Restoring coastal images from high tide to low tide conditions is crucial for applications such as environmental monitoring, coastal planning, and disaster management. Image dewatering, the process of transforming high-tide images into their corresponding low-water-level states to reveal submerged objects, remains a challenging task. Unmanned aerial vehicle (UAV)-based image dewatering presents several unique difficulties. First, there is a scarcity of suitable pre-existing datasets for supervised learning and model training, necessitating extensive data collection to construct an appropriate dataset. Additionally, aligning and matching image pairs is complex. Due to the operational characteristics of UAVs, precisely aligning high-altitude images captured at the same geographic location is difficult, which negatively impacts model training and evaluation. To address these challenges, this study proposes Water2LandNet, an unsupervised UAV image dewatering model based on generative adversarial networks (GANs), which enables training with unpaired images. This approach effectively resolves the data-pairing problem present in our dewatering dataset, OUCD (OUC_UAV_DEWATER). Extensive experiments on the OUCD dataset show that the proposed model effectively removes water from UAV images, as evidenced by qualitative and quantitative evaluations. Furthermore, compared with traditional supervised methods, Water2LandNet demonstrates greater robustness to texture inconsistencies and illumination variations arising from dynamic marine environments. Experimental results confirm that the model can reconstruct visually realistic low-tide scenes even when trained on limited unpaired data, offering a practical solution for large-scale coastal mapping and post-disaster reconstruction.

Localization is a crucial technique for determining target locations in underwater acoustic internet of things (UAIoT) systems. However, the presence of stratified oceanic environments and anchor position uncertainties significantly undermine localization accuracy. To rigorously investigate their adverse effects on localization performance, this study presents a comprehensive theoretical analysis of hybrid RSS/TOA-based localization under stratified propagation and inaccurate anchors in UAIoT systems. First, a stratified acoustic propagation model is established, and the corresponding Cram

r-Rao lower bound (CRLB) is derived for RSS and TOA measurements. The analysis is then extended to a hybrid scenario incorporating anchor inaccuracy using the Banachiewicz-Schur theorem, resulting in a unified expression for the theoretical performance bound. Simulation results confirm the validity of the derived CRLB across diverse scenarios. Compared with using a single ranging modality, the derived lower-bound performance of the proposed hybrid RSS/TOA approach improves by at least 19.8% under accurate-anchor conditions and 24.1% under inaccurate-anchor conditions. The findings provide theoretical benchmarks and practical guidance for designing robust localization strategies in stratified oceanic environments.