The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform

Joseph L Mundy - One of the best experts on this subject based on the ideXlab platform.

  • SatTel: A Framework for Commercial Satellite Imagery Exploitation
    2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
    Co-Authors: Andrew D. Gilliam, Thomas B. Pollard, Andrew Neff, Selene Chew, Todd V. Rovito, Robert Wagner, Scott Sorensen, Yi Dong, Joseph L Mundy
    Abstract:

    This paper presents the innovative SatTel framework, designed to automatically access, collate, process, and exploit Commercial Satellite imagery from a wide variety of vendors. Established vendors such as DigitalGlobe provide high resolution imagery with limited coverage, while disruptive vendors such as Planet and BlackSky provide low resolution imagery with near global coverage. SatTel provides a single point of entry for exploitation of these contrasting and complementary vendor capabilities. The authors illustrate the value of the SatTel framework via demonstrative change detection capabilities. SatTel change detection from small Satellite imagery based on comparison of image to image appearance achieves mean average precision (MAP) above 0.75 for many sites compared to ground truth analyst annotation. SatTel change detection from high resolution Satellite imagery based on multidimensional geometric structures achieves an average precision of 0.84 for elevation changes above 3.0 meters compared to ground truth analyst annotation.

  • WACV - SatTel: A Framework for Commercial Satellite Imagery Exploitation
    2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
    Co-Authors: Andrew D. Gilliam, Thomas B. Pollard, Andrew Neff, Selene Chew, Todd V. Rovito, Robert Wagner, Scott Sorensen, Yi Dong, Joseph L Mundy
    Abstract:

    This paper presents the innovative SatTel framework, designed to automatically access, collate, process, and exploit Commercial Satellite imagery from a wide variety of vendors. Established vendors such as DigitalGlobe provide high resolution imagery with limited coverage, while disruptive vendors such as Planet and BlackSky provide low resolution imagery with near global coverage. SatTel provides a single point of entry for exploitation of these contrasting and complementary vendor capabilities. The authors illustrate the value of the SatTel framework via demonstrative change detection capabilities. SatTel change detection from small Satellite imagery based on comparison of image to image appearance achieves mean average precision (MAP) above 0.75 for many sites compared to ground truth analyst annotation. SatTel change detection from high resolution Satellite imagery based on multidimensional geometric structures achieves an average precision of 0.84 for elevation changes above 3.0 meters compared to ground truth analyst annotation.

  • WACV - SatTel: A Framework for Commercial Satellite Imagery Exploitation
    2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
    Co-Authors: Andrew D. Gilliam, Thomas B. Pollard, Andrew Neff, Selene Chew, Todd V. Rovito, Robert Wagner, Scott Sorensen, Yi Dong, Joseph L Mundy
    Abstract:

    This paper presents the innovative SatTel framework, designed to automatically access, collate, process, and exploit Commercial Satellite imagery from a wide variety of vendors. Established vendors such as DigitalGlobe provide high resolution imagery with limited coverage, while disruptive vendors such as Planet and BlackSky provide low resolution imagery with near global coverage. SatTel provides a single point of entry for exploitation of these contrasting and complementary vendor capabilities. The authors illustrate the value of the SatTel framework via demonstrative change detection capabilities. SatTel change detection from small Satellite imagery based on comparison of image to image appearance achieves mean average precision (MAP) above 0.75 for many sites compared to ground truth analyst annotation. SatTel change detection from high resolution Satellite imagery based on multidimensional geometric structures achieves an average precision of 0.84 for elevation changes above 3.0 meters compared to ground truth analyst annotation.

Stuart R. Phinn - One of the best experts on this subject based on the ideXlab platform.

