Airborne LiDAR bathymetry (ALB) has proven to be an effective technology for shallow water mapping. To collect data with a high point density, a lightweight dual-wavelength LiDAR system mounted on unmanned aerial vehicles (UAVs) was developed. This study presents and evaluates the system using the field data acquired from a flight test in Dazhou Island, China. In the precision and accuracy assessment, the local fitted planes extracted from the water surface points and the multibeam echosounder data are used as a reference for water surface and bottom measurements, respectively. For the bathymetric performance comparison, the study area is also measured with an ALB system installed on the manned aerial platform. The object detection capability of the system is examined with placed small cubes. Results show that the fitting precision of the water surface is 0.1227 m, and the absolute accuracy of the water bottom is 0.1268 m, both of which reach a decimeter level. Compared to the manned ALB system, the UAV-borne system provides higher resolution data with an average point density of 42 points/m² and maximum detectable depth of 1.7–1.9 Secchi depths. In the point cloud of the water bottom, the existence of a 1-m target cube and the rough shape of a 2-m target cube are clearly observed at a depth of 12 m. The system shows great potential for flexible shallow water mapping and underwater object detection with promising results.

Airborne LiDAR bathymetry (ALB) has shown great potential in shallow water and coastal mapping. However, due to the variability of the waveforms, it is hard to detect the signals from the received waveforms with a single algorithm. This study proposed a depth-adaptive waveform decomposition method to fit the waveforms of different depths with different models. In the proposed method, waveforms are divided into two categories based on the water depth, labeled as “shallow water (SW)” and “deep water (DW)”. An empirical waveform model (EW) based on the calibration waveform is constructed for SW waveform decomposition which is more suitable than classical models, and an exponential function with second-order polynomial model (EFSP) is proposed for DW waveform decomposition which performs better than the quadrilateral model. In solving the model’s parameters, a trust region algorithm is introduced to improve the probability of convergence. The proposed method is tested on two field datasets and two simulated datasets to assess the accuracy of the water surface detected in the shallow water and water bottom detected in the deep water. The experimental results show that, compared with the traditional methods, the proposed method performs best, with a high signal detection rate (99.11% in shallow water and 74.64% in deep water), low RMSE (0.09 m for water surface and 0.11 m for water bottom) and wide bathymetric range (0.22 m to 40.49 m).

In high-resolution remote sensing images, road scale diversity and occlusions caused by shadows, buildings, and vegetation often pose challenges for road extraction. Currently, end-to-end models constructed using deep convolutional neural networks are widely used in road extraction and have significantly improved the accuracy of this task. However, the connectivity and completeness of their results require improvement. This paper proposes a dual task-driven deep convolutional neural network constructed by combining road shape patterns and scale differences. The mainline task is road-surface segmentation, the encoder of which employs residual convolution for feature extraction. The decoder comprises a multi-scale and multi-direction strip convolution module, the output of which is the final extraction result. The splitting task is road centerline extraction, the input features of which come from the coding layer of the road-surface segmentation branches. The intermediate features are incorporated into the decoder of the road-surface segmentation branches, to fully exploit the road centerline and thus improve the road-surface segmentation result connectivity. Implementation of the proposed method on the CHN6-CUG and DeepGlobe datasets reveals superior performance to comparative methods as regards quantitative evaluation metrics; evident advantages for road coverings, road intersections, and low-scale roads; greater model portability; and better small-sample learning capability.

The new generation of spaceborne laser altimeter, the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), which can simultaneously generate laser reflections on the water surface and underwater, is a potential data source for exploring water depth in nearshore environments. To achieve this scientific goal, accurate bathymetric photon extraction is needed. This study proposed a pre-pruning quadtree isolation (PQI) method with changing threshold. Firstly, the pre-pruning step is introduced. Photons are transformed into different levels in the isolated quadtree structure according to spatial density. Then, the frequency histogram of photon elevation and isolated level (IL) is generated, the IL thresholds in different depth ranges are calculated by the Otsu method, and the bathymetric photons are extracted. The results in the Culebra archive show that this method achieved a 92.71% F1 score. Noise rate and water depth are the main factors affecting the extraction of sounding photons. When the photon density gradually increases from 2–4 pts/m to 6–8 pts/m, the F1 score of PQI decreases by no more than two percent. In different depth ranges, the extraction results of PQI are also better than those of comparison methods. Therefore, PQI can provide reliable theoretical support for nearshore areas lacking water depth data.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) equips with a novel photon-counting LiDAR system, which can generate underwater reflections in nearshore environments. However, due to the water reflection, scattering, and absorption, the distribution of bathymetric photons in the nearshore data varies with depth. The existing bathymetric photon extraction algorithms need more adaptability to seafloor topography. The changing density of bathymetric photons and the fluctuation of underwater topography make the noise removal of nearshore data full of challenges. This study proposed a bathymetric photon extraction algorithm using pre-pruning quadtree isolation (PQI). Firstly, the pre-pruning step judges whether to stop the growth of quadtree in advance during quadtree isolation (QI) to avoid excessive division of noise photons. Secondly, the maximum inter-class variance algorithm (also called the Otsu method) obtains the best threshold of isolation depth and extracts bathymetric photons. The algorithm was tested on the Florida coast. The results show that the PQI algorithm can wholly and accurately extract bathymetric photons with different acquisition times from the data. The F1-score of the extracted results is 93.96%. This study provides an intelligent solution to processing bathymetric data in nearshore environments worldwide.

Photon-counting light detection and ranging (LiDAR) Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) enables the drafting of global elevation maps. However, vegetation cover, terrain undulation, and residual noise in signal photons substantially reduce the accuracy of ground photon extraction. Existing ground photon extraction algorithms do not consider the factors influencing photon extraction, and the threshold setting lacks a theoretical basis. This study proposed a photon-extraction algorithm with scenario adaptability. First, the cloth simulation (CS) was adapted with a terrain index (TI) to extract ground photons; based on this, the cloth breakage concept was proposed to remove residual noise. We tested the algorithm in Denali National Park and compared its results with those of other extraction algorithms. The results showed that the TI was robust and consistent with the actual terrain; the adaptive CS achieved the best accuracy and precision under different canopy heights and terrains. The mean absolute error (MAE) and root mean square error (RMSE) of extracted photons were 0.95 and 3.41 m, respectively. This study provides a solution to estimate ground elevation using photon-counting LiDAR data.