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The rapid, accurate extraction of water body information is critical for water resource management and disaster assessment. Its data foundation was mostly provided by remote sensing images through deep learning methods. However, the methods still require the improvement of recognition accuracy and reduction of model size. As a solution, this paper proposed a new high-precision convolutional neural network for water body extraction. This network’s structural design is based on the assumption that the extraction effect of a convolutional neural network is independent from its parameters number, thus the recognition effect could be effectively improved through reasonable adjustment of the network structure according to characteristics of water bodies on high-resolution remote sensing images. It brings two critical improvements. Firstly, the number of downsampling layers was reduced to adapt to the low resolution of remote sensing imagery. Secondly, the bottleneck structure has also been updated to fit the decoder–encoder framework. The improved bottleneck structures were nested to ensure the transmission of water characteristics information in the model. In comparison with the other five commonly used networks, the new network has achieved the best results (average overall accuracy: 98.31%, parameter benefit value: 0.2625), indicating the extremely high practical value of this approach.

In recent years, the rapid expansion of urban rail transit has significantly improved travel efficiency, yet it has also exacerbated spatial inequality in service coverage. Accessibility, as a fundamental metric for evaluating the equity of service distribution, remains limited by three major shortcomings in current assessment methods: the neglect of actual road network characteristics, reliance on a single static scale, and the absence of quantitative mechanisms to assess accessibility equity. These deficiencies hinder a comprehensive understanding of how equity evolves with the spatiotemporal dynamics of rail systems. To address the aforementioned issues, this study proposes an innovative spatiotemporally dynamic and multi-scale analytical framework for evaluating urban rail accessibility and its equity implications. Specifically, we develop a network-based buffer decay model to refine service population estimation by incorporating realistic walking paths, capturing both distance decay and road network constraints. The framework integrates multiple spatial analytical techniques, including the Gini coefficient, Lorenz curve, global and local spatial autocorrelation, center-of-gravity shift, and standard deviation ellipse, to quantitatively assess the equity and evolutionary patterns of accessibility across multiple spatial scales. Taking the central urban area of Hangzhou as a case study, this research investigates the spatiotemporal patterns and equity changes in metro station accessibility in 2019 and 2023. The results indicate that the expansion of the metro network has partially improved overall accessibility equity: the Gini coefficient at the TAZ (Traffic Analysis Zone) scale decreased from 0.56 to 0.425. Nevertheless, significant inequality remains at finer spatial resolutions (grid-level Gini coefficient = 0.404). In terms of spatial pattern, the core area (e.g., Wulin Square) forms a ‘high-high’ accessibility agglomeration area, while the urban fringe area (e.g., northern Yuhang) presents a ‘low-low’ agglomeration, and the problem of local ‘accessibility depression’ still exists. Additionally, the accessibility centroid has consistently shifted northwestward, and the long axis of the standard deviation ellipse has rotated from an east–west to a northwest-southeast orientation, indicating a growing spatial polarization between core and peripheral zones. The findings suggest that improving equity in urban rail accessibility cannot rely solely on expanding network size; rather, it requires coordinated strategies involving network structure optimization, branch line development, multimodal integration, and the construction of efficient transfer systems to promote more balanced and equitable spatial distribution of rail transit resources citywide.

Rapid, accurate extraction of terraces from high-resolution images is of great significance for promoting the application of remote-sensing information in soil and water conservation planning and monitoring. To solve the problem of how deep learning requires a large number of labeled samples to achieve good accuracy, this article proposes an automatic identification method for terraces that can obtain high precision through small sample datasets. Firstly, a terrace identification source model adapted to multiple data sources is trained based on the WorldView-1 dataset. The model can be migrated to other types of images for terracing extraction as a pre-trained model. Secondly, to solve the small sample problem, a deep transfer learning method for accurate pixel-level extraction of high-resolution remote-sensing image terraces is proposed. Finally, to solve the problem of insufficient boundary information and splicing traces during prediction, a strategy of ignoring edges is proposed, and a prediction model is constructed to further improve the accuracy of terrace identification. In this paper, three regions outside the sample area are randomly selected, and the OA, F1 score, and MIoU averages reach 93.12%, 91.40%, and 89.90%, respectively. The experimental results show that this method, based on deep transfer learning, can accurately extract terraced field surfaces and segment terraced field boundaries.

