The temporal dimensions of public transit accessibility have recently garnered an increasing amount of interest. However, the existing literature on transit accessibility is heavily based on oversimplified assumptions that transit services operate at deterministic speeds using predetermined timetables. These measurements may overestimate transit accessibility, especially for large metropolitan areas where inter- and intra-modal transfers are frequent. To handle travel time uncertainty, a multi-modal transit accessibility modeling approach is proposed to account for realistic variations in travel time and service reliability. The proposed approach is applied to the mapping of transit accessibility in Shenzhen (China), where transit services exhibit significant travel time variations over space and time. Compared to traditional transit accessibility measures, our method has been demonstrated to better capture intrinsic spatial and temporal accessibility variations with complex multi-modal transit networks. Normal distribution of inter-stop travel times and constant travel speed between GPS sampling points are assumed to simply the computation, which we consider to adjust in future studies to better quantify the dynamics of transit accessibility across space and time.

Numerous aquaculture ponds are intensively distributed around inland natural lakes and mixed with cropland, especially in areas with high population density in Asia. Information about the distribution of aquaculture ponds is essential for monitoring the impact of human activities on inland lakes. Accurate and efficient mapping of inland aquaculture ponds using high-spatial-resolution remote-sensing images is a challenging task because aquaculture ponds are mingled with other land cover types. Considering that aquaculture ponds have intertwining regular embankments and that these salient features are prominent at different scales, a Row-wise and Column-wise Self-Attention (RCSA) mechanism that adaptively exploits the identical directional dependency among pixels is proposed. Then a fully convolutional network (FCN) combined with the RCSA mechanism (RCSANet) is proposed for large-scale extraction of aquaculture ponds from high-spatial-resolution remote-sensing imagery. In addition, a fusion strategy is implemented using a water index and the RCSANet prediction to further improve extraction quality. Experiments on high-spatial-resolution images using pansharpened multispectral and 2 m panchromatic images show that the proposed methods gain at least 2–4% overall accuracy over other state-of-the-art methods regardless of regions and achieve an overall accuracy of 85% at Lake Hong region and 83% at Lake Liangzi region in aquaculture pond extraction.

Sea fog is a disastrous marine phenomenon for ship navigation. Sea fog reduces visibility at sea and has a great impact on the safety of ship navigation, which may lead to catastrophic accidents. Geostationary orbit satellites such as Himawari-8 make it possible to monitor sea fog over large areas of the sea. In this paper, a framework for marine navigation risk evaluation in fog seasons is developed based on Himawari-8 satellite data, which includes: (1) a sea fog identification method for Himawari-8 satellite data based on multilayer perceptron; (2) a navigation risk evaluation model based on the CRITIC objective weighting method, which, along with the sea fog identification method, allows us to obtain historical sea fog data and marine environmental data, such as properties related to wind, waves, ocean currents, and water depth to evaluate navigation risks; and (3) a way to determine shipping routes based on the Delaunay triangulation method to carry out risk analyses of specific navigation areas. This paper uses global information system mapping technology to get navigation risk maps in different seasons in Bohai Sea and its surrounding waters. The proposed sea fog identification method is verified by CALIPSO vertical feature mask data, and the navigation risk evaluation model is verified by historical accident data. The probability of detection is 81.48% for sea fog identification, and the accident matching rate of the navigation risk evaluation model is 80% in fog seasons.

Target recognition from remote sensing images is commonly challenging because of large-scale variations and small objects, and these challenges are more prominent in satellite video images. The current object detection algorithms have some difficulties in fine-grained feature extraction and classification for multi-scale and small objects. We propose a novel model called the SVSDet method based on YOLOv5 improvement to address the abovementioned issues. In this method, we have introduced the Space-to-depth module into the backbone of the network, which enhances the network's ability to extract fine-grained features. The Neck structure is improved by using the bidirectional feature pyramid network to enhance the network's ability to extract features at multiple scales, thereby improving its overall multi-scale feature extraction ability. Subsequently, we have replaced the C3 module in the original network's neck with the C2f module to obtain more abundant gradient flow information. This helps to improve the network's performance further. Finally, the CA attention module is introduced into the cross-scale feature connection path, which effectively enhances the network's target detection and recognition performance. We have conducted extensive comparative experiments and ablation experiments on the publicly available datasets ShipRSImageNet and SAT-MTB to confirm the effectiveness of our proposed SVSDet method. The performance of this approach is then evaluated using Jilin 1 satellite video data, and it outperforms the main YOLO series algorithms currently used.

Sea fog poses a critical challenge to maritime safety. Current sea fog detection methods based on geostationary satellite data predominantly rely on the visual interpretation of samples, yet conventional visual interpretation methods are prone to subjective biases in sample labeling. This study proposes a novel dual-branch Patch-CNN architecture using Himawari-8 satellite data, which addresses labeling uncertainty through CALIPSO-derived vertical aerosol profiles that unambiguously discriminate sea fog from low-level clouds. Training samples were exclusively constructed from CALIPSO vertical feature masks (VFM), ensuring spectral reliability by eliminating cloud-fog ambiguity. The framework integrates three distinctive features: (1) Spatial-context preservation via patch-based inputs to encapsulate neighborhood characteristics around sample points; (2) multispectral feature extraction through a dual-branch architecture that separately captures characteristics from visible-near infrared and infrared channels; and (3) adaptive feature fusion employing attention-weighted feature integration to enhance discriminative representation. Evaluated across the Yellow-Bohai Sea region, the model achieved 89.74% overall accuracy and 82.98% detection probability, outperforming conventional machine learning and deep learning benchmarks. In validation testing with meteorological station data, the proposed method exhibited stronger robustness than traditional machine learning and deep learning methods, with 87 true positives, while case studies using CALIOP VFM and ICOADS visibility data found that the present network could accurately identify the foggy area. This work establishes a satellite-based fog detection network, particularly suited for complex marine environments where fog-cloud spectral overlap challenges traditional methods.

Wind turbines are vital equipment for converting wind energy into electrical energy. Precise detection of wind turbines in high-resolution images is of great importance for building accurate wind turbine databases. However, their distinctive white appearance poses challenges for detection in snowy domain images. To address this problem, a domain adaptive deep convolutional neural network model named DAWindNet, is proposed, aiming at cross-domain extraction of wind turbines in snow background images. First, in the image-level module, the model learns discrepancies between images from different domains, alters the image style, and achieves data alignment for complete images. Second, in the instance-level module, the model focus shifts to the structural information of wind turbine targets, further refining cross-domain data alignment. Perception loss and domain difference loss are designed to preserve semantic consistency across domains and mitigate domain offset phenomena of the target. Finally, bidirectional feature pyramid network and an attention mechanism are incorporated to enhance the network's ability to extract wind power features and achieve higher recognition rates. Experimental results on datasets representing ordinary, bare soil, and snowy domains validate that DAWindNet achieves satisfactory performance, with a recall rate of 63.8% and an average precision (AP) of 67.3% when transitioning from ordinary to snow background domains, and a recall rate of 65.1% and an AP of 66.1% for the transition between bare soil and snow background domains. These results demonstrate the effectiveness of the proposed modifications for the cross-domain extraction of wind turbines