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Open Science for Earth Remote Sensing: latest developments in software and data

Significant changes have taken place over the past few years in remote sensing technology. Quantity, quality and diversity of sensors have increased exponentially and so have related data. Open source software packages capable of processing digital imagery have improved in terms of number of available programs, implemented algorithms, and stability. Internet speed increases by 50% every year (Nielsen’s Law), and this fact, along with a growing awareness of the importance of collaboration and sharing through open access to data, has boosted the public availability of remote sensing datasets.

The trends outlined above indicate that the remote sensing science community has easier access to data which are growing exponentially in terms of volume, velocity and variety. Every part of Earth’s surface has been - and will be - covered frequently by numerous active and passive imaging sensors, which differ in resolution (spatial, spectral and radiometric), in revisit time and in the sensing mode (passive, active). The above characteristics can lead to consider a “big data” approach to image analysis in some cases.

For decades, the open source community has provided tremendous contributions to remote sensing in terms of tools and solutions which has led to support and inspiration to research, development and end-users. The evolution in open access of data and tools and the corresponding advantages in science for Earth observation has motivated this special issue, whose objective is to report on past, current and future scenarios regarding the “open” parts of the remote sensing analysis process, from data acquisition, analysis to a final deliverable.


Guest Editors

Dr. Francesco Pirotti, University of Padova, Italy (francesco.pirotti@unipd.it)
Dr. Markus Neteler, mundialis GmbH & Co. KG, Bonn, Germany (neteler@mundialis.de)
Dr. Duccio Rocchini, Fondazione Edmund Mach, Italy (duccio.rocchini@fmach.it)


  1. Content type: Original Article

    Principal components analysis (PCA) is based conventially on the eigenvector decomposition (EVD). Mean-centering the input data prior to the eigenanalysis is treated as an integral part of the algorithm. It en...

    Authors: Nikos Alexandris, Sandeep Gupta and Nikos Koutsias

    Citation: Open Geospatial Data, Software and Standards 2017 2:17

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  2. Content type: Original article

    The increase in the number of remote sensing platforms, ranging from satellites to close-range Remotely Piloted Aircraft System (RPAS), is leading to a growing demand for new image processing and classificatio...

    Authors: Marco Piragnolo, Andrea Masiero and Francesco Pirotti

    Citation: Open Geospatial Data, Software and Standards 2017 2:16

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  3. Content type: Software

    Orfeo ToolBox is an open-source project for state-of-the-art remote sensing, including a fast image viewer, applications callable from command-line, Python or QGIS, and a powerful C++ API. This article is an i...

    Authors: Manuel Grizonnet, Julien Michel, Victor Poughon, Jordi Inglada, Mickaël Savinaud and Rémi Cresson

    Citation: Open Geospatial Data, Software and Standards 2017 2:15

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  4. Content type: Software

    The publication familiarizes the reader with MicMac - a free, open-source photogrammetric software for 3D reconstruction. A brief history of the tool, its organisation and unique features vis-à-vis other softw...

    Authors: Ewelina Rupnik, Mehdi Daakir and Marc Pierrot Deseilligny

    Citation: Open Geospatial Data, Software and Standards 2017 2:14

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  5. Content type: ORIGINAL ARTICLE

    In the last decade Photogrammetry has shown to be a valid alternative to LiDAR techniques for the generation of dense point clouds in many applications. However, dealing with large image sets is computationall...

    Authors: Oscar Martinez-Rubi, Francesco Nex, Marc Pierrot-Deseilligny and Ewelina Rupnik

    Citation: Open Geospatial Data, Software and Standards 2017 2:12

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  6. Content type: ORIGINAL ARTICLE

    Point clouds with increased point densities create new opportunities for analyzing landscape structure in 3D space. Taking advantage of these dense point clouds we have extended a 2D forest fragmentation index...

    Authors: Vaclav Petras, Douglas J. Newcomb and Helena Mitasova

    Citation: Open Geospatial Data, Software and Standards 2017 2:9

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  7. Content type: ORIGINAL ARTICLE

    New technologies for terrain reconstruction have increased the availability of topographic data at a broad range of resolutions and spatial extents. The existing digital elevation models (DEMs) can now be upda...

    Authors: Anna Petrasova, Helena Mitasova, Vaclav Petras and Justyna Jeziorska

    Citation: Open Geospatial Data, Software and Standards 2017 2:6

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