2,113 research outputs found

    A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

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    In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

    Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

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    The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

    United States Air Force Applications of Unmanned Aerial Systems: Modernizing Airfield Damage Assessment

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    Modernizing airfield damage assessment has long been a priority mission at the Air Force Civil Engineer Center (AFCEC). Previously, AFCEC has made advances to expedite unexploded ordnance (UXO) neutralization and pavement repair. Missing from these initiatives is the initial assessment component. This thesis expands the idea of using Small Unmanned Aerial Systems (SUAS), applies it to the Air Force mission, and provides SUAS vehicle configuration and sensor recommendations. In this study, 25 civil engineer officers reviewed airfield imagery gathered using two small air vehicles. For the first review, participants attempted to identify UXOs and foreign object debris (FOD) in a computer interface that leverages images collected by a fixed-wing air vehicle. The second review uses a two-dimensional map created using a hex-rotor. The results of both systems were then compared to the status quo. Resulting statistics indicate that, irrespective of image resolution, additional analysis time does not result in greater object detection or correct identification. Overall, this thesis concludes that SUAS use for afield damage assessment shows promise. Moreover, they can provide the Air Force improved precision for locating UXOs and FOD, as well as estimate dimensions of damage. Dedicating resources to developing this technology will also assist with improving object detection and manpower efficiency. Further research is required for optimal image characterization requisite for reducing and/or eliminating the occurrence of false negative events

    Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

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    This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

    Geo-Information Harvesting from Social Media Data

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    As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysis-ready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

    Remote Sensing for Land Administration

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    How Much Shallow Coral Habitat Is There on the Great Barrier Reef?

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    Australia’s Great Barrier Reef (GBR) is a globally unique and precious national resource; however, the geomorphic and benthic composition and the extent of coral habitat per reef are greatly understudied. However, this is critical to understand the spatial extent of disturbance impacts and recovery potential. This study characterizes and quantifies coral habitat based on depth, geomorphic and benthic composition maps of more than 2164 shallow offshore GBR reefs. The mapping approach combined a Sentinel-2 satellite surface reflectance image mosaic and derived depth, wave climate, reef slope and field data in a random-forest machine learning and object-based protocol. Area calculations, for the first time, incorporated the 3D characteristic of the reef surface above 20 m. Geomorphic zonation maps (0–20 m) provided a reef extent estimate of 28,261 km2 (a 31% increase to current estimates), while benthic composition maps (0–10 m) estimated that ~10,600 km2 of reef area (~57% of shallow offshore reef area) was covered by hard substrate suitable for coral growth, the first estimate of potential coral habitat based on substrate availability. Our high-resolution maps provide valuable information for future monitoring and ecological modeling studies and constitute key tools for supporting the management, conservation and restoration efforts of the GBR

    Coral Colony-Scale Rugosity Metrics and Applications for Assessing Temporal Trends in the Structural Complexity of Coral Reefs.

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    Globally, coral reefs are experiencing reductions in structural complexity, primarily due to a loss of key reef building taxa. Monitoring these changes is difficult due to the time-consuming nature of in-situ measurements and lack of data concerning coral genus-specific contributions to reef structure. This research aimed to develop a new technique that uses coral colony level data to quantify reef rugosity (a 3-dimensional measure of reef structure) from three sources of coral survey data: 2D video imagery, line intercept data and UAV imagery. A database of coral colony rugosity data, comparing coral colony planar and contour length for 40 coral genera, 14 morphotypes and 9 abiotic reef substrates, was created using measurements from the Great Barrier Reef and Natural History Museum. Mean genus rugosity was identified as a key trait related to coral life history strategy. Linear regression analyses (y = mx) revealed statistically significant (p < 0.05) relationships between coral colony size and rugosity for every coral genus, morphotype and substrate. The gradient governing these relationships was unique for each coral taxa, ranging from mean = 1.23, for (encrusting) Acanthastrea, to m = 3.84, for (vase-shape) Merulina. These gradients were used as conversion factors to calculate reef rugosity from linear distances measured in video transects of both artificial reefs, used as a control test, and in-situ natural coral reefs, using Kinovea software. This calculated, ‘virtual’ rugosity had a strong, positive relationship with in-situ microscale rugosity (r2 = 0.96) measured from the control transects, but not with that measured at the meso-scale in natural, highly heterogeneous reef environments (r2 < 0.2). This showed that the technique can provide accurate rugosity information when considered at the coral colony level. The conversion factors were also applied to historic line intercept data from the Seychelles, where temporal changes in calculated rugosity were consistent with changes in coral cover between 2008 and 2017. Finally, on application to 2,283 corals digitised from UAV imagery of the Maldives, the conversion factors enabled calculation of rugosity for three 100 m2 reef areas and prediction of how this rugosity will decrease during two future scenarios of coral reef degradation and community change. The study highlights that the application of genera-specific coral rugosity data to both new and existing coral reef survey datasets could be a valuable tool for monitoring reef structural complexity over large spatial scales

    Applications of Photogrammetry for Environmental Research

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    ISPRS International Journal of Geo-Information: special issue entitled "Applications of Photogrammetry for Environmental Research
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