  • a new source for high spatial resolution night time images the eros b Commercial Satellite
    Remote Sensing of Environment, 2014
    Co-Authors: Noam Levin, Kasper Johansen, Jorg M. Hacker, Stuart R. Phinn
    Abstract:

    Abstract City lights present one of humankind's most unique footprints on Earth as seen from space. Resulting light pollution from artificial lights obscures the night sky for astronomy and has negative impacts on biodiversity as well as on human health. However, remote sensing studies of night lights to date have been mostly limited to coarse spatial resolution sensors such as the DMSP-OLS. Here we present a new source for high spatial resolution mapping of night lights from space, derived from a Commercial Satellite. We tasked the Israeli EROS-B Satellite to acquire two night-time light images (at a spatial resolution of 1 m) of Brisbane, Australia, and analyzed their radiometric quality and content with respect to land cover and land use. The spatial distribution of night lights as imaged by EROS-B corresponded with night-time images acquired by an airborne camera, although EROS-B was not as sensitive to low light levels. Using land cover and land use data at the statistical local area level, we could statistically explain 89% of the variability in night-time lights. Arterial roads and Commercial and service areas were found to be some of the brightest land use types. Overall, we found that EROS-B imagery provides fine spatial resolution images of night lights, opening new avenues for studying light pollution in cities worldwide.

  • A new source for high spatial resolution night time images — The EROS-B Commercial Satellite
    Remote Sensing of Environment, 2014
    Co-Authors: Noam Levin, Kasper Johansen, Jorg M. Hacker, Stuart R. Phinn
    Abstract:

    Abstract City lights present one of humankind's most unique footprints on Earth as seen from space. Resulting light pollution from artificial lights obscures the night sky for astronomy and has negative impacts on biodiversity as well as on human health. However, remote sensing studies of night lights to date have been mostly limited to coarse spatial resolution sensors such as the DMSP-OLS. Here we present a new source for high spatial resolution mapping of night lights from space, derived from a Commercial Satellite. We tasked the Israeli EROS-B Satellite to acquire two night-time light images (at a spatial resolution of 1 m) of Brisbane, Australia, and analyzed their radiometric quality and content with respect to land cover and land use. The spatial distribution of night lights as imaged by EROS-B corresponded with night-time images acquired by an airborne camera, although EROS-B was not as sensitive to low light levels. Using land cover and land use data at the statistical local area level, we could statistically explain 89% of the variability in night-time lights. Arterial roads and Commercial and service areas were found to be some of the brightest land use types. Overall, we found that EROS-B imagery provides fine spatial resolution images of night lights, opening new avenues for studying light pollution in cities worldwide.

Noam Levin - One of the best experts on this subject based on the ideXlab platform.

  • a new source for high spatial resolution night time images the eros b Commercial Satellite
    Remote Sensing of Environment, 2014
    Co-Authors: Noam Levin, Kasper Johansen, Jorg M. Hacker, Stuart R. Phinn
    Abstract:

    Abstract City lights present one of humankind's most unique footprints on Earth as seen from space. Resulting light pollution from artificial lights obscures the night sky for astronomy and has negative impacts on biodiversity as well as on human health. However, remote sensing studies of night lights to date have been mostly limited to coarse spatial resolution sensors such as the DMSP-OLS. Here we present a new source for high spatial resolution mapping of night lights from space, derived from a Commercial Satellite. We tasked the Israeli EROS-B Satellite to acquire two night-time light images (at a spatial resolution of 1 m) of Brisbane, Australia, and analyzed their radiometric quality and content with respect to land cover and land use. The spatial distribution of night lights as imaged by EROS-B corresponded with night-time images acquired by an airborne camera, although EROS-B was not as sensitive to low light levels. Using land cover and land use data at the statistical local area level, we could statistically explain 89% of the variability in night-time lights. Arterial roads and Commercial and service areas were found to be some of the brightest land use types. Overall, we found that EROS-B imagery provides fine spatial resolution images of night lights, opening new avenues for studying light pollution in cities worldwide.

