Journal of Imaging

Drastic alterations have been observed in the coastline of Bangkok Bay, Thailand, over the past three decades. Understanding how coastlines change plays a key role in developing strategies for coastal protection and sustainable resource utilization. This study investigates the temporal and spatial changes in the Bangkok Bay coastline, Thailand, using remote sensing and GIS techniques from 1989 to 2024. The historical rate of coastline change for a typical segment was analyzed using the EPR method, and the underlying causes of these changes were discussed. Finally, the variation trend of the total shoreline length and the characteristics of erosion and sedimentation for a typical shoreline in Bangkok Bay, Thailand, over the past 35 years were obtained. An overall increase in coastline length was observed in Bangkok Bay, Thailand, over the 35-year period from 1989 to 2024, with a net gain from 507.23 km to 571.38 km. The rate of growth has transitioned from rapid to slow, with the most significant changes occurring during the period 1989–1994. Additionally, the average and maximum erosion rates for the typical shoreline segment were notably high during 1989–1994, with values of −21.61 m/a and −55.49 m/a, respectively. The maximum sedimentation rate along the coastline was relatively high from 2014 to 2024, reaching 10.57 m/a. Overall, the entire coastline of the Samut Sakhon–Bangkok–Samut Prakan Provinces underwent net erosion from 1989 to 2024, driven by a confluence of natural and anthropogenic factors.

The increasing use of artificial intelligence (AI) and deep learning (DL) techniques has driven advances in vehicle classification and detection applications for embedded devices with deployment constraints due to computational cost and response time. In the case of urban environments with high traffic congestion, such as the city of Lima, it is important to determine the trade-off between model accuracy, type of embedded system, and the dataset used. This study was developed using a methodology adapted from the CRISP-DM approach, which included the acquisition of traffic videos in the city of Lima, their segmentation, and manual labeling. Subsequently, three SSD-based detection models (MobileNetV1-SSD, MobileNetV2-SSD-Lite, and VGG16-SSD) were trained on the NVIDIA Jetson Orin NX 16 GB platform. The results show that the VGG16-SSD model achieved the highest average precision (mAP $\approx 90.7\%$), with a longer training time, while the MobileNetV1-SSD ($512\times 512$) model achieved comparable performance (mAP $\approx 90.4\%$) with a shorter time. Additionally, data augmentation through contrast adjustment improved the detection of minority classes such as Tuk-tuk and Motorcycle. The results indicate that, among the evaluated models, MobileNetV1-SSD ($512\times 512$) achieved the best balance between accuracy and computational load for its implementation in ADAS embedded systems in congested urban environments.

Existing methods for reconstructing hyperspectral images from single RGB images struggle to obtain a large number of labeled RGB-HSI paired images. These methods face issues such as detail loss, insufficient robustness, low reconstruction accuracy, and the difficulty of balancing the spatial–spectral trade-off. To address these challenges, a Double-Gated Mamba Multi-Scale Adaptive Feature (DMMAF) learning network model is proposed. DMMAF designs a reflection dot-product adaptive dual-noise-aware feature extraction method, which is used to supplement edge detail information in spectral images and improve robustness. DMMAF also constructs a deformable attention-based global feature extraction method and a double-gated Mamba local feature extraction approach, enhancing the interaction between local and global information during the reconstruction process, thereby improving image accuracy. Meanwhile, DMMAF introduces a structure-aware smooth loss function, which, by combining smoothing, curvature, and attention supervision losses, effectively resolves the spatial–spectral resolution balance problem. This network model is applied to three datasets—NTIRE 2020, Harvard, and CAVE—achieving state-of-the-art unsupervised reconstruction performance compared to existing advanced algorithms. Experiments on the NTIRE 2020, Harvard, and CAVE datasets demonstrate that this model achieves state-of-the-art unsupervised reconstruction performance. On the NTIRE 2020 dataset, our method attains MRAE, RMSE, and PSNR values of 0.133, 0.040, and 31.314, respectively. On the Harvard dataset, it achieves RMSE and PSNR values of 0.025 and 34.955, respectively, while on the CAVE dataset, it achieves RMSE and PSNR values of 0.041 and 30.983, respectively.