LiDAR applications in precision agriculture for cultivating crops: A review of recent advances

Changes in the seafloor relief are particularly noticeable in shallow waterbodies (at depths up to several metres), where they are of significance for human safety and environmental protection, as well as for which the highest measurement accuracy is required. The aim of this publication is to present the integration data model of the bathymetric monitoring system for shallow waterbodies using Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV). As part of this model, three technology components will be created: a hydroacoustic and optoelectronic data integration component proposed by Dąbrowski et al., a radiometric depth determination component based on optoelectronic data using the Support Vector Regression (SVR) method, and a coastline extraction component proposed by Xu et al. Thanks to them, it will be possible to cover the entire area with measurements in the coastal zone, in particular between the shallow waterbody coastline and the min. isobath recorded by the echo sounder (the area is lacking actual measurement data). Multisensor data fusion obtained using Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS), Light Detection And Ranging (LiDAR), Real Time Kinematic (RTK), UAV, and USV will allow to meet the requirements provided for the International Hydrographic Organization (IHO) Special Order (horizontal position error ≤ 2 m (p = 0.95), vertical position error ≤ 0.25 m (p = 0.95)). To this end, bathymetric and photogrammetric measurements shall be carried out under appropriate conditions. The water transparency in the tested waterbody should be at least 2 m. Hydrographic surveys shall be performed in windless weather and the water level is 0 in the Douglas sea scale (no waves or sea currents). However, the mission with the use of an UAV should take place in appropriate meteorological conditions, i.e., no precipitation, windless weather (wind speed not exceeding 6–7 m/s), sunny day.

https://www.mdpi.com/2072-4292/14/16/4075/pdf?version=1661228853

Integration Data Model of the Bathymetric Monitoring System for Shallow Waterbodies Using UAV and USV Platforms

• A new building segmentation dataset for deep learning is generated. • The current building datasets have been examined in terms of quality over quantity. • Recent state-of-the-art architectures and encoders have been employed. • Cross-evaluations with different datasets were performed for generability. Automatic building segmentation from satellite images is an important task for various applications such as urban mapping, disaster management and regional planning. With the broader availability of very high-resolution satellite images, deep learning-based techniques have been broadly used for remote sensing image-related tasks. In this study, we generated a new building dataset, the Istanbul dataset, for the building segmentation task. 150 Pléiades image tiles of 1500 × 1500 pixels covering an area of 85 km 2 area of Istanbul city were used and approximately 40,000 buildings were labelled, representing different building structures and spatial distribution. We extensively investigated the ideal architecture, encoder and hyperparameter settings for building segmentation tasks using the new Istanbul dataset. More than 60 experiments were conducted by applying state-of-the-art architectures such as U-Net, Unet++, DeepLabv3+, FPN and PSPNet with different pre-trained encoders and hyperparameters. Our experiments showed that Unet++ architecture using SE-ResNeXt101 encoder pre-trained with ImageNet provides the best results with 93.8% IoU on the Istanbul dataset. In order to prove our solution's generalizability, the ideal network has also been trained separately on Inria and Massachusetts building segmentation datasets. The networks have produced IoU values of 75.39% and 92.53% on the Inria and Massachusetts datasets, respectively. The results indicate that our ideal network solution settings outperform other methods in terms of building segmentation even without any specific architectural modification. The weights files and inference notebook is available on: https://github.com/TolgaBkm/Istanbul_Dataset . 

Comparative analysis of deep learning based building extraction methods with the new VHR Istanbul dataset

The development of a sustainable water quality monitoring system at national scale remains a big challenge until today, acting as a hindrance for the efficient implementation of the Water Framework Directive (WFD). This work provides valuable insights into the current state-of-the-art Earth Observation (EO) tools and services, proposing a synergistic use of innovative remote sensing technologies, in situ sensors, and databases, with the ultimate goal to support the European Member States in effective WFD implementation. The proposed approach is based on a recent research and scientific analysis for a six-year period (2017–2022) after reviewing 71 peer-reviewed articles in international journals coupled with the scientific results of 11 European-founded research projects related to EO and WFD. Special focus is placed on the EO data sources (spaceborne, in situ, etc.), the sensors in use, the observed water Quality Elements as well as on the computer science techniques (machine/deep learning, artificial intelligence, etc.). The combination of the different technologies can offer, among other things, low-cost monitoring, an increase in the monitored Quality Elements per water body, and a minimization of the percentage of water bodies with unknown ecological status.

