Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001

An Approach Of Automatic Reconstruction Of Building Models For Virtual Cities From Open Resources

Along with the ever-increasing popularity of virtual reality technology in recent years, 3D city models have been used in different applications, such as urban planning, disaster management, tourism, entertainment, and video games. Currently, those models are mainly reconstructed from access-restricted data sources such as LiDAR point clouds, airborne images, satellite images, and UAV (uncrewed air vehicle) images with a focus on structural illustration of buildings’ contours and layouts. To help make 3D models closer to their real-life counterparts, this thesis research proposes a new approach for the automatic reconstruction of building models from open resources. In this approach, first, building shapes are reconstructed by using the structural and geographic information retrievable from the open repository of OpenStreetMap (OSM). Later, images available from the street view of Google maps are used to extract information of the exterior appearance of buildings for texture mapping onto their boundaries. The constructed 3D environment is used as prior knowledge for the navigation purposes in a self-driving car. The static objects from the 3D model are compared with the real-time images of static objects to reduce the computation time by eliminating them from the detection proces

Context-based urban terrain reconstruction from uav-videos for geoinformation applications

Urban terrain reconstruction has many applications in areas of civil engineering, urban planning, surveillance and defense research. Therefore the needs of covering ad-hoc demand and performing a close-range urban terrain reconstruction with miniaturized and relatively inexpensive sensor platforms are constantly growing. Using (miniaturized) unmanned aerial vehicles, (M) UAVs, represents one of the most attractive alternatives to conventional large-scale aerial imagery. We cover in this paper a four-step procedure of obtaining georeferenced 3D urban models from video sequences. The four steps of the procedure - orientation, dense reconstruction, urban terrain modeling and geo-referencing - are robust, straight-forward, and nearly fully-automatic. The two last steps - namely, urban terrain modeling from almost-nadir videos and co-registration of models - represent the main contribution of this work and will therefore be covered with more detail. The essential substeps of the third step include digital terrain model (DTM) extraction, segregation of buildings from vegetation, as well as instantiation of building and tree models. The last step is subdivided into quasi-intrasensorial registration of Euclidean reconstructions and intersensorial registration with a geo-referenced orthophoto. Finally, we present reconstruction results from a real data-set and outline ideas for future work

Military Application of Aerial Photogrammetry Mapping Assisted by Small Unmanned Air Vehicles

This research investigated the practical military applications of the photogrammetric methods using remote sensing assisted by small unmanned aerial vehicles (SUAVs). The research explored the feasibility of UAV aerial mapping in terms of the specific military purposes, focusing on the geolocational and measurement accuracy of the digital models, and image processing time. The research method involved experimental flight tests using low-cost Commercial off-the-shelf (COTS) components, sensors and image processing tools to study key features of the method required in military like location accuracy, time estimation, and measurement capability. Based on the results of the data analysis, two military applications are defined to justify the feasibility and utility of the methods. The first application is to assess the damage of an attacked military airfield using photogrammetric digital models. Using a hex-rotor test platform with Sony A6000 camera, georeferenced maps with 1 meter accuracy was produced and with sufficient resolution (about 1 cm/pixel) to identify foreign objects on the runway. The other case examines the utility and quality of the targeting system using geo-spatial data from reconstructed 3-Dimensional (3-D) photogrammetry models. By analyzing 3-D model, operable targeting under 1meter accuracy with only 5 percent error on distance, area, and volume wer

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Generation of Digital Surface Models (DSMs) from stereo satellite (spaceborne) images is classically performed by Ground Control Points (GCPs) which require site visits and precise measurement equipment. However, collection of GCPs is not always possible and such requirement limits the usage of spaceborne imagery. This study aims at developing a fast, fully automatic, GCP-free workflow for DSM generation. The problems caused by GCP-free workflow are overcome using freely-available, low resolution static DSMs (LR-DSM). LR-DSM is registered to the reference satellite image and the registered LR-DSM is used for i) correspondence generation and ii) initial estimate generation for 3-D reconstruction. Novel methods are developed for bias removal for LR-DSM registration and bias equalization for projection functions of satellite imaging. The LR-DSM registration is also shown to be useful for computing the parameters of simple, piecewise empirical projective models. Recent computer vision approaches on stereo correspondence generation and dense depth estimation are tested and adopted for spaceborne DSM generation. The study also presents a complete, fully automatic scheme for GCPfree DSM generation and demonstrates that GCP-free DSM generation is possible and can be performed in much faster time on computers. The resulting DSM can be used in various remote sensing applications including building extraction, disaster monitoring and change detection.Ph.D. - Doctoral Progra

3D modelling for surveying projects using unmanned arial vehicles (UAVs) and laser scanning