  • A new source for high spatial resolution night time images — The EROS-B Commercial Satellite
    Remote Sensing of Environment, 2014
    Co-Authors: Noam Levin, Kasper Johansen, Jorg M. Hacker, Stuart R. Phinn
    Abstract:

    Abstract City lights present one of humankind's most unique footprints on Earth as seen from space. Resulting light pollution from artificial lights obscures the night sky for astronomy and has negative impacts on biodiversity as well as on human health. However, remote sensing studies of night lights to date have been mostly limited to coarse spatial resolution sensors such as the DMSP-OLS. Here we present a new source for high spatial resolution mapping of night lights from space, derived from a Commercial Satellite. We tasked the Israeli EROS-B Satellite to acquire two night-time light images (at a spatial resolution of 1 m) of Brisbane, Australia, and analyzed their radiometric quality and content with respect to land cover and land use. The spatial distribution of night lights as imaged by EROS-B corresponded with night-time images acquired by an airborne camera, although EROS-B was not as sensitive to low light levels. Using land cover and land use data at the statistical local area level, we could statistically explain 89% of the variability in night-time lights. Arterial roads and Commercial and service areas were found to be some of the brightest land use types. Overall, we found that EROS-B imagery provides fine spatial resolution images of night lights, opening new avenues for studying light pollution in cities worldwide.

Andrew D. Gilliam - One of the best experts on this subject based on the ideXlab platform.

  • SatTel: A Framework for Commercial Satellite Imagery Exploitation
    2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
    Co-Authors: Andrew D. Gilliam, Thomas B. Pollard, Andrew Neff, Selene Chew, Todd V. Rovito, Robert Wagner, Scott Sorensen, Yi Dong, Joseph L Mundy
    Abstract:

    This paper presents the innovative SatTel framework, designed to automatically access, collate, process, and exploit Commercial Satellite imagery from a wide variety of vendors. Established vendors such as DigitalGlobe provide high resolution imagery with limited coverage, while disruptive vendors such as Planet and BlackSky provide low resolution imagery with near global coverage. SatTel provides a single point of entry for exploitation of these contrasting and complementary vendor capabilities. The authors illustrate the value of the SatTel framework via demonstrative change detection capabilities. SatTel change detection from small Satellite imagery based on comparison of image to image appearance achieves mean average precision (MAP) above 0.75 for many sites compared to ground truth analyst annotation. SatTel change detection from high resolution Satellite imagery based on multidimensional geometric structures achieves an average precision of 0.84 for elevation changes above 3.0 meters compared to ground truth analyst annotation.

  • WACV - SatTel: A Framework for Commercial Satellite Imagery Exploitation
    2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
    Co-Authors: Andrew D. Gilliam, Thomas B. Pollard, Andrew Neff, Selene Chew, Todd V. Rovito, Robert Wagner, Scott Sorensen, Yi Dong, Joseph L Mundy
    Abstract:

    This paper presents the innovative SatTel framework, designed to automatically access, collate, process, and exploit Commercial Satellite imagery from a wide variety of vendors. Established vendors such as DigitalGlobe provide high resolution imagery with limited coverage, while disruptive vendors such as Planet and BlackSky provide low resolution imagery with near global coverage. SatTel provides a single point of entry for exploitation of these contrasting and complementary vendor capabilities. The authors illustrate the value of the SatTel framework via demonstrative change detection capabilities. SatTel change detection from small Satellite imagery based on comparison of image to image appearance achieves mean average precision (MAP) above 0.75 for many sites compared to ground truth analyst annotation. SatTel change detection from high resolution Satellite imagery based on multidimensional geometric structures achieves an average precision of 0.84 for elevation changes above 3.0 meters compared to ground truth analyst annotation.

  • WACV - SatTel: A Framework for Commercial Satellite Imagery Exploitation
    2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
    Co-Authors: Andrew D. Gilliam, Thomas B. Pollard, Andrew Neff, Selene Chew, Todd V. Rovito, Robert Wagner, Scott Sorensen, Yi Dong, Joseph L Mundy
    Abstract:

    This paper presents the innovative SatTel framework, designed to automatically access, collate, process, and exploit Commercial Satellite imagery from a wide variety of vendors. Established vendors such as DigitalGlobe provide high resolution imagery with limited coverage, while disruptive vendors such as Planet and BlackSky provide low resolution imagery with near global coverage. SatTel provides a single point of entry for exploitation of these contrasting and complementary vendor capabilities. The authors illustrate the value of the SatTel framework via demonstrative change detection capabilities. SatTel change detection from small Satellite imagery based on comparison of image to image appearance achieves mean average precision (MAP) above 0.75 for many sites compared to ground truth analyst annotation. SatTel change detection from high resolution Satellite imagery based on multidimensional geometric structures achieves an average precision of 0.84 for elevation changes above 3.0 meters compared to ground truth analyst annotation.

