scholarly journals Identification and Analysis of Microscale Hydrologic Flood Impacts Using Unmanned Aerial Systems

2020 ◽  
Vol 12 (10) ◽  
pp. 1549 ◽  
Author(s):  
Jamie L. Dyer ◽  
Robert J. Moorhead ◽  
Lee Hathcock
Keyword(s):  
High Resolution ◽  
Situational Awareness ◽  
Natural Resistance ◽  
Unmanned Aerial Systems ◽  
Hydrologic Models ◽  
River Flooding ◽  
Flood Impacts ◽  
Aerial Systems ◽  
Imaging Sensor ◽  
Scale Surface

The need for accurate and spatially detailed hydrologic information is critical due to the microscale influences on the severity and distribution of flooding, and new and/or updated approaches in observations of river systems are required that are in line with the current push towards microscale numerical simulations. In response, the aim of this project is to define and illustrate the hydrologic response of river flooding relative to microscale surface properties by using an unmanned aerial system (UAS) with dedicated imaging, sensor, and communication packages for data collection. As part of a larger project focused on increasing situational awareness during flood events, a fixed-wing UAS was used to overfly areas near Greenwood, MS before and during a flood event in February 2019 to provide high-resolution visible and infrared imagery for analysis of hydrologic features. The imagery obtained from these missions provide direct examples of fine-scale surface features that can alter water level and discharge, such as built structures (i.e., levees and bridges), natural storage features (low-lying agricultural fields), and areas of natural resistance (inundated forests). This type of information is critical in defining where and how to incorporate high-resolution information into hydrologic models and also provides an invaluable dataset for eventual verification of hydrologic simulations through inundation mapping.

Unmanned aerial systems (UAS) in urban search and rescue-methodology, capacity development, and integration

2021 ◽  
Vol 19 (1) ◽  
pp. 33-38
Author(s):  
Ariel Braverman, BSc, RN, EMT-P
Keyword(s):  
Situational Awareness ◽  
Search And Rescue ◽  
Unmanned Aerial Systems ◽  
Urban Search And Rescue ◽  
Emergency Operations ◽  
Ventilation Ducts ◽  
Speed Up ◽  
Modernization Process ◽  
Roadless Areas ◽  
Aerial Systems

This paper’s purpose is to establish a methodological basis for using unmanned aerial vehicles (UAV) in urban search and rescue (USAR). Modern USAR operations involve the location, rescue (extrication), and initial medical stabilization of individuals trapped in confined spaces or places with complicated access, eg, high structures. As a part of the ongoing modernization process, this paper explores possible options for UAV utilization in USAR operations. Today, UAV are already taking part in support emergency operations all over the world, and possible forms of operation for UAV in USAR environment can be in two primary modes: on-site and logistic chain. The on-site mode includes various capabilities of multilayer UAV array, mostly based on enhanced visual capabilities to create situational awareness and to speed-up search and rescue (SAR) process including using nanodrones for entering into confined places, ventilation ducts, and underground sewer channels can give to rescue teams’ opportunities to have eyes within ruins even before initial clearing process. Cargo drones will be able to bring equipment directly to high floors or roadless areas in comparison to wheeled transportation. The advantages of cargo drones operation are the ability of autonomous flight based on GPS or homing beacon and ability to provide logistics supports without involving additional personnel and vehicles and with no dependence on road conditions.

Making the invisible visible: using UAS-based high-resolution color-infrared imagery to identify buried medieval monastery walls

2015 ◽  
Vol 3 (2) ◽  
pp. 58-67 ◽  
Author(s):  
Jan Rudolf Karl Lehmann ◽  
Keturah Zoe Smithson ◽  
Torsten Prinz
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
High Resolution ◽  
Vegetation Index ◽  
Archaeological Site ◽  
Unmanned Aerial Systems ◽  
Surrounding Water ◽  
Normalised Difference Vegetation Index ◽  
Remote Sensing Techniques ◽  
Aerial Systems

Remote sensing techniques have become an increasingly important tool for surveying archaeological sites. However, budgeting issues in archaeological research often limit the application of satellite or airborne imagery. Unmanned aerial systems (UAS) provide a flexible, quick, and more economical alternative to commonly used remote sensing techniques. In this study, the buried features of the archaeological site of the Kleinburlo monastery, near Münster, Germany, were identified using high-resolution color–infrared (CIR) images collected from a UAS platform. Based on these CIR images, a modified normalised difference vegetation index (NDVIblue) was calculated, showing reflectance spectra of vegetation anomalies caused by water stress. In the presented study, the vegetation growing on top of the buried walls was better nourished than the surrounding plants because very wet conditions over the days previous to data collection caused higher levels of water stress in the surrounding water-drenched land. This difference in water stress was a good indicator for detecting archaeological remains.

