Capabilities Of Unmanned Aerial Vehicles Research Articles

UAV-Aided Edge/Fog Computing in Smart IoT Community for Social Augmented Reality

In 5G and beyond, the adequate interaction between densely deployed Internet-of-Things (IoT) devices and cellular users will generate a massive cyber-physical information stream in a real-time manner. How to capture insights underneath these data in a smart city context is gaining great attention nowadays. In this article, we introduce a highly function-differentiated metropolitan scenario, which is covered by multiple unmanned aerial vehicles (UAVs) serving as cache-enabled edge computing nodes. With the help of wireless backhaul technology, coverage capability of UAV can be dynamically configured through smart 3-D placement, where trajectories of two types of UAVs are optimized. A social augmented reality (AR)-based use case is proposed and discussed in the proposed scenario, from which we derive the fundamental mechanisms and strategies to maintain a green and sustainable edge/fog computing framework. We sequentially establish two nonconvex programming problems and optimize delay and energy performance during AR data acquisition and AR content downloading, respectively. Two convex approximation skills are applied to transform the original problems into tractable form. The experimental results show that our proposed edge computing framework can help provide energy-efficient AR service to cellular users, catering to pretty tight delay constraints.

Performance Analysis of Medium Altitude Low-Cost Autonomous Quadcopter

In modern engineering, the rapid growth in the development of unmanned aerial vehicles which deserves the major space in the area of intelligence and surveillance operations. Unlike the conventional aircraft, the number of rotors mounted in an autonomous body that determines the propulsive efficiency as well as the maneuvering capabilities of unmanned aerial vehicles. The main objective of this research reveals that the low cost and an autonomous aerial vehicle with four rotors configuration design to achieve the long endurance flight limit. The developed Quadcopter flying model integrated with Arduino autopilot module which effectively controls the various maneuvering actions during the flight. This paper also reveals the entire hardware structure of the designed Quadcopter and its flight dynamic response characteristics. The remotely operated Quadcopter model was subjected to the flight test and the performance parameters have recorded. The flight test was conducted in an open environment in which stability parameters has been observed during onboard. Pitch angle, the Rotational speed of four rotors and the various attitudes of Quadcopter were changed accordingly to maintain the steady level flight. Findings are plotted with respect to the flight endurance.

Using Ship-Deployed High-Endurance Unmanned Aerial Vehicles for the Study of Ocean Surface and Atmospheric Boundary Layer Processes

Unmanned aerial vehicles (UAVs) are proving to be an important modern sensing platform that supplement the sensing capabilities from platforms such as satellites, aircraft, research vessels, moorings, and gliders. UAVs, like satellites and aircraft can provide a synoptic view of a relatively large area. However, the coarse resolution provided by satellites and the operational limitations of manned aircraft has motivated the development of unmanned systems. UAVs offer unparalleled flexibility of tasking; for example, low altitude flight and slow airspeed allow for the characterization of a wide variety of geophysical phenomena at the ocean surface and in the marine atmospheric boundary layer. Here, we present the development of cutting-edge payload instrumentation for UAVs that provides a new capability for ship-deployed operations to capture a unique, high resolution spatial and temporal variability of the changing air-sea interaction processes than was previously possible. The modular design of the base payload means that new instruments can be incorporated into new research proposals that may include new instruments for expanded use of the payloads as a long-term research facility. Additionally, we implement a novel capability for vertical take-off and landing (VTOL) from research vessels. This VTOL capability is safer and requires less logistical support than previous ship-deployed systems. The payloads developed include thermal infrared, visible broadband and hyperspectral, and near-infrared hyperspectral high-resolution imaging. Additional capabilities include quantification of the longwave and shortwave hemispheric radiation budget (up- and down-welling) as well as direct air-sea turbulent fluxes. Finally, a UAV-deployed dropsonde-microbuoy was developed in order to profile the temperature, pressure and humidity of the atmosphere and the temperature and salinity of the near-surface ocean. These technological advancements provide the next generation of instrumentation capability for UAVs. When deployed from research vessels, UAVs will provide a transformational science prism unequaled using 1-D data snapshots from ships or moorings alone.