Robert Wagner - One of the best experts on this subject based on the ideXlab platform.

  • SatTel: A Framework for Commercial Satellite Imagery Exploitation
    2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
    Co-Authors: Andrew D. Gilliam, Thomas B. Pollard, Andrew Neff, Selene Chew, Todd V. Rovito, Robert Wagner, Scott Sorensen, Yi Dong, Joseph L Mundy
    Abstract:

    This paper presents the innovative SatTel framework, designed to automatically access, collate, process, and exploit Commercial Satellite imagery from a wide variety of vendors. Established vendors such as DigitalGlobe provide high resolution imagery with limited coverage, while disruptive vendors such as Planet and BlackSky provide low resolution imagery with near global coverage. SatTel provides a single point of entry for exploitation of these contrasting and complementary vendor capabilities. The authors illustrate the value of the SatTel framework via demonstrative change detection capabilities. SatTel change detection from small Satellite imagery based on comparison of image to image appearance achieves mean average precision (MAP) above 0.75 for many sites compared to ground truth analyst annotation. SatTel change detection from high resolution Satellite imagery based on multidimensional geometric structures achieves an average precision of 0.84 for elevation changes above 3.0 meters compared to ground truth analyst annotation.

  • WACV - SatTel: A Framework for Commercial Satellite Imagery Exploitation
    2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
    Co-Authors: Andrew D. Gilliam, Thomas B. Pollard, Andrew Neff, Selene Chew, Todd V. Rovito, Robert Wagner, Scott Sorensen, Yi Dong, Joseph L Mundy
    Abstract:

    This paper presents the innovative SatTel framework, designed to automatically access, collate, process, and exploit Commercial Satellite imagery from a wide variety of vendors. Established vendors such as DigitalGlobe provide high resolution imagery with limited coverage, while disruptive vendors such as Planet and BlackSky provide low resolution imagery with near global coverage. SatTel provides a single point of entry for exploitation of these contrasting and complementary vendor capabilities. The authors illustrate the value of the SatTel framework via demonstrative change detection capabilities. SatTel change detection from small Satellite imagery based on comparison of image to image appearance achieves mean average precision (MAP) above 0.75 for many sites compared to ground truth analyst annotation. SatTel change detection from high resolution Satellite imagery based on multidimensional geometric structures achieves an average precision of 0.84 for elevation changes above 3.0 meters compared to ground truth analyst annotation.

  • WACV - SatTel: A Framework for Commercial Satellite Imagery Exploitation
    2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
    Co-Authors: Andrew D. Gilliam, Thomas B. Pollard, Andrew Neff, Selene Chew, Todd V. Rovito, Robert Wagner, Scott Sorensen, Yi Dong, Joseph L Mundy
    Abstract:

    This paper presents the innovative SatTel framework, designed to automatically access, collate, process, and exploit Commercial Satellite imagery from a wide variety of vendors. Established vendors such as DigitalGlobe provide high resolution imagery with limited coverage, while disruptive vendors such as Planet and BlackSky provide low resolution imagery with near global coverage. SatTel provides a single point of entry for exploitation of these contrasting and complementary vendor capabilities. The authors illustrate the value of the SatTel framework via demonstrative change detection capabilities. SatTel change detection from small Satellite imagery based on comparison of image to image appearance achieves mean average precision (MAP) above 0.75 for many sites compared to ground truth analyst annotation. SatTel change detection from high resolution Satellite imagery based on multidimensional geometric structures achieves an average precision of 0.84 for elevation changes above 3.0 meters compared to ground truth analyst annotation.