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1. DESIGN RESEARCH BASED DEVELOPMENT OF CAMOUFLAGE LANDSCAPE FEATURES TO PREVENT CRIMINAL UAV ACTIVITY

   Hewitt, Robert and ( January 2019 , Landscape research record)

   The popularity of small consumer drones (UAVs) has prompted increased use of these vehicles in and around public outdoor spaces, with expected commercial drone numbers to reach nearly 7 million by 2030 (FAA). Research in landscape architecture related to UAV (drone) use in public space to date, has just begun to address conceptual approaches to landscape assessment, representation, and park user behavior (Kullmann, Park). While research related to the development of countermeasures for security purposes is more extensive, no research to date addresses the development of landscape countermeasures for the use of UAVs in criminal activities. The paper presents design-based research (DBR) methodology and findings funded by a multiyear, multidisciplinary NSF grant to develop landscape architectural interventions that discourage the use of UAVs for criminal purposes at correctional facilities. Consistent with design based research (DBR) models (Brown), this project is complex, incorporating the development of a) landscape assessments for potential UAV launch and landing sites around prisons; b) the creation of UAV tracking and monitoring systems, and c) the development of model countermeasures. The paper describes design and placement of embedded landscape features utilizing landscape camouflage principles for UAV detection systems in forested upstate North Carolina. Modelled camouflage mimicked landscape features and were fabricated in two stages: 1) landscape superstructure, and 2) landscape camouflage. The embedded landscape features incorporated a launch warning system capable of alerting prison officials of drone launch locations, identifying future drone operators, and predicting drone flight paths. 

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2. DESIGN RESEARCH BASED DEVELOPMENT OF CAMOUFLAGE LANDSCAPE FEATURES TO PREVENT CRIMINAL UAV ACTIVITY

   Hewitt, Robert ; Nassar, Hala ; Cummings, Mary ( January 2019 , Landscape research record)

   The popularity of small consumer drones (UAVs) has prompted increased use of these vehicles in and around public outdoor spaces, with expected commercial drone numbers to reach nearly 7 million by 2030 (FAA). Research in landscape architecture related to UAV (drone) use in public space to date, has just begun to address conceptual approaches to landscape assessment, representation, and park user behavior (Kullmann, Park). While research related to the development of countermeasures for security purposes is more extensive, no research to date addresses the development of landscape countermeasures for the use of UAVs in criminal activities. The paper presents design-based research (DBR) methodology and findings funded by a multiyear, multidisciplinary NSF grant to develop landscape architectural interventions that discourage the use of UAVs for criminal purposes at correctional facilities. Consistent with design based research (DBR) models (Brown), this project is complex, incorporating the development of a) landscape assessments for potential UAV launch and landing sites around prisons; b) the creation of UAV tracking and monitoring systems, and c) the development of model countermeasures. The paper describes design and placement of embedded landscape features utilizing landscape camouflage principles for UAV detection systems in forested upstate North Carolina. Modelled camouflage mimicked landscape features and were fabricated in two stages: 1) landscape superstructure, and 2) landscape camouflage. The embedded landscape features incorporated a launch warning system capable of alerting prison officials of drone launch locations, identifying future drone operators, and predicting drone flight paths. Keywords: 

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3. Mapping spatially explicit vegetation gaps in Florida rosemary scrub using unmanned aerial vehicles

   https://doi.org/10.1002/ecs2.3470  

   Charton, Katherine T. ; Sclater, Vivienne L. ; Menges, Eric S. ( April 2021 , Ecosphere)