SIDR: A Swarm Intelligence-Based Damage-Resilient Mechanism for UAV Swarm Networks

Unmanned Aerial Vehicle (UAV) swarm networks have been presented as a promising paradigm for conducting monitoring, and inter-connecting tasks in unattended or even hostile environments. However, harsh deployment scenario may make the UAVs susceptible to large-scale damage, and thus degrades the connectivity and performance of the network. None of existing technologies can effectively re-organize the surviving UAVs in a severely damaged UAV swarm into a unified UAV Swarm Network (USNET), this paper presents and analyzes the damage-resilience problem of USNETs for the first time, and put forwards a Swarm Intelligence-based Damage-Resilient (SIDR) mechanism. First, a damage model of USNETs and several metrics are defined before the problem is formally formulated. Second, the SIDR mechanism is detailed based on comprehensively utilizing the storage, communication, positioning, and maneuvering capabilities of UAVs. Third, a potential-field-based solution to the proposed SIDR mechanism is presented, aiming to recover a USNET rapidly and elastically. At last, an evaluation environment is built on the OMNeT++ platform, and the proposed SIDR mechanism is implemented. Extensive simulations are conducted in both dynamic and static scenarios. Simulation results demonstrate that SIDR outperforms the existing algorithms in terms of resilience capability, convergence time and communication overhead. Even if a USNET is divided into multiple disjoint subnets with arbitrary shape, SIDR can aggregate the surviving nodes into a connected network while the network is still flying along the flight path during the recovery process.

Maneuver Decision of UAV in Short-Range Air Combat Based on Deep Reinforcement Learning

With the development of artificial intelligence and integrated sensor technologies, unmanned aerial vehicles (UAVs) are more and more applied in the air combats. A bottleneck that constrains the capability of UAVs against manned vehicles is the autonomous maneuver decision, which is a very challenging problem in the short-range air combat undergoing highly dynamic and uncertain maneuvers of enemies. In this paper, an autonomous maneuver decision model is proposed for the UAV short-range air combat based on reinforcement learning, which mainly includes the aircraft motion model, one-to-one short-range air combat evaluation model and the maneuver decision model based on deep Q network (DQN). However, such model includes a high dimensional state and action space which requires huge computation load for DQN training using traditional methods. Then, a phased training method, called “basic-confrontation”, which is based on the idea that human beings gradually learn from simple to complex is proposed to help reduce the training time while getting suboptimal but efficient results. Finally, one-to-one short-range air combats are simulated under different target maneuver policies. Simulation results show that the proposed maneuver decision model and training method can help the UAV achieve autonomous decision in the air combats and obtain an effective decision policy to defeat the opponent.

Infrared and Visible Airborne Targets Image Fusion with Applications to Sense and Avoid

Machine vision has revealed great potential in recent years for Sense and Avoid (SAA) ability of Unmanned Aerial Vehicle (UAV). However, the target perception capability of machine vision largely depends on illumination, which restricts UAV to move safely in dark environment. Since images acquired by infrared and visible sensors are complementary in most cases, enhancing image qualities in dark environments by fusion of infrared and visible images is a promising solution. By considering the difficulties of image fusion for airborne targets, a Convolutional Sparse Representation (CSR) based infrared and visible airborne targets image fusion algorithm is proposed in this paper for enhancing SAA capability of UAV in dark environments, which contains three parts: image decomposition, image transformation and image reconstruction. A series of registered infrared and visible images containing airborne targets are selected to evaluate the algorithm proposed in this paper. Simulation results demonstrate the algorithm proposed in this paper effectively increases image qualities in dark environments. In the aspects of fusion metrics, the algorithm proposed in this paper can achieve favorable performance against other image fusion algorithms.

Conceptual aspects of improving legislation and law enforcement practice in use of robot systems to ensure traffic safety in large cities

The article provides a general description of the legislation for the use of unmanned aerial vehicles (UAVs) in the Russian Federation and some foreign countries. Special attention is paid to the promising directions of UAV use in the process of ensuring traffic safety and regulatory restrictions that prevent the use of UAVs over highways and in the airspace of large cities (megacities). We analyze UAV capabilities in the implementation of the main administrative procedures (actions) to study the circumstances of road traffic accidents (RTAs), as well as gaps in legislation related to the implementation of these procedures. Based on the research, including comparative legal studies, we propose a “road map” for improving legislation aimed at protecting the interests of a person, society, and the state against the negative consequences of RTAs through the use of UAVs.