scholarly journals Deep Learning Based Wildfire Event Object Detection from 4K Aerial Images Acquired by UAS

AI ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 166-179 ◽  
Author(s):  
Ziyang Tang ◽  
Xiang Liu ◽  
Hanlin Chen ◽  
Joseph Hupy ◽  
Baijian Yang
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Object Detection ◽  
Aerial Images ◽  
Unmanned Aerial Systems ◽  
Computer Assisted ◽  
Visual Interpretation ◽  
Two Phase ◽  
Aerial Systems ◽  
Spot Fires

Unmanned Aerial Systems, hereafter referred to as UAS, are of great use in hazard events such as wildfire due to their ability to provide high-resolution video imagery over areas deemed too dangerous for manned aircraft and ground crews. This aerial perspective allows for identification of ground-based hazards such as spot fires and fire lines, and to communicate this information with fire fighting crews. Current technology relies on visual interpretation of UAS imagery, with little to no computer-assisted automatic detection. With the help of big labeled data and the significant increase of computing power, deep learning has seen great successes on object detection with fixed patterns, such as people and vehicles. However, little has been done for objects, such as spot fires, with amorphous and irregular shapes. Additional challenges arise when data are collected via UAS as high-resolution aerial images or videos; an ample solution must provide reasonable accuracy with low delays. In this paper, we examined 4K ( 3840 × 2160 ) videos collected by UAS from a controlled burn and created a set of labeled video sets to be shared for public use. We introduce a coarse-to-fine framework to auto-detect wildfires that are sparse, small, and irregularly-shaped. The coarse detector adaptively selects the sub-regions that are likely to contain the objects of interest while the fine detector passes only the details of the sub-regions, rather than the entire 4K region, for further scrutiny. The proposed two-phase learning therefore greatly reduced time overhead and is capable of maintaining high accuracy. Compared against the real-time one-stage object backbone of YoloV3, the proposed methods improved the mean average precision(mAP) from 0 . 29 to 0 . 67 , with an average inference speed of 7.44 frames per second. Limitations and future work are discussed with regard to the design and the experiment results.

Soil moisture estimation from high-resolution UASs imagery based on machine learning approaches for land cover classification

2021 ◽  
Author(s):  
Teresa Pizzolla ◽  
Silvano Fortunato Dal Sasso ◽  
Ruodan Zhuang ◽  
Alonso Pizarro ◽  
Salvatore Manfreda
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Thermal Inertia ◽  
Bare Soil ◽  
Unmanned Aerial Systems ◽  
Learning Approaches ◽  
Land Use Classification ◽  
Aerial Systems

<p>Soil moisture (SM) is an essential variable in the earth system as it influences water, energy and, carbon fluxes between the land surface and the atmosphere. The SM spatio-temporal variability requires detailed analyses, high-definition optics and fast computing approaches for near real-time SM estimation at different spatial scales. Remote Sensing-based Unmanned Aerial Systems (UASs) represents the actual solution providing low-cost approaches to meet the requirements of spatial, spectral and temporal resolutions [1; 3; 4]. In this context, a proper land use classification is crucial in order to discriminate the behaviors of vegetation and bare soil in such high-resolution imagery. Therefore, high-resolution UASs-based imagery requires a specific images classification approach also considering the illumination conditions. In this work, the land use classification was carried out using a methodology based on a combined machine learning approaches: k-means clustering algorithm for removing shadow pixels from UASs images and, binary classifier for vegetation filtering. This approach led to identifying the bare soil on which SM estimation was computed using the Apparent Thermal Inertia (ATI) method [2]. The estimated SM values were compared with field measurements obtaining a good correlation (R<sup>2</sup> = 0.80). The accuracy of the results shows good reliability of the procedure and allows extending the use of UASs also in unclassified areas and ungauged basins, where the monitoring of the SM is very complex.</p><p><strong>References</strong></p><p>[1] Manfreda, S., McCabe, M.F., Miller, P.E., Lucas, R., Pajuelo Madrigal, V., Mallinis, G., Ben Dor, E., Helman, D., Estes, L., Ciraolo, G., et al. On the Use of Unmanned Aerial Systems for Environmental Monitoring, Remote Sensing, 2018, 10, 641.</p><p>[2] Minacapilli, M., Cammalleri, C., Ciraolo, G., D’Asaro, F., Iovino, M., and Maltese, A. Thermal Inertia Modeling for Soil Surface Water Content Estimation: A Laboratory Experiment. Soil. Sci. Soc. Amer. J. 2012, vol.76, n.1, pp. 92–100</p><p>[3] Paruta, A., P. Nasta, G. Ciraolo, F. Capodici, S. Manfreda, N. Romano, E. Bendor, Y. Zeng, A. Maltese, S. F. Dal Sasso and R. Zhuang, A geostatistical approach to map near-surface soil moisture through hyper-spatial resolution thermal inertia, IEEE Transactions on Geoscience and Remote Sensing, 2020.</p><p>[4] Petropoulos, G.P., A. Maltese, T. N. Carlson, G. Provenzano, A. Pavlides, G. Ciraolo, D. Hristopulos, F. Capodici, C. Chalkias, G. Dardanelli, S. Manfreda, Exploring the use of UAVs with the simplified “triangle” technique for Soil Water Content and Evaporative Fraction retrievals in a Mediterranean setting, International Journal of Remote Sensing, 2020.</p>

