be sheltered in emergency shelters can help establish and improve a city’s overall disaster prevention system. However, small building extraction often involves problems, such as integrity, missed and false detection, and blurred boundaries. In this study, EfficientUNet+, an improved building extraction method from remote sensing images based on the UNet model, is proposed. This method uses EfficientNet-b0 as the encoder and embeds the spatial and channel squeeze and excitation (scSE) in the decoder to realize forward correction of features and improve the accuracy and speed of model extraction. Next, for the problem of blurred boundaries, we propose a joint loss function of building boundary-weighted cross-entropy and Dice loss to enforce constraints on building boundaries. Finally, model pretraining is performed using the WHU aerial building dataset with a large amount of data. The transfer learning method is used to complete the high-precision extraction of buildings with few training samples in specific scenarios. We created a Google building image dataset of emergency shelters within the Fifth Ring Road of Beijing and conducted experiments to verify the effectiveness of the method in this study. The proposed method is compared with the state-of-the-art methods, namely, DeepLabv3+, PSPNet, ResUNet, and HRNet. The results show that the EfficientUNet+ method is superior in terms of Precision, Recall, F1-Score, and mean intersection over union (mIoU). The accuracy of the EfficientUNet+ method for each index is the highest, reaching 93.01%, 89.17%, 91.05%, and 90.97%, respectively. This indicates that the method proposed in this study can effectively extract buildings in emergency shelters and has an important reference value for guiding urban emergency evacuation.

The effective refuge area is a key indicator in the study of emergency shelters. Accurately extracting the effective refuge area and analyzing the effectiveness of emergency shelters are of great significance for site selection, spatial distribution, and the evaluation of suitability. Beijing is one of only three capitals in the world located in a high-seismic-intensity zone of magnitude 8. The fast and accurate monitoring of effective refuge areas and an analysis of the effectiveness of emergency shelters are conducive to evacuation planning and disaster prevention and mitigation, and they promote the construction of a resilient city. However, the extraction of effective refuge areas in existing studies is not only a time-consuming and labor-intensive task but also has accuracy and efficiency problems, resulting in less precise validity analyses. In this paper, a remote sensing monitoring technology system for the effective refuge areas of emergency shelters is proposed based on multi-source data. Different methods were used to extract various land features, such as buildings and collapsed areas, water, dense areas of understory vegetation, and steep slope areas that cannot be evacuated, to obtain the effective refuge area at a detailed scale, in combination with the service radius of emergency shelters, the population distribution, and the actual road network, the criteria for effectiveness analysis were established for the effective open space ratio, capacity, per capita accessible effective refuge area, and population allocation gap. Taking the area within the Fifth Ring Road of Beijing as an example, the effectiveness of emergency shelters was analyzed at both the whole scale and a local scale. The results show that the effective refuge areas of different emergency shelters in Beijing vary significantly, with the smallest effective refuge area being located in Rings 2–3 and the largest one being located in Rings 4–5; between different regions, there are differences in the effectiveness. This study provides a feasible method for the fast, accurate, and detailed extraction of the effective refuge areas of emergency shelters and also provides a reference for emergency planning for disaster prevention and mitigation.

This paper utilizes agent-based simulation to compare different market making strategies for high frequency traders (HFTs). After proposing a model representing HFTs' activities in financial market when they act as market makers, we carry out simulations to explore how different quoting strategies affect their profit. The results show that combination of (i) offering prices based on the latest trading price, and (ii) using the information about market volatility and order imbalance, increase market makers' daily returns. In addition, other scenarios including the competition environment of increased competitors and decreased latencies are incorporated in the model, in order to find out how these factors change the performance of market making strategy.