CORRELATION OF UAV-BASED MULTISPECTRAL VEGETATION INDICES AND LEAF COLOR CHART OBSERVATIONS FOR NITROGEN CONCENTRATION ASSESSMENT ON RICE CROPS

Abstract. Fertilizer application is a crucial farming operation for regulating crop health thus crop yield. Optimal fertilizing doubles agricultural production subsequently raising farmers’ income, food security and economic agriproducts. To optimize the application of fertilizers, initial monitoring of the current nutrient status of the crops is required. This research will focus on Nitrogen (N), the most extensive fertilizer nutrient in crop cultivation. Conventional N monitoring involves the use of Leaf Color Charts (LCC) wherein leaf color intensity is associated with the N content of the crops. Despite its ability to quantify the optimal amount of needed fertilizers, the LCC method requires extensive on-site labor and lacks accuracy. This study developed a method that incorporates capabilities of Unmanned Aerial Vehicles (UAVs) equipped with a multispectral sensor in N monitoring specifically in rice crops, a major agricultural product in the Philippines. In situ N level information collected through LCC was correlated with remote sensing data, particularly vegetation indices (VIs) extracted from UAV multispectral imagery of a rice plantation in San Rafael, Bulacan. Several VIs sensitive to crop N content were tested to determine which has the highest correlation with the LCC data. Through Pearson correlation and regression analysis, NDVIRed Edge was found to be the most strongly correlated with LCC data suggesting its potential in mapping variability in fertilizer requirements. An equation modelling LCC observations and NDVIRed Edge values that estimates the N levels of an entire rice plantation was generated along with the N concentration map of the study area.

Application o f Inn ovative Aerospace Technologies f or Pastoral Farming o f Sheeps

Development of an innovative system of livestock husbandry based on the use of digital aerospace technologies and telemetry is a new modern direction in the development of the livestock industry, designed to solve the many problems of restoring fertility of soil and pasture for animals. To develop a methodology for remote assessment of pasture fertility, we used the technological capabilities of using unmanned aerial vehicles and satellite service, which allows us to study the dynamics of the NDVI (Normalized Difference Vegetation Index) of various pasture plots in the Stavropol Territory of the Russian Federation. The analysis of literary sources shows that the main problems associated with the complex automation of forecasting processes for complex objects are not technical, but methodological in nature and are caused by the lack of a theoretical base that should form the basis for creating the corresponding model and algorithmic support. A comparison of the results of a prognostic assessment of the nutritional value of fodder plants obtained from unmanned aerial vehicle (UAV) images, space services with actual nutritional values obtained from studies of the zoochemical composition of feeds showed a high degree of correlation. The UAV capabilities were recognized as promising for assessing the ethological characteristics of the Manych merino sheep, which allows to optimize the acquisition of groups of animals consolidated by forage activity. The article considers the issues of assessing the nutritional value of pasture feed and the vegetation index when raising sheep of the Jalgin merino breed in the conditions of the steppe regions of the Stavropol Territory. The introduction of remote assessment methods in pastoral livestock can optimize the cultivation of various sex and age groups of sheep and reduce the time to achieve production parameters by 5-8%.

THE CAPABILITIES OF UNMANNED AERIAL VEHICLE FOR SLOPE CLASSIFICATION

Abstract. Slope classification mapping is an important component of land suitability analysis for preventing landslides. This study aim to investigate the capabilities and application of Unmanned Aerial Vehicle (UAV) platform for slope classification. The objectives of this study such as investigating the capabilities of UAV for slope classification, generating Digital Elevation Model (DEM) and orthophoto from the image acquired and assessing the accuracy of DEM and orthophoto produced for slope classification. In this study, the aerial image was acquired using UAV at 60 m and 40 m altitude will then generates the DEM and orthophoto used to produce the slope map and classify the slope. The UAV data was validated with the check points observed from ground survey using GPS to obtain the Root Mean Square Error (RMSE) values. The RMSE value for UAV derived DEM at 60 m altitude is ±0.234 m and ±0.604 m for X and Y respectively. The average RMSE is ±0.279 m. The average RMSE value obtained from LiDAR derived DEM in previous research is ±0.616 m. The RMSE value for UAV derived DEM at 40 m altitude is ±0.596 m and ±0.405 for X and Y respectively. The average RMSE is ±0.334 m. The average RMSE value obtained from LiDAR derived DEM in previous research is ±0.450 m. In conclusion, it shows that the RMSE value obtained from UAV derived DEM is smaller than the RMSE value obtained from LiDAR derived DEM. Hence, UAV is capable for the generation of slope map and slope classification.