Controlling Multiple Unmanned Aircraft from a Manned Helicopter: The Need for Advanced Autonomy and Refined Pilot-Vehicle Interface

2017 ◽  
Vol 61 (1) ◽  
pp. 78-82 ◽  
Author(s):  
Grant S. Taylor ◽  
Thomas J. Alicia ◽  
Terry Turpin ◽  
Amit Surana
Keyword(s):  
Supervisory Control ◽  
Situational Awareness ◽  
Unmanned Aircraft ◽  
Experimental Results ◽  
Unmanned Aerial Systems ◽  
Current Ratio ◽  
One To One ◽  
Aerial Systems

Manned-Unmanned Teaming (MUM-T) is a military concept that employs unmanned aerial systems (UASs) in support of traditional manned aircraft. The current ratio of manned to unmanned aircraft in MUM-T operations is one to one with a goal to expand to multiple UASs to further enhance the capability, but this imposes significant challenges on the operator. To address these challenges, this research implemented automated UAS behaviors combined with a pilot-vehicle interface tailored to provide supervisory control over multiple UASs. Results demonstrated that this combination of technologies allows a single crewmember to effectively manage up to three UASs while executing complex MUM-T tactical missions with manageable workload, improved situational awareness, and improved mission performance. Experimental results also identified areas where the current implementations can be further refined.

scholarly journals ’Looking Through a Soda Straw’: Mediated Vision in Remote Warfare

Politik ◽  
2017 ◽  
Vol 20 (1) ◽  
Author(s):  
Moritz Queisner
Keyword(s):  
Real Time ◽  
Situational Awareness ◽  
Unmanned Aerial Systems ◽  
Military Operations ◽  
Sensory Data ◽  
Action And Perception ◽  
Image Production ◽  
Functional Shift ◽  
Human Actors ◽  
Aerial Systems

Image-guided military operations embed soldiers into a complex system of image production, transmission, and perception. These soldiers separate their bodies from the battlefield, but they also mediate between them. In particular, remote controlled operations of so-called unmanned aerial systems (UAS) require the synchronization between human actors and technical sensors in real-time, such as the knowledge of a situation. This situational awareness relies almost exclusively on the visualization of sensory data. This human-machine entanglement corresponds to a new operative modality of images which differs from previous forms of real-time imaging such as live broadcasting, as it is based on a feedback-loop that turns the observer into an actor. Images are not simply analyzed and interpreted but become agents in a socio- technological assemblage. The paper will draw upon this functional shift of images from a medium of visualization towards a medium that guides operative processes. Based on the analysis of vision, architecture, and navigation in remote warfare, it will discuss how real-time video technology and the mobilization of sensor and transmission technology produce a type of intervention, in which action and perception is increasingly organized and determined by machines. 

Coastal Management Using UAS and High-Resolution Satellite Images for Touristic Areas

2019 ◽  
Vol 10 (1) ◽  
pp. 54-72 ◽  
Author(s):  
Apostolos Papakonstantinou ◽  
Michaela Doukari ◽  
Panagiotis Stamatis ◽  
Konstantinos Topouzelis
Keyword(s):  
High Resolution ◽  
Case Studies ◽  
Satellite Images ◽  
Unmanned Aerial Systems ◽  
Shoreline Evolution ◽  
Coastline Change ◽  
Coastline Evolution ◽  
High Resolution Satellite Images ◽  
Two Factors ◽  
Aerial Systems

Coastline change and human activities in shoreline zones are two factors indicating the vulnerability and the quality of a coastal environment. In this article, coastline evolution and spatiotemporal differences on coastal touristic infrastructure are presented as two case studies. Both case studies have increasing interest among scientists monitoring sensitive coastal areas, and for stakeholders evolved in the tourist industry. The study is twofold: monitors the shoreline evolution and examines how the shoreline behavior affects the seasonal anthropogenic touristic infrastructure. Shoreline detection methodology integrates unmanned aerial systems (UAS) or high-resolution satellite images for data acquisition, and geographic object-based image analysis (GEOBIA) for the shoreline recognition and the infrastructure change detection. The methodology used produced robust results in the aspect of mapping and detecting coastline changes, coastal erosion and the human pressure due to specific activities.

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