10,372 research outputs found
The function of remote sensing in support of environmental policy
Limited awareness of environmental remote sensingâs potential ability to support environmental policy development constrains the technologyâs utilization. This paper reviews the potential of earth observation from the perspective of environmental policy. A literature review of âremote sensing and policyâ revealed that while the number of publications in this field increased almost twice as rapidly as that of remote sensing literature as a whole (15.3 versus 8.8% yrâ1), there is apparently little academic interest in the societal contribution of environmental remote sensing. This is because none of the more than 300 peer reviewed papers described actual policy support. This paper describes and discusses the potential, actual support, and limitations of earth observation with respect to supporting the various stages of environmental policy development. Examples are given of the use of remote sensing in problem identification and policy formulation, policy implementation, and policy control and evaluation. While initially, remote sensing contributed primarily to the identification of environmental problems and policy implementation, more recently, interest expanded to applications in policy control and evaluation. The paper concludes that the potential of earth observation to control and evaluate, and thus assess the efficiency and effectiveness of policy, offers the possibility of strengthening governance
Airborne Visible/Infrared Imaging spectrometer AVIS: Design, characterization and calibration
The Airborne Visible/Infrared imaging Spectrometer AVIS is a hyperspectral imager designed for environmental monitoring purposes. The sensor, which was constructed entirely from commercially available components, has been successfully deployed during several experiments between 1999 and 2007. We describe the instrument design and present the results of laboratory characterization and calibration of the system's second generation, AVIS-2, which is currently being operated. The processing of the data is described and examples of remote sensing reflectance data are presented
European Arctic Initiatives Compendium
Julkaistu versi
Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy
Astrophysics and cosmology are rich with data. The advent of wide-area
digital cameras on large aperture telescopes has led to ever more ambitious
surveys of the sky. Data volumes of entire surveys a decade ago can now be
acquired in a single night and real-time analysis is often desired. Thus,
modern astronomy requires big data know-how, in particular it demands highly
efficient machine learning and image analysis algorithms. But scalability is
not the only challenge: Astronomy applications touch several current machine
learning research questions, such as learning from biased data and dealing with
label and measurement noise. We argue that this makes astronomy a great domain
for computer science research, as it pushes the boundaries of data analysis. In
the following, we will present this exciting application area for data
scientists. We will focus on exemplary results, discuss main challenges, and
highlight some recent methodological advancements in machine learning and image
analysis triggered by astronomical applications
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
Remote sensing (RS) image retrieval is of great significant for geological
information mining. Over the past two decades, a large amount of research on
this task has been carried out, which mainly focuses on the following three
core issues: feature extraction, similarity metric and relevance feedback. Due
to the complexity and multiformity of ground objects in high-resolution remote
sensing (HRRS) images, there is still room for improvement in the current
retrieval approaches. In this paper, we analyze the three core issues of RS
image retrieval and provide a comprehensive review on existing methods.
Furthermore, for the goal to advance the state-of-the-art in HRRS image
retrieval, we focus on the feature extraction issue and delve how to use
powerful deep representations to address this task. We conduct systematic
investigation on evaluating correlative factors that may affect the performance
of deep features. By optimizing each factor, we acquire remarkable retrieval
results on publicly available HRRS datasets. Finally, we explain the
experimental phenomenon in detail and draw conclusions according to our
analysis. Our work can serve as a guiding role for the research of
content-based RS image retrieval
Proceedings of the 2011 New York Workshop on Computer, Earth and Space Science
The purpose of the New York Workshop on Computer, Earth and Space Sciences is
to bring together the New York area's finest Astronomers, Statisticians,
Computer Scientists, Space and Earth Scientists to explore potential synergies
between their respective fields. The 2011 edition (CESS2011) was a great
success, and we would like to thank all of the presenters and participants for
attending. This year was also special as it included authors from the upcoming
book titled "Advances in Machine Learning and Data Mining for Astronomy". Over
two days, the latest advanced techniques used to analyze the vast amounts of
information now available for the understanding of our universe and our planet
were presented. These proceedings attempt to provide a small window into what
the current state of research is in this vast interdisciplinary field and we'd
like to thank the speakers who spent the time to contribute to this volume.Comment: Author lists modified. 82 pages. Workshop Proceedings from CESS 2011
in New York City, Goddard Institute for Space Studie
Artificial Intelligence Data Science Methodology for Earth Observation
This chapter describes a Copernicus Access Platform Intermediate Layers Small-Scale Demonstrator, which is a general platform for the handling, analysis, and interpretation of Earth observation satellite images, mainly exploiting big data of the European Copernicus Programme by artificial intelligence (AI) methods. From 2020, the platform will be applied at a regional and national level to various use cases such as urban expansion, forest health, and natural disasters. Its workflows allow the selection of satellite images from data archives, the extraction of useful information from the metadata, the generation of descriptors for each individual image, the ingestion of image and descriptor data into a common database, the assignment of semantic content labels to image patches, and the possibility to search and to retrieve similar content-related image patches. The main two components, namely, data mining and data fusion, are detailed and validated. The most important contributions of this chapter are the integration of these two components with a Copernicus platform on top of the European DIAS system, for the purpose of large-scale Earth observation image annotation, and the measurement of the clustering and classification performances of various Copernicus Sentinel and third-party mission data. The average classification accuracy is ranging from 80 to 95% depending on the type of images
NASA geology program bibliography
A bibliography of scientific papers, articles, and books based on research supported by the NASA Geology Program is given. The citations cover the period 1980 to 1990. An author index is included
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