Low-cost Drones Research Articles

UAV Remote Sensing for delineation of Urban Vegetation using Object Based Image Analysis

Remote sensing technology has rapidly advanced during the last few decades and the number of remote sensing platforms has increased. The development of Unmanned Aerial Vehicles (UAV) image acquisition systems has radically changed the aerial Photogrammetric mapping due to its low cost, high spatial resolution and high accuracy and provide a great potential for vegetation mapping in urban areas. Urban environment planning becomes a challenging task for urban planners due to fast urbanization processes and growth of population. Urban land use is a crucial information for planning authorities and there is a growing demand for urban land cover maps for decision-making procedures in urban planning. In this article, we demonstrate a rule sets knowledge-based classification method, in object oriented classification which is a fully automated and highly accurate engineering approach for demarcation of urban vegetation with the use of eCognition software. DJI Phantom 4, consumer grade drone was used to acquire high resolution aerial photos as an input dataset in the study. In this study, vegetation mapping was done using the textural and contextual information acquired from the RGB image (Orthophoto) without using any Near Infrared (NIR) information and a Digital Surface Model (DSM) which was developed using Pix4D software, used as an ancillary data in the classification process in order to obtain the elevation information. The extraction of tree canopy and the buildings in a coastal urban environment has used to illustrate the analysis. The DSM was validated using ground control points observed by field measurements. The resultant urban vegetation map was validated with the digitized land use layer and an overall accuracy of 90.15% was obtained. This study indicates that low cost drones compared to the survey grade drones, also can provide high accurate and high resolution products suitable for many urban local area planning studies. Keywords: Photogrammetry, Object oriented classification, DSM, Orthophoto, Urban planning

DroneTrack: Cloud-Based Real-Time Object Tracking Using Unmanned Aerial Vehicles Over the Internet

Low-cost drones represent an emerging technology that opens the horizon for new smart Internet-of-Things (IoT) applications. Recent research efforts in cloud robotics are pushing for the integration of low-cost robots and drones with the cloud and the IoT. However, the performance of real-time cloud robotics systems remains a fundamental challenge that demands further investigation. In this paper, we present DroneTrack, a real-time object tracking system using a drone that follows a moving object over the Internet. The DroneTrack leverages the use of Dronemap planner (DP), a cloud-based system, for the control, communication, and management of drones over the Internet. The main contributions of this paper consist in: (1) the development and deployment of the DroneTrack, a real-time object tracking application through the DP cloud platform and (2) a comprehensive experimental study of the real-time performance of the tracking application. We note that the tracking does not imply computer vision techniques but it is rather based on the exchange of GPS locations through the cloud. Three scenarios are used for conducting various experiments with real and simulated drones. The experimental study demonstrates the effectiveness of the DroneTrack system, and a tracking accuracy of 3.5 meters in average is achieved with slow-speed moving targets.

THE XII CENTURY TOWERS, A BENCHMARK OF THE ROME COUNTRYSIDE ALMOST CANCELLED: THE SAFEGUARD PLAN BY LOW COST UAV AND TERRESTRIAL DSM PHOTOGRAMMETRY SURVEYING AND 3D WEB GIS APPLICATIONS

Abstract. “Giving a bird-fly look at the Rome countryside, throughout the Middle Age central period, it would show as if the multiple city towers has been widely spread around the territory” on a radial range of maximum thirty kilometers far from the Capitol Hill center (Carocci and Vendittelli, 2004). This is the consequence of the phenomenon identified with the “Incasalamento” neologism, described in depth in the following paper, intended as the general process of expansion of the urban society interests outside the downtown limits, started from the half of the XII and developed through all the XIII century, slowing down and ending in the following years. From the XIX century till today the architectural finds of this reality have raised the interest of many national and international scientists, which aimed to study and catalog them all to create a complete framework that, cause of its extension, didn’t allow yet attempting any element by element detailed analysis. From the described situation has started our plan of intervention, we will apply integrated survey methods and technologies of terrestrial and UAV near stereo-photogrammetry, by the use of low cost drones, more than action cameras and reflex on extensible rods, integrated and referenced with GPS and topographic survey. In the final project we intend to produce some 3D scaled and textured surface models of any artifact (almost two hundreds were firstly observed still standing), to singularly study the dimensions and structure, to analyze the building materials and details and to formulate an hypothesis about any function, based even on the position along the territory. These models, successively georeferenced, will be imported into a 2D and 3D WebGIS and organized in layers made visible on basemaps of reference, as much as on historical maps.

Drones as a Tool for Monoculture Plantation Assessment in the Steepland Tropics

Smallholder tree plantations are expanding in the steepland tropics due to demand for timber and interest in ecosystem services, such as carbon storage. Financial mechanisms are developing to compensate vegetation carbon stores. However, measuring biomass—necessary for accessing carbon funds—at small scales is costly and time-intensive. Therefore, we test whether low-cost drones can accurately estimate height and biomass in monoculture plantations in the tropics. We used Ecosynth, a drone-based structure from motion technique, to build 3D vegetation models from drone photographs. These data were filtered to create a digital terrain model (DTM) and digital surface model (DSM). Two different canopy height models (CHMs) from the Ecosynth DSM were obtained by subtracting terrain elevations from the Ecosynth DTM and a LIDAR DTM. We compared height and biomass derived from these CHMs to field data. Both CHMs accurately predicted the height of all species combined; however, the CHM from the LiDAR DTM predicted heights and biomass on a per-species basis more accurately. Height and biomass estimates were strong for evergreen single-stemmed trees, and unreliable for small leaf-off species during the dry season. This study demonstrates that drones can estimate plantation biomass for select species when used with an accurate DTM.