3D models that have been created from photogrammetry have some evident limitations. To create better, more complete 3D models, it is necessary to understand and reduce these limitations. The project aims to look at the effect of camera orientation and its effect on the overall accuracy of the project. Furthermore, it is proposed to reduce the inevitable gaps in the model by the use of terrestrial photogrammetry. The primary comparison of the model will be between the data captured from photogrammetry techniques and that of traditional style of surveying methods such as total station and terrestrial scanning. The research was conducted in late 2015 and was processed using the latest software versions as of mid-2016. The research is supported by UAS Pacific, the aim is to ultimately provide the industry with a better understanding of the data and aims to improve the overall quality of 3D modelling with the use of new exciting technologies and techniques that are available to the public today

On-the-fly olive tree counting using a UAS and cloud services

Unmanned aerial systems (UAS) are becoming a common tool for aerial sensing applications. Nevertheless, sensed data need further processing before becoming useful information. This processing requires large computing power and time before delivery. In this paper, we present a parallel architecture that includes an unmanned aerial vehicle (UAV), a small embedded computer on board, a communication link to the Internet, and a cloud service with the aim to provide useful real-time information directly to the end-users. The potential of parallelism as a solution in remote sensing has not been addressed for a distributed architecture that includes the UAV processors. The architecture is demonstrated for a specific problem: the counting of olive trees in a crop field where the trees are regularly spaced from each other. During the flight, the embedded computer is able to process individual images on board the UAV and provide the total count. The tree counting algorithm obtains an F1 score of 99.09% for a sequence of ten images with 332 olive trees. The detected trees are geolocated and can be visualized on the Internet seconds after the take-off of the flight, with no further processing required. This is a use case to demonstrate near real-time results obtained from UAS usage. Other more complex UAS applications, such as tree inventories, search and rescue, fire detection, or stock breeding, can potentially benefit from this architecture and obtain faster outcomes, accessible while the UAV is still on flightPeer ReviewedPostprint (published version

2008 Annual Report of the Graduate School of Engineering and Management, Air Force Institute of Technology

The Graduate School\u27s Annual Report highlights research focus areas, new academic programs, faculty accomplishments and news, and provides top-level sponsor-funded research data and information

UAV Command and Control, Navigation and Surveillance: A Review of Potential 5G and Satellite Systems

Drones, unmanned aerial vehicles (UAVs), or unmanned aerial systems (UAS) are expected to be an important component of 5G/beyond 5G (B5G) communications. This includes their use within cellular architectures (5G UAVs), in which they can facilitate both wireless broadcast and point-to-point transmissions, usually using small UAS (sUAS). Allowing UAS to operate within airspace along with commercial, cargo, and other piloted aircraft will likely require dedicated and protected aviation spectrum at least in the near term, while regulatory authorities adapt to their use. The command and control (C2), or control and non-payload communications (CNPC) link provides safety critical information for the control of the UAV both in terrestrial-based line of sight (LOS) conditions and in satellite communication links for so-called beyond LOS (BLOS) conditions. In this paper, we provide an overview of these CNPC links as they may be used in 5G and satellite systems by describing basic concepts and challenges. We review new entrant technologies that might be used for UAV C2 as well as for payload communication, such as millimeter wave (mmWave) systems, and also review navigation and surveillance challenges. A brief discussion of UAV-to-UAV communication and hardware issues are also provided.Comment: 10 pages, 5 figures, IEEE aerospace conferenc

New Perspectives for UAV-Based Modelling the Roman Gold Mining Infrastructure in NW Spain

This contribution discusses the potential of UAV-assisted (unmanned aerial vehicles) photogrammetry for the study and preservation of mining heritage sites using the example of Roman gold mining infrastructure in northwestern Spain. The study area represents the largest gold area in Roman times and comprises 7 mining elements of interest that characterize the most representative examples of such ancient works. UAV technology provides a non-invasive procedure valuable for the acquisition of digital information in remote, difficult to access areas or under the risk of destruction. The proposed approach is a cost-effective, robust and rapid method for image processing in remote areas were no traditional surveying technologies are available. It is based on a combination of data provided by aerial orthoimage and LiDAR (Light Detection and Ranging) to improve the accuracy of UAV derived data. The results provide high-resolution orthomosaic, DEMs and 3D textured models that aim for the documentation of ancient mining scenarios, providing high-resolution digital information that improves the identification, description and interpretation of mining elements such as the hydraulic infrastructure, the presence of open-cast mines which exemplifies the different exploitation methods, and settlements. However, beyond the scientific and technical information provided by the data, the 3D documentation of ancient mining scenarios is a powerful tool for an effective and wider public diffusion ensuring the visualization, preservation and awareness over the importance and conservation of world mining heritage sites