The visual perception of streets plays an important role in urban planning, and contributes to the quality of residents’ lives. However, evaluation of the visual perception of streetscapes has been restricted by inadequate techniques and the availability of data sources. The emergence of street view services (Google Street View, Tencent Street View, etc.) has provided an enormous number of new images at street level, thus shattering the restrictions imposed by the limited availability of data sources for evaluating streetscapes. This study explored the possibility of analyzing the visual perception of an urban street based on Tencent Street View images, and led to the proposal of four indices for characterizing the visual perception of streets: salient region saturation, visual entropy, a green view index, and a sky-openness index. We selected the Jianye District of Nanjing City, China, as the study area, where Tencent Street View is available. The results of this experiment indicated that the four indices proposed in this work can effectively reflect the visual attributes of streets. Thus, the proposed indices could facilitate the assessment of urban landscapes based on visual perception. In summary, this study suggests a new type of data for landscape study, and provides a technique for automatic information acquisition to determine the visual perception of streets.

Shallow-water bathymetry based on multispectral satellite imagery (MSI) is an important technology for depth measurement, but it is difficult to obtain a bathymetric map with high reliability and no missing data because of the ubiquitous image noise. Here, we propose a time-series-based bathymetry framework (TSBF). First, a pixel-level time series is constructed using remote sensing images collected at multiple points in time. Then, a new time-domain denoising method, the maximum outlier removal method, is used to create an optimal image from this time series. Finally, bathymetric inversion is performed using this optimal image to obtain a bathymetric map. Anda Reef and northeastern Jiuzhang Atoll, which have complex noise features, were selected as test cases to validate the proposed framework. Results show that the proposed TSBF can obtain bathymetric maps with high accuracy, reliability, and no missing data, outperforming the conventional bathymetry framework based on a single image.

Satellite-derived bathymetry (SDB) techniques are increasingly valuable for deriving high-quality bathymetric maps of coral reefs. Investigating the performance of the related SDB algorithms in purely spaceborne active–passive fusion bathymetry contributes to formulating reliable bathymetric strategies, particularly for areas such as the Spratly Islands, where in situ observations are exceptionally scarce. In this study, we took Anda Reef as a case study and evaluated the performance of eight common SDB approaches by integrating Sentinel-2 images with Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). The bathymetric maps were generated using two classical and six machine-learning algorithms, which were then validated with measured sonar data. The results illustrated that all models accurately estimated the depth of coral reefs in the 0–20 m range. The classical algorithms (Lyzenga and Stumpf) exhibited a mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of less than 0.990 m, 1.386 m, and 11.173%, respectively. The machine learning algorithms generally outperformed the classical algorithms in accuracy and bathymetric detail, with a coefficient of determination (R²) ranging from 0.94 to 0.96 and an RMSE ranging from 1.034 m to 1.202 m. The multilayer perceptron (MLP) achieved the highest accuracy and consistency with an RMSE of as low as 1.034 m, followed by the k-nearest neighbor (KNN) (1.070 m). Our results provide a practical reference for selecting SDB algorithms to accurately obtain shallow water bathymetry in subsequent studies.

Most existing satellite derived bathymetry (SDB) methods generally face the problem of reduced reliability caused by signal attenuation when the water depth exceeds 20 m. To address these challenges in SDB, we propose PhysFormer, a multi scale Transformer that incorporates semi-analytical spectral constraints comprising a hierarchical self-attention backbone and two complementary modules: (1) a multi-scale input (MSI) module to encode local seabed texture and broader environmental structure, and (2) a parallel spectral-reconstruction branch (SRB) that, using a semi-analytical ocean-optics forward model, reconstructs subsurface reflectance from the predicted depth and wavelength-dependent parameters to strengthen physical consistency and thus improve robustness when bottom signals are weak. We trained and tested the model on the French Frigate Shoals dataset and applied the trained model (without fine-tuning) directly to Saipan and Zhongye Reef to assess cross-region generalization. Experiments show that PhysFormer attains an overall RMSE of 0.8328 m on the French Frigate Shoals test set, about 22% lower than the best baseline; in the more challenging 20–30 m interval it achieves RMSE = 0.9052 m, representing an improvement of approximately 24.9% relative to the best performing baseline deep learning model in this depth interval. Without fine-tuning, PhysFormer yields RMSEs of 1.4071 m (Saipan) and 1.7332 m (Zhongye Reef). Ablation experiments suggest that SRB provides a larger contribution than MSI to the 20–30 m performance gain under our controlled setting, while MSI offers complementary benefits. In summary, the integration of physical consistency within the multi-scale transformer architecture offers a significant advancement in SDB accuracy and generalization, providing improved performance in the optically challenging deep-water band (20–30 m).