/pdf/synergistic-use-of-earth-observation-driven-techniques-to-vcxquxaz.pdf

Synergistic Use of Earth Observation Driven Techniques to Support the Implementation of Water Framework Directive in Europe: A Review

This paper presents a perception-aware path planner for active SLAM in dynamic environments using micro-aerial vehicles (MAV). The “Next-Best-View” planner (NBVP planner) is combined with an active loop closing, which is called the Active Loop Closing Planner (ALCP planner). The planner is proposed to avoid both static and dynamic obstacles in unknown environments while reducing the uncertainty of the SLAM system and further improving the accuracy of localization. First, the receding horizon strategy is adopted to find the next waypoint. The cost function that combines the exploration gain and the loop closing gain is designed. The former can reduce the mapping uncertainty, while the latter takes the loop closing possibility into consideration. Second, a key waypoint selection strategy is designed. The selected key waypoints, instead of all waypoints, are treated as potential loop-closing points to make the algorithm more efficient. Moreover, a fuzzy RRT-based dynamic obstacle avoidance algorithm is adopted to realize obstacle avoidance in dynamic environments. Simulations in different challenging scenarios are conducted to verify the effectiveness of the proposed algorithm.

/pdf/perception-aware-planning-for-active-slam-in-dynamic-1utuh5kl.pdf

Perception-Aware Planning for Active SLAM in Dynamic Environments

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.

https://www.mdpi.com/1424-8220/22/4/1379/pdf?version=1644567617

Evaluation of a New Lightweight UAV-Borne Topo-Bathymetric LiDAR for Shallow Water Bathymetry and Object Detection

In this paper, we investigate the outage performance of full-duplex (FD) and half-duplex (HD) vehicle to vehicle (V2V) networks. We consider two vehicular communication scenarios with the assistance of roadside unit (RSU), i.e., RSU-to-vehicle-to-vehicle (RVV) and vehicle-to-RSU-to- vehicle (VRV) scenarios. In particular, we analyze the outage performance of vehicular networks with the amplify-and-forward (AF) and decode-and-forward (DF) protocols under the effect of residual loop-back-interference (LBI) and channel estimation errors. For RVV scenario, the upper bounds for the outage probability of the AF based FD and HD relaying over double Rayleigh fading channels are derived. Moreover, we derive the approximate closed form expressions for the outage probability of the DF based FD and HD relaying protocol over Rayleigh fading channels; For VRV scenario, the exact closed-form expressions for the outage probability of the AF and DF relaying protocols are presented in case of FD and HD modes. Furthermore, we derive the approximate closed-form expressions for the outage probability of hybrid FD/HD mode on AF and DF relaying protocols. The numerical and simulation results corroborate our theoretical ones and quantify the impact of the data rates, channel estimation errors and the residual LBI on the system performance.

Full Duplex AF and DF Relaying Under Channel Estimation Errors for V2V Communications

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.

https://ieeexplore.ieee.org/ielx7/8859/9651998/09762982.pdf

Ground Photon Extraction From Photon-Counting LiDAR Data Using Adaptive Cloth Simulation With Terrain Index

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.

/pdf/a-pre-pruning-quadtree-isolation-method-with-changing-24t6y7kp.pdf

A Pre-Pruning Quadtree Isolation Method with Changing Threshold for ICESat-2 Bathymetric Photon Extraction

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 article 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 multiscale and multidirection 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.

https://ieeexplore.ieee.org/ielx7/4609443/4609444/10162179.pdf

Dual-Task Network for Road Extraction From High-Resolution Remote Sensing Images

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