New Technical Means In The System Of Control And Logistics Of Construction

The emergence and development of machine learning technologies, unmanned aerial vehicles, 3D-modeling technologies integrated with the concept of "smart construction" significantly expand the possibilities of creating and applying the concept of an effective logistics system for building production at the stage of construction and installation works. At the same time, there is a need to develop a model for creating, processing, storing, issuing and visualizing information on the state of construction production from the point of view of the material and technical support system. The article formulates and considers the concept in the form of electronic automated model with the use of unmanned aerial vehicles, presents the mechanism of monitoring and control of construction and installation works and logistics. The capabilities of unmanned aerial vehicles, such as visual inspection of buildings, measurements of structures, monitoring of the construction process, control of work performed, monitoring of the timing and cost of work, etc. were determined. Advantages and disadvantages of this system are revealed. The concept developed on the basis of the use of unmanned aerial vehicles makes it possible to create a 3D model, compare its graphic and digital characteristics with the project, identify deviations and defects, the amount of material and technical resources necessary for the uninterrupted construction process, automate and reduce the cost of the process of logistics.

Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data

High resolution mapping of coastal habitats is invaluable for resource inventory, change detection, and inventory of aquaculture applications. However, coastal areas, especially the interior of mangroves, are often difficult to access. An Unmanned Aerial Vehicle (UAV), equipped with a multispectral sensor, affords an opportunity to improve upon satellite imagery for coastal management because of the very high spatial resolution, multispectral capability, and opportunity to collect real-time observations. Despite the recent and rapid development of UAV mapping applications, few articles have quantitatively compared how much improvement there is of UAV multispectral mapping methods compared to more conventional remote sensing data such as satellite imagery. The objective of this paper is to quantitatively demonstrate the improvements of a multispectral UAV mapping technique for higher resolution images used for advanced mapping and assessing coastal land cover. We performed multispectral UAV mapping fieldwork trials over Indian River Lagoon along the central Atlantic coast of Florida. Ground Control Points (GCPs) were collected to generate a rigorous geo-referenced dataset of UAV imagery and support comparison to geo-referenced satellite and aerial imagery. Multi-spectral satellite imagery (Sentinel-2) was also acquired to map land cover for the same region. NDVI and object-oriented classification methods were used for comparison between UAV and satellite mapping capabilities. Compared with aerial images acquired from Florida Department of Environmental Protection, the UAV multi-spectral mapping method used in this study provided advanced information of the physical conditions of the study area, an improved land feature delineation, and a significantly better mapping product than satellite imagery with coarser resolution. The study demonstrates a replicable UAV multi-spectral mapping method useful for study sites that lack high quality data.

Research on Duration Estimation of Rotor UAV Based on Flight Condition-Energy Consumption Identification

The quad-rotor UAV (Unmanned aerial vehicle) has a wide application market for its simple structure, easy operation and strong adaptability. During the flight, the endurance of UAV is an important parameter for flight planning, and it is of great significance to master the endurance capability of UAV. The endurance of UAV is mainly decided by the remaining capacity of the battery and the future energy consumption which changes with flight conditions. In this paper, a Flight Condition-Energy Consumption Model is established by analysing a large number of flight data with machine learning method, and the effects of different regression algorithms are compared. The validity of the model is verified by the actual flight. The endurance of the aircraft can be estimated using this model with the given remaining capacity of the battery and the future flight tasks.

A Low Cost Approach to Disturbed Soil Detection Using Low Altitude Digital Imagery from an Unmanned Aerial Vehicle

Until recently, clandestine burial investigations relied upon witness statements to determine target search areas of soil and vegetation disturbance. Due to this, remote sensing technologies are increasingly used to detect fresh clandestine graves. However, despite the increased capabilities of remote sensing, clandestine burial searches remain resourcefully intensive as the police have little access to the technology when it is required. In contrast to this, Unmanned Aerial Vehicle (UAV) technology is increasingly popular amongst law enforcement worldwide. As such, this paper explores the use of digital imagery collected from a low cost UAV for the aided detection of disturbed soil sites indicative of fresh clandestine graves. This is done by assessing the unaltered UAV video output using image processing tools to detect sites of disturbance, therefore highlighting previously unrecognised capabilities of police UAVs. This preliminary investigation provides a low cost rapid approach to detecting fresh clandestine graves, further supporting the use of UAV technology by UK police.