   Advances in remote sensing technologies offer new means to monitor habitats of importance on large scales. Florida rosemary scrub is one such threatened habitat, found in patches across the landscape in relatively elevated areas, and is often characterized by shrub‐less areas (gaps) among the dominant shrubs, which provide favorable microhabitats for many endemic and endangered plants and animals. However, gaps are difficult and time‐consuming to characterize, especially across large areas, using traditional ground‐based field methods. We developed and tested a method for rapidly classifying gaps using an unmanned aerial vehicle (UAV or drone). Aerial data were collected by a UAV‐mounted camera in April 2018, and stratified, random ground surveys to verify UAV data were conducted March through April 2018 at Archbold Biological Station in south‐central Florida, USA. We used mosaicked and georeferenced digital surface and terrain models to calculate vegetation height across 33 rosemary scrub sites (~230,000 m²at 0.064 m²pixel resolution). Gaps were defined as >1 m²areas where vegetation height was <10 cm. We found that gap areas from UAV models and field surveys were significantly correlated across varying gap sizes, times‐since‐fire, and relative elevations. We also observed a significant decrease in mean gap area and percent gap space with increasing time‐since‐fire, a pattern consistent with smaller‐scale, ground‐based sampling, and a marginally significant increase in gap area with relative elevation. This remote sensing method lends itself to better exploration of how gap areas, their spatiotemporal patterns, and associated fire history, elevation, soil, and other geographic data affect structural vegetation dynamics across the landscape. This study illustrates the success of UAV modeling of gap space in Florida rosemary scrub, a result of regional consequence for the southeastern United States, but more broadly, it encourages the use of UAV technology as a tool to enhance traditional field‐based methods in systems globally. As habitat fragmentation and loss become increasingly problematic for the conservation of threatened habitats, understanding these complex spatial dynamics is crucial to the conservation and management of vegetation communities and their biodiversity.

    

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4. Autonomous UAV with Learned Trajectory Generation and Control

   https://doi.org/10.1109/SiPS47522.2019.9020508  

   Li, Yilan ; Li, Mingyang ; Sanyal, Amit ; Wang, Yanzhi ; Qiu, Qinru ( October 2019 , IEEE Workshop on Signal Processing Systems (SIPS))

   Unmanned aerial vehicle (UAV) technology is a rapidly growing field with tremendous opportunities for research and applications. To achieve true autonomy for UAVs in the absence of remote control, external navigation aids like global navigation satellite systems and radar systems, a minimum energy trajectory planning that considers obstacle avoidance and stability control will be the key. Although this can be formulated as a constrained optimization problem, due to the complicated non-linear relationships between UAV trajectory and thrust control, it is almost impossible to be solved analytically. While deep reinforcement learning is known for its ability to provide model free optimization for complex system through learning, its state space, actions and reward functions must be designed carefully. This paper presents our vision of different layers of autonomy in a UAV system, and our effort in generating and tracking the trajectory both using deep reinforcement learning (DRL). The experimental results show that compared to conventional approaches, the learned trajectory will need 20% less control thrust and 18% less time to reach the target. Furthermore, using the control policy learning by DRL, the UAV will achieve 58.14% less position error and 21.77% less system power. 

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5. Strategies for effective unmanned aerial vehicle use in geological field studies based on cognitive science principles

   https://doi.org/10.1130/GES02440.1  

   Bateman, Kathryn M. ; Williams, Randolph T. ; Shipley, Thomas F. ; Tikoff, Basil ; Pavlis, Terry ; Wilson, Cristina G. ; Cooke, Michele L. ; Fagereng, Ake ( November 2022 , Geosphere)

   Abstract Field geologists are increasingly using unmanned aerial vehicles (UAVs or drones), although their use involves significant cognitive challenges for which geologists are not well trained. On the basis of surveying the user community and documenting experts’ use in the field, we identified five major problems, most of which are aligned with well-documented limits on cognitive performance. First, the images being sent from the UAV portray the landscape from multiple different view directions. Second, even with a constant view direction, the ability to move the UAV or zoom the camera lens results in rapid changes in visual scale. Third, the images from the UAVs are displayed too quickly for users, even experts, to assimilate efficiently. Fourth, it is relatively easy to get lost when flying, particularly if the user is unfamiliar with the area or with UAV use. Fifth, physical limitations on flight time are a source of stress, which renders the operator less effective. Many of the strategies currently employed by field geologists, such as postprocessing and photogrammetry, can reduce these problems. We summarize the cognitive science basis for these issues and provide some new strategies that are designed to overcome these limitations and promote more effective UAV use in the field. The goal is to make UAV-based geological interpretations in the field possible by recognizing and reducing cognitive load. 

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