One of the keys in time-dependent routing is determining the weight of each road network link based on traffic information. To facilitate the estimation of the road's weight, Global Position System (GPS) data are commonly used in obtaining real-time traffic information. However, the information obtained by taxi-GPS does not cover the entire road network. Aiming at incomplete traffic information on urban roads, this paper proposes a novel fuzzy inference method. It considers the combined effect of road grade, traffic information, and other spatial factors. Taking the third law of geography as the basic premise, that is, the more similar the geographical environment, the more similar the characteristics of the geographical target will be. This method uses a Typical Link Pattern (TLP) model to describe the geographical environment. The TLP represents typical road sections with complete information. Then, it determines the relationship between roads lacking traffic information and the TLPs according to their related factors. After obtaining the TLPs, this method ascertains the weight of road links by calculating their similarities with TLPs based on the theory of fuzzy inference. Aiming at road links at different places, the dividing - conquering strategy and globe algorithm are also introduced to calculate the weight. These two strategies are used to address the excessively fragmented or lengthy links. The experimental results with the case of Newcastle show robustness in that the average Root Mean Square Error (RMSE) is 1.430 mph, and the bias is 0.2%; the overall RMSE is 11.067 mph, and the bias is 0.6%. This article is the first to combine the third law of geography with fuzzy inference, which significantly improves the estimation accuracy of road weights with incomplete information. Empirical application and validation show that the method can accurately predict vehicle speed under incomplete information.

The frequency of marine oil spills has increased in recent years. The growing exploitation of marine oil and continuous increase in marine crude oil transportation has caused tremendous damage to the marine ecological environment. Using synthetic aperture radar (SAR) images to monitor marine oil spills can help control the spread of oil spill pollution over time and reduce the economic losses and environmental pollution caused by such spills. However, it is a significant challenge to distinguish between oil-spilled areas and oil-spill-like in SAR images. Semantic segmentation models based on deep learning have been used in this field to address this issue. In addition, this study is dedicated to improving the accuracy of the U-Shape Network (UNet) model in identifying oil spill areas and oil-spill-like areas and alleviating the overfitting problem of the model; a feature merge network (FMNet) is proposed for image segmentation. The global features of SAR image, which are high-frequency component in the frequency domain and represents the boundary between categories, are obtained by a threshold segmentation method. This can weaken the impact of spot noise in SAR image. Then high-dimensional features are extracted from the threshold segmentation results using convolution operation. These features are superimposed with to the down sampling and combined with the high-dimensional features of original image. The proposed model obtains more features, which allows the model to make more accurate decisions. The overall accuracy of the proposed method increased by 1.82% and reached 61.90% compared with the UNet. The recognition accuracy of oil spill areas and oil-spill-like areas increased by approximately 3% and reached 56.33%. The method proposed in this paper not only improves the recognition accuracy of the original model, but also alleviates the overfitting problem of the original model and provides a more effective monitoring method for marine oil spill monitoring. More importantly, the proposed method provides a design principle that opens up new development ideas for the optimization of other deep learning network models.

Obtaining PM2.5 data for the entirety of a research region underlies the study of the relationship between PM2.5 and human spatiotemporal activity. A professional sampler with a filter membrane is used to measure accurate values of PM2.5 at single points in space. However, there are numerous PM2.5 sampling and monitoring facilities that rely on data from only representative points, and which cannot measure the data for the whole region of research interest. This provides the motivation for researching the methods of estimation of particulate matter in areas having fewer monitors at a special scale, an approach now attracting considerable academic interest. The aim of this study is to (1) reclassify and particularize the most frequently used approaches for estimating the PM2.5 concentrations covering an entire research region; (2) list improvements to and integrations of traditional methods and their applications; and (3) compare existing approaches to PM2.5 estimation on the basis of accuracy and applicability.

Navigation systems are extensively used in everyday life, but the conventional A* algorithm has several limitations in path planning applications within these systems, such as low degrees of freedom in path planning, inadequate consideration of the effects of special regions, and excessive nodes and turns. Addressing these limitations, an enhanced A* algorithm was proposed using regular hexagonal grid mapping. First, the approach to map modeling using hexagonal grids was described. Subsequently, the A* algorithm was refined by optimizing the calculation of movement costs, thus allowing the algorithm to integrate environmental data more effectively and flexibly adjust node costs while ensuring path optimality. A quantitative method was also introduced to assess map complexity and adaptive heuristics that decrease the number of traversed nodes and increase the search speed. Moreover, a turning penalty measure was implemented to minimize unnecessary turns on the planned paths. Simulation results confirmed that the improved A* algorithm exhibits superior performance, which can dynamically adjust movement costs, enhance search efficiency, reduce turns, improve overall path planning quality, and solve critical path planning issues in navigation systems, greatly aiding the development and design of these systems and making them better suited to meet modern navigation requirements.