Assessment of maize yield and phenology by drone-mounted superspectral camera

The capability of unmanned aerial vehicle (UAV) spectral imagery to assess maize yield under full and deficit irrigation is demonstrated by a Tetracam MiniMCA12 11 bands camera. The MiniMCA12 was used to image an experimental field of 19 maize hybrids. Yield prediction models were explored for different maize development stages, with the best model found using maize plant development stage reproductive 2 (R2) for both maize grain yield and ear weight (respective R2 values of 0.73 and 0.49, and root mean square error of validation (RMSEV) values of 2.07 and 3.41 metric tons per hectare using partial least squares regression (PLS-R) validation models). Models using vegetation indices for inputs rather than superspectral data showed similar R2 but higher RMSEV values, and produced best results for the R4 development stage. In addition to being able to predict yield, spectral models were able to distinguish between different development stages and irrigation treatments. These abilities potentially allow for yield prediction of maize plants whose development stage and water status are unknown.

AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming

The combination of aerial survey capabilities of Unmanned Aerial Vehicles with targeted intervention abilities of agricultural Unmanned Ground Vehicles can significantly improve the effectiveness of robotic systems applied to precision agriculture. In this context, building and updating a common map of the field is an essential but challenging task. The maps built using robots of different types show differences in size, resolution and scale, the associated geolocation data may be inaccurate and biased, while the repetitiveness of both visual appearance and geometric structures found within agricultural contexts render classical map merging techniques ineffective. In this paper we propose AgriColMap, a novel map registration pipeline that leverages a grid-based multimodal environment representation which includes a vegetation index map and a Digital Surface Model. We cast the data association problem between maps built from UAVs and UGVs as a multimodal, large displacement dense optical flow estimation. The dominant, coherent flows, selected using a voting scheme, are used as point-to-point correspondences to infer a preliminary non-rigid alignment between the maps. A final refinement is then performed, by exploiting only meaningful parts of the registered maps. We evaluate our system using real world data for 3 fields with different crop species. The results show that our method outperforms several state of the art map registration and matching techniques by a large margin, and has a higher tolerance to large initial misalignments. We release an implementation of the proposed approach along with the acquired datasets with this paper.

Analysis and Optimization of Unmanned Aerial Vehicle Swarms in Logistics: An Intelligent Delivery Platform

Deploying unmanned aerial vehicle (UAV) swarms in delivery systems are still in its infancy with regard to the technology, safety, and aviation rules and regulations. Optimal use of UAVs in dynamic environments is important in many aspects, e.g., increasing efficacy and reducing the air traffic, resulting in a safer environment, and it requires new techniques and robust approaches based on the capabilities of UAVs and constraints. This paper analyzes several delivery schemes within a platform, such as delivery with and without using air highways and delivery using a hybrid scheme along with several delivery methods (i.e., optimal, premium, and first-in first-out) to explore the use of UAV swarms as part of the logistics operations. In this platform, a dimension reduction technique, “dynamic multiple assignments in multi-dimensional space,” and several other new techniques along with Hungarian and cross-entropy Monte Carlo techniques are forged together to assign tasks and plan 3D routes dynamically. This particular approach is performed in such a way that UAV swarms in several warehouses are deployed optimally given the delivery scheme, method, and constraints. Several scenarios are tested on the simulator using small and big data sets. The results show that the distribution and the characteristics of data sets and constraints affect the decision on choosing the optimal delivery scheme and the method. The findings are expected to guide the aviation authorities in their decisions before dictating rules and regulations regarding effective, efficient, and safe use of UAVs. Furthermore, the companies that produce UAVs are going to take the demonstrated results into account for their functional design of UAVs along with other companies that aim to deliver their products using UAVs. Additionally, private industries, logistics operators, and municipalities are expected to benefit from the potential adoption of the simulator in strategic decisions before embarking on the practical implementation of UAV delivery systems.

