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.

This study presents a novel method for high-altitude terrain following photogrammetric mapping using Unmanned Aerial Vehicles (UAVs) that adapt their height to the terrain's morphology. Traditional grid missions in UAV mapping surveys use nadir photographs to cover an area of interest which is taken from a fixed height from the datum.Many times this leads to difficulties in flight mission and processing when hilly/undulated terrain is under consideration. The proposed solution addresses this by adjusting the UAV's flying height based on real-time terrain information gathered by a range detection sensor. This paper tests the proposed method in a hilly area in the Himalayas, capturing high-resolution images and processing them using UAV-based photogrammetry software. The pre-acquired terrain information ensures safe separation from obstacles and enables trajectory generation at lower altitudes when planning initial flights. The experimental results validate the effectiveness of the proposed method and highlight its potential for generating high-resolution photogrammetric products applicable to various UAV mapping tasks and monitoring scenarios. Overall, this research contributes to advancing the field of UAV mapping by providing a practical and effective solution for UAV based surveys in hilly and undulated terrains. The findings of this study have significant implications for various applications, including land surveying, environmental monitoring, landslide mapping and other scenarios that require detailed and accurate data acquisition from UAVs.