Learning to Program in Python – by Teaching It!

The US Bureau of Labor Statistics predicts over 8 million job openings in IT and computing, including 1 million cybersecurity postings, over the current five-year period. This paper presents lessons learned in preparing middle-school students in rural Georgia for future careers in computer science/ IT by teaching computer programming in the free, open-source programming language Python using Turtle graphics, and discusses exercises and activities with low-cost drones, bots, and 3D printers to get students interested and keep them engaged in coding. Described herein is one pair of instructors’ (one middle-school, one university) multi-year, multi-stage approach to providing engineering and technology courses, including: how to code Turtle graphics in Python; how to engage children by using short, interactive, visual programs for every age level; building cross-curricular bridges toward technology careers using 3D printing, robotics, and low-cost drones; and, how to build more advanced programming skills in Python.

Mission architecture for Mars exploration based on small satellites and planetary drones

Purpose The purpose of this paper is to deal with the study of an innovative unmanned mission to Mars, which is aimed at acquiring a great amount of detailed data related to both Mars’ atmosphere and surface. Design/methodology/approach The Mars surface exploration is conceived by means of a fleet of drones flying among a set of reference points (acting also as entry capsules and charging stations) on the surface. The three key enabling technologies of the proposed mission are the use of small satellites (used in constellation with a minimum of three), the use of electric propulsion systems for the interplanetary transfer (to reduce the propellant mass fraction) and lightweight, efficient, drones designed to operate in the harsh Mars environment and with its tiny atmosphere. Findings The low-thrust Earth-Mars transfer is designed by means of an optimization approach resulting in a duration of slightly more than 27 months with a propellant amount of about 125 kg, which is compatible with the choice of considering a 500 kg-class spacecraft. Four candidate drone configurations have been selected as the result of a sensitivity analysis. Flight endurance, weight and drone size have been considered as the driving design parameters for the selection of the final configuration, which is characterized by six rotors, a total mass of about 6.5 kg and a flight endurance of 28 minutes. In the mission scenario proposed, the drone is assumed to be delivered on the Mars surface by means of a passive entry capsule, which acts also as a docking station and charging base. Such a capsule has been sized both in terms of mass (68 kg) and power (80 W), showing to be compatible with 500 kg-class spacecraft. Research limitations/implications As a general conclusion, the study shows the mission concept feasibility. Practical implications The concept would return incomparable scientific data and can be also be potentially implemented with a relatively low budget exploiting of the shelf components to the larger extent, small identical spacecraft buses and modular low-cost drones. Originality/value The innovative mission architecture proposed in this study aims at providing a complete coverage of the surface and lowest atmospheric layers. The main innovation factor of the proposed mission consists in the adoption of small multi-copter UAVs, also called “drones,” as remote-sensing platforms.

Small Drones for Community-Based Forest Monitoring: An Assessment of Their Feasibility and Potential in Tropical Areas

Data gathered through community-based forest monitoring (CBFM) programs may be as accurate as those gathered by professional scientists, but acquired at a much lower cost and capable of providing more detailed data about the occurrence, extent and drivers of forest loss, degradation and regrowth at the community scale. In addition, CBFM enables greater survey repeatability. Therefore, CBFM should be a fundamental component of national forest monitoring systems and programs to measure, report and verify (MRV) REDD+ activities. To contribute to the development of more effective approaches to CBFM, in this paper we assess: (1) the feasibility of using small, low-cost drones (i.e., remotely piloted aerial vehicles) in CBFM programs; (2) their potential advantages and disadvantages for communities, partner organizations and forest data end-users; and (3) to what extent their utilization, coupled with ground surveys and local ecological knowledge, would improve tropical forest monitoring. To do so, we reviewed the existing literature regarding environmental applications of drones, including forest monitoring, and drew on our own firsthand experience flying small drones to map and monitor tropical forests and training people to operate them. We believe that the utilization of small drones can enhance CBFM and that this approach is feasible in many locations throughout the tropics if some degree of external assistance and funding is provided to communities. We suggest that the use of small drones can help tropical communities to better manage and conserve their forests whilst benefiting partner organizations, governments and forest data end-users, particularly those engaged in forestry, biodiversity conservation and climate change mitigation projects such as REDD+.

Dispatches

Dispatches

Modelling Oil-Spill Detection with Swarm Drones

Nowadays, swarm robotics research is having a great increase due to the benefits derived from its use, such as robustness, parallelism, and flexibility. Unlike distributed robotic systems, swarm robotics emphasizes a large number of robots, and promotes scalability. Among the multiple applications of such systems we could find are exploring unstructured environments, resource monitoring, or distributed sensing. Two of these applications, monitoring, and perimeter/area detection of a given resource, have several ecological uses. One of them is the detection and monitoring of pollutants to delimit their perimeter and area accurately. Maritime activity has been increasing gradually in recent years. Many ships carry products such as oil that can adversely affect the environment. Such products can produce high levels of pollution in case of being spilled into sea. In this paper we will present a distributed system which monitors, covers, and surrounds a resource by using a swarm of homogeneous low cost drones. These drones only use their local sensory information and do not require any direct communication between them. Taking into account the properties of this kind of oil spills we will present a microscopic model for a swarm of drones, capable of monitoring these spills properly. Furthermore, we will analyse the proper macroscopic operation of the swarm. The analytical and experimental results presented here show the proper evolution of our system.