Vision-Based Flying Targets Detection via Spatiotemporal Context Fusion

Deriving from the imperative necessities for developing Sense and Avoid (SAA) capability of Unmanned Aerial Vehicle (UAV), a newly designed flying targets detection algorithm is presented in this paper for enhancing the UAV environment perception ability. Since spatiotemporal context is crucial for insuring the effectiveness of flying targets detection, the algorithm is constructed on the basis of spatiotemporal context fusion. The algorithm proposed in this paper contains three parts, namely the spatial context extraction, temporal context extraction and spatiotemporal context fusion. 1) In order to extract spatial context, dense sampling method is firstly applied to obtain dense image grids, then spatial context is generated via pre-learned conditional random field (CRF) model using a layered structure: dense image patches, bottom feature descriptors, sparse codes, and predicted CRF labels. 2) In order to extract temporal context, the forward and back motion history image (FBMHI) is firstly computed for detecting motion cues, and the adaptive foreground and background isolation is further adopted for acquiring the temporal probability map. 3) The presence probability map of flying targets is finally obtained by spatiotemporal context fusion, and flying targets is therefore picked out by analyzing fused presence probability map. A set of videos containing different drone models are selected for evaluation, and the comparisons against other algorithms demonstrate superiority of the proposed algorithm.

Interference Management in UAV-Assisted Integrated Access and Backhaul Cellular Networks

An integrated access and backhaul (IAB) network architecture can enable flexible and fast deployment of the next-generation cellular networks. However, mutual interference between access and backhaul links, small inter-site distance, and spatial dynamics of user distribution pose major challenges in the practical deployment of the IAB networks. To tackle these problems, we leverage the flying capabilities of unmanned aerial vehicles (UAVs) as hovering IAB-nodes and propose an interference management algorithm to maximize the overall sum rate of the IAB network. In particular, we jointly optimize the user and base station associations, the downlink power allocations for access and backhaul transmissions, and the spatial configurations of the UAVs. We consider two spatial configuration modes of the UAVs, distributed UAVs and drone antenna array (DAA), and show how they are intertwined with the spatial distribution of ground users. Our numerical results show that the proposed algorithm achieves an average of 2.9× and 6.7× gains in the received downlink signal-to-interference-plus-noise ratio (SINR) and overall network sum rate, respectively. Finally, the numerical results reveal that UAVs can not only be used for coverage improvement but also for capacity boosting in the IAB cellular networks.

NEW INITIATIVE OF UNMANNED AERIAL VEHICLE (UAV) EMERGING TECHNOLOGY APPLICATIONS IN NORTH EAST FOR CAPACITY BUILDING AND OUTREACH ACTIVITIES OF NORTH EASTERN SPACE APPLICATIONS CENTRE

Abstract. Unmanned Aerial Vehicle (UAV) technology is revolutionizing and acting as an alternative for many of remote sensing applications, particularly for very high resolution satellite requirements, considering easy of flying in the areas of persistent cloud cover like North East. According to top market reports, UAV business is growing at very fast rate. It is valued at USD 18.14 Billion in 2017 and is projected to reach USD 52.30 Billion by 2025, at a CAGR of 14.15% from 2018 to 2025.North Eastern Space Applications Centre, Umiam, Shillong, Meghalaya, which is responsible for promoting space technology tools for governance and development activities; has taken up a lead role in initiating use of Unmanned Aerial Vehicle for large number of applications such as natural resources management, infrastructure development, disaster response and rescue, project monitoring, research and development etc. Capacity building, training and outreach are important activities taken up by NESAC for promoting use of UAV remote sensing at central/state/academic/research institutions and individual level. As part of capacity building, NESAC has successfully organized two 2 weeks training program for UAV Remote sensing & Applications for the participants all over the country. NESAC has also organised 1 week training programs for officials from State Remote Sensing Centres of North Eastern Region and North Eastern Council. More than 100 participants have been trained from different part of the country. The focus of the training was to include all aspects of UAV Remote Sensing applications. The details of course curriculum are: basic concepts of UAV, building of UAVs, UAV flight simulation, flight planning, UAV data processing, generation of DSM/DTM/Orthomosaic, use of UAV data for different applications in the field of Agriculture, Disaster management, Forestry, Infrastructure planning, construction monitoring etc. Pilot project is also incorporated as part of the training. Apart from training programs at NESAC, large numbers of application projects (> 60 nos.) have been successfully completed. Outreach activities are also carried out which includes exhibiting UAVs at seminars, demonstration to large no. of students, showcasing UAV capabilities at disaster drills carried out by State Disaster Management Authority (SDMA) etc. The new initiatives of UAV convinced all concerned at taking up UAV RS applications for governance and developmental activities. The full paper will discuss all the aspects of UAV technology and applications.