This study aims to quantitatively model rather than to presuppose whether or not air pollution in Beijing (China) affects people’s activities of daily living (ADLs) based on an Internet of Behaviours (IoB), in which IoT sensor data can signal environmental events that can change human behaviour on mass. Peoples’ density distribution computed by call detail records (CDRs) and air quality data are used to build a fixed effect model (FEM) to analyse the influence of air pollution on four types of ADLs. The following four effects are discovered: Air pollution negatively impacts people going sightseeing in the afternoon; has a positive impact on people staying-in, in the morning and the middle of the day. Air pollution lowers people’s desire to go to restaurants for lunch, but far less so in the evening. As air quality worsens, people tend to decrease their walking and cycling and tend to travel more by bus or subway. We also find a monotonically decreasing nonlinear relationship between air quality index and the average CDR-based distance for each person of two citizen groups that go walking or cycling. Our key and novel contributions are that we first define IoB as a ubiquitous concept. Based on this, we propose a methodology to better understand the link between bad air pollution events and citizens’ activities of daily life. We applied this methodology in the first comprehensive study that provides quantitative evidence of the actual effect, not the presumed effect, that air pollution can significantly affect a wide range of citizens’ activities of daily living.

Air-borne particulate matter, PM2.5 (PM having a diameter of less than 2.5 micrometers), has aroused widespread concern and is a core indicator of severe air pollution in many cities globally. In our study, we present a validated framework to predict the daily PM2.5 distributions, exemplified by a use case of Shijiazhuang City, China, based on daily aerosol optical depth (AOD) datasets. The framework involves obtaining the high-resolution spatiotemporal AOD distributions, estimation of the spatial distributions of PM2.5 and the prediction of these based on a convolutional long short-term memory (ConvLSTM) model. In the estimation part, the eXtreme gradient boosting (XGBoost) model has been determined as the estimation model with the lowest root mean square error (RMSE) of 32.86 µg/m3 and the highest coefficient of determination regression score function (R2) of 0.71, compared to other common models used as a baseline for comparison (linear, ridge, least absolute shrinkage and selection operator (LASSO) and cubist). For the prediction part, after validation and comparison with a seasonal autoregressive integrated moving average (SARIMA), which is a traditional time-series prediction model, in both time and space, the ConvLSTM gives a more accurate performance for the prediction, with a total average prediction RMSE of 14.94 µg/m3 compared to SARIMA’s 17.41 µg/m3. Furthermore, ConvLSTM is more stable and with less fluctuations for the prediction of PM2.5 in time, and it can also eliminate better the spatial predicted errors compared to SARIMA.

Mangroves are indispensable to coastlines, maintaining biodiversity, and mitigating climate change. Therefore, improving the accuracy of mangrove information identification is crucial for their ecological protection. Aiming at the limited morphological information of synthetic aperture radar (SAR) images, which is greatly interfered by noise, and the susceptibility of optical images to weather and lighting conditions, this paper proposes a pixel-level weighted fusion method for SAR and optical images. Image fusion enhanced the target features and made mangrove monitoring more comprehensive and accurate. To address the problem of high similarity between mangrove forests and other forests, this paper is based on the U-Net convolutional neural network, and an attention mechanism is added in the feature extraction stage to make the model pay more attention to the mangrove vegetation area in the image. In order to accelerate the convergence and normalize the input, batch normalization (BN) layer and Dropout layer are added after each convolutional layer. Since mangroves are a minority class in the image, an improved cross-entropy loss function is introduced in this paper to improve the model’s ability to recognize mangroves. The AttU-Net model for mangrove recognition in high similarity environments is thus constructed based on the fused images. Through comparison experiments, the overall accuracy of the improved U-Net model trained from the fused images to recognize the predicted regions is significantly improved. Based on the fused images, the recognition results of the AttU-Net model proposed in this paper are compared with its benchmark model, U-Net, and the Dense-Net, Res-Net, and Seg-Net methods. The AttU-Net model captured mangroves’ complex structures and textural features in images more effectively. The average OA, F1-score, and Kappa coefficient in the four tested regions were 94.406%, 90.006%, and 84.045%, which were significantly higher than several other methods. This method can provide some technical support for the monitoring and protection of mangrove ecosystems.