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Author

G. Lewis

Bio: G. Lewis is an academic researcher from Assiniboine Community College. The author has contributed to research in topics: Digital imaging & Flight training. The author has an hindex of 1, co-authored 1 publications receiving 26 citations.

Papers
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Book ChapterDOI
01 Jan 2007
TL;DR: In this article, the authors evaluated the utilization of a low-cost UAV digital imaging platform developed in Manitoba, Canada for emergency response situations, which allows for the timely acquisition of high resolution imagery during emergency situations by personnel with relatively limited UAV flight training.
Abstract: This research project evaluates the utilization of a low-cost Unmanned Aerial Vehicle (UAV) digital imaging platform developed in Manitoba, Canada for emergency response situations. Such a platform allows for the timely acquisition of high resolution imagery during emergency situations by personnel with relatively limited UAV flight training.

28 citations


Cited by
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Journal ArticleDOI
TL;DR: To provide a reliable end product to farmers, advances in platform design, production, standardization of image georeferencing and mosaicing, and information extraction workflow are required and the farmer should involve in the process of field design, image acquisition, image interpretation and analysis.
Abstract: Precision agriculture (PA) is the application of geospatial techniques and sensors (e.g., geographic information systems, remote sensing, GPS) to identify variations in the field and to deal with them using alternative strategies. In particular, high-resolution satellite imagery is now more commonly used to study these variations for crop and soil conditions. However, the availability and the often prohibitive costs of such imagery would suggest an alternative product for this particular application in PA. Specifically, images taken by low altitude remote sensing platforms, or small unmanned aerial systems (UAS), are shown to be a potential alternative given their low cost of operation in environmental monitoring, high spatial and temporal resolution, and their high flexibility in image acquisition programming. Not surprisingly, there have been several recent studies in the application of UAS imagery for PA. The results of these studies would indicate that, to provide a reliable end product to farmers, advances in platform design, production, standardization of image georeferencing and mosaicing, and information extraction workflow are required. Moreover, it is suggested that such endeavors should involve the farmer, particularly in the process of field design, image acquisition, image interpretation and analysis.

1,353 citations

Journal ArticleDOI
TL;DR: Although potential applications abound, small-scale unmanned aerial vehicles have not yet been widely used for environmental remote sensing as discussed by the authors. Several challenges remain to be overcome until widesprea...
Abstract: Although potential applications abound, small-scale unmanned aerial vehicles have not yet been widely used for environmental remote sensing. Several challenges remain to be overcome until widesprea...

170 citations

Journal ArticleDOI
TL;DR: This system was significantly improved with respect to its searching/planning strategy and vision‐based evaluation in different environments based on the lessons learned from actual missions after the earthquake and has proved to be applicable and time saving.
Abstract: Rapid search and rescue responses after earthquakes or in postseismic evaluation tend to be extremely difficult. To solve this problem, we summarized the requirements of search and rescue rotary-wing unmanned aerial vehicle SR-RUAV systems according to related works, manual earthquake search and rescue, and our knowledge to guide our research works. Based on these requirements, a series of research and technical works have been conducted to present an efficient SR-RUAV system. To help rescue teams locate interested areas quickly, a collapsed-building detecting approach that integrates low-altitude statistical image processing methods was proposed, which can increase survival rates by detecting collapsed buildings in a timely manner. The entire SR-RUAV system was illustrated by simulated earthquake response experiments in the China National Training Base for Search and Rescue CNTBSR from 2008 to 2010. On April 20, 2013, Lushan China experienced a disastrous earthquake magnitude 7.0. Because of the distribution of buildings in the rural areas, it was impossible to implement a rapid search and postseismic evaluation via ground searching. We provided our SR-RUAV to the Chinese International Search and Rescue Team CISAR and accurately detected collapsed buildings for ground rescue guidance at low altitudes. This system was significantly improved with respect to its searching/planning strategy and vision-based evaluation in different environments based on the lessons learned from actual missions after the earthquake. The SR-RUAV has proved to be applicable and time saving. The physical structure, searching and planning strategy, image-processing algorithm, and improvements in real missions are described in detail in this study.

111 citations

Journal ArticleDOI
TL;DR: This study provides a comprehensive review of how UAV-based damage mapping has evolved from providing simple descriptive overviews of a disaster science, to more sophisticated texture and segmentation-based approaches, and finally to studies using advanced deep learning approaches to provide comprehensive damage descriptions.
Abstract: Structural disaster damage detection and characterization is one of the oldest remote sensing challenges, and the utility of virtually every type of active and passive sensor deployed on various air- and spaceborne platforms has been assessed. The proliferation and growing sophistication of unmanned aerial vehicles (UAVs) in recent years has opened up many new opportunities for damage mapping, due to the high spatial resolution, the resulting stereo images and derivatives, and the flexibility of the platform. This study provides a comprehensive review of how UAV-based damage mapping has evolved from providing simple descriptive overviews of a disaster science, to more sophisticated texture and segmentation-based approaches, and finally to studies using advanced deep learning approaches, as well as multi-temporal and multi-perspective imagery to provide comprehensive damage descriptions. The paper further reviews studies on the utility of the developed mapping strategies and image processing pipelines for first responders, focusing especially on outcomes of two recent European research projects, RECONASS (Reconstruction and Recovery Planning: Rapid and Continuously Updated Construction Damage, and Related Needs Assessment) and INACHUS (Technological and Methodological Solutions for Integrated Wide Area Situation Awareness and Survivor Localization to Support Search and Rescue Teams). Finally, recent and emerging developments are reviewed, such as recent improvements in machine learning, increasing mapping autonomy, damage mapping in interior, GPS-denied environments, the utility of UAVs for infrastructure mapping and maintenance, as well as the emergence of UAVs with robotic abilities.

96 citations

Journal ArticleDOI
TL;DR: The advantages of small-scale remotely piloted vehicles (RPVs) include pilot safety, fast data turnaround time, low capital and per-flight costs, ease in flight planning, quick response for targets of opportunity, and the potential to gather ultrahigh resolution pictures in situations where in situ data gathering is impossible as mentioned in this paper.
Abstract: Small-scale remotely piloted vehicles (RPVs) have been experimentally used in environmental fieldwork. They have found employment in range management, forestry, wildlife studies, crop monitoring, marine investigations, and aerobiological sampling. The advantages of small-scale RPVs include pilot safety, fast data turnaround time, low capital and per-flight costs, ease in flight planning, quick response for targets of opportunity, and the potential to gather ultrahigh resolution pictures in situations where in situ data gathering is impossible. Significant limitations of small-scale RPVs include their low stability as photographic platforms; short flight times, airframe fragility, the paucity of sensor packages available, and the difficulties involved integrating pilot-assist flight navigation systems. Further development is required before small-scale RPVs will be accepted as mainstream tools for environmental monitoring. Besides discovering and validating environmental applications of the technology, this work includes the design of inexpensive lightweight sensor packages for environmental tasks and the improvement of image processing algorithms to interpret, rectify, and manage RPV imagery.

71 citations