Uncrewed aerial systems (UASs) are becoming very popular in the domain of water resource mapping and management (WRMM). Being a cheaper and quicker option capable of providing high temporal and spatial resolution data, UAS has become a much sought-after platform for remote sensing. Still, their application in the field is in its early stage. This paper encompasses basic concepts of UAS, different payloads and sensor technologies available, various methodologies for its application in WRMM, different software available, and challenges associated with them, thus presenting a comprehensive review of multiple applications of UAS in different sub-domains of water resources. From cryosphere, rivers and lakes, and coastal areas to sub-surface water, as well as from water quality to wastewater management, the authors have discussed various applications of uncrewed aerial vehicles. At the end of the paper, the authors have identified the issues posing problems in the wider implementation of UAS in WRMM. Also, the future scope of the UAS in WRMM has been discussed.
In the last few years, uncrewed aerial systems (UASs) have been broadly employed for many applications including urban traffic monitoring. However, in the detection, tracking, and geolocation of moving vehicles using UAVs there are problems to be encountered such as low-accuracy sensors, complex scenes, small object sizes, and motion-induced noises. To address these problems, this study presents an intelligent, self-optimised, real-time framework for automated vehicle detection, tracking, and geolocation in UAV-acquired images which enlist detection, location, and tracking features to improve the final decision. The noise is initially reduced by applying the proposed adaptive filtering, which makes the detection algorithm more versatile. Thereafter, in the detection step, top-hat and bottom-hat transformations are used, assisted by the Overlapped Segmentation-Based Morphological Operation (OSBMO). Following the detection phase, the background regions are obliterated through an analysis of the motion feature points of the obtained object regions using a method that is a conjugation between the Kanade–Lucas–Tomasi (KLT) trackers and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering. The procured object features are clustered into separate objects on the basis of their motion characteristics. Finally, the vehicle labels are designated to their corresponding cluster trajectories by employing an efficient reinforcement connecting algorithm. The policy-making possibilities of the reinforcement connecting algorithm are evaluated. The Fast Regional Convolutional Neural Network (Fast-RCNN) is designed and trained on a small collection of samples, then utilised for removing the wrong targets. The proposed framework was tested on videos acquired through various scenarios. The methodology illustrates its capacity through the automatic supervision of target vehicles in real-world trials, which demonstrates its potential applications in intelligent transport systems and other surveillance applications.
Recent improvements in robotics and computer vision enable new camera-equipped drone applications. Aerial Object Detection (OD) is one. Despite recent advances, computer vision OD remains difficult. Due to UAVs' fast speed, different views, and fluctuating altitudes, objects in Unmanned Aerial Vehicle (UAV) photos are heterogeneous, fluctuate in size, and are dense, making OD challenging with existing algorithms. Existing object recognition algorithms perform worse on UAV images because OD in aerial images is more difficult than in ground-taken images. If a generic object detection technique is used to drone-captured images, its performance will be drastically degraded owing to the fact that varied surroundings with complicated background and the size of objects are at the core of this phenomena. In this work, an object detection model CHA-YOLOv5 is proposed. In the proposed system the detection module is optimized using the Color Harmony Algorithm, as it determines the prediction head from the available three prediction heads. CHA-YOLOv5 accurately predicts several bounding boxes per grid cell. The proposed model has been trained using several images from challenging contexts. The experimental results show that the objects are identified accurately using the proposed improved network model in this study. It achieved precision 98.675%, pecall 97.8023%, mAP 97.23%, F1 score 98.50%. Our system outperformed the YOLOV5, ResNet50, VGG19, and InceptionNet V3.
Abstract. Monitoring Reservoir-induced landslides is critical for determining and mitigating the geo-environmental risks associated with the artificial reservoir. This paper employs Persistent Scatterer Interferometry (PSI) technique for the mapping and monitoring of Reservoir Induced Landslides in the rim area of Baglihar Dam Reservoir on Chenab river (Jammu and Kashmir, India) along National Highway NH-244. It has been evident that the slope movements are necessarily impacted by the reservoir drawdown effect (RDE) as well as the rainfall. We have utilised 46 Sentinel-1A C-band radar satellite imagery of the study area. The monitoring results have yielded the Line of Sight slope movement along the reservoir rim varies from −97 mm/year to +80 mm/year. The temporal correlation between the displacements and the local precipitation (derived from TRMM using Google Earth Engine) was qualitatively analysed. Two unstable slopes have been identified and monitored.
Abstract. Unmanned Aerial Vehicles (UAVs) are used as stand-alone systems for a variety of purposes from agriculture, and environmental monitoring through architecture, and construction to humanitarian missions. The advantage of UAV is high spatial and temporal resolution but on the other hand, the disadvantage is the small area of cover and time demanding data collection. As a result of technological advancements, the complexity of systems to unprecedented levels these disadvantages can be solved by UAV Swarm systems. A UAV Swarm system is defined as the utilization of more than one UAV that are cooperating together in a semi-autonomous or autonomous manner to achieve a common goal. There are numerous factors in play while designing a system as advanced as a UAV Swarm. In our experiment, we focused on the semi-autonomous concept of creating and deploying a UAV Swarm with three Small UAVs in master-slave architecture for high-resolution fine-scale mapping. We demonstrate the implementation of collective behaviour of UAV swarm for river bed mapping that considers all on-board systems, including high resolution georeferenced aerial photography and navigation using high accuracy GPS. The testing field for this study was a 13.3 ha linear area of Solani River in the Haridwar district of the state of Uttarakhand, India. Images were captured by all three UAVs (one leader and two followers) and 5 ground control points (GCP) were used for geo-referencing. Aerial Triangulation and Bundle Block adjustment were processed by photogrammetric software Pix4DMapper. This UAV swarm mapping concept generates standard accurate geospatial results of 1.24 cm GSD and RMS Error 0.023 meter. Assessing the proposed system's efficiency and accuracy after such processes are taken into account reduces the time and cost manifolds of the UAV surveying.
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