ºÝºÝߣshows by User: rlewis48 / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: rlewis48 / Fri, 02 Nov 2018 14:35:02 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: rlewis48 Deep Learning Applications to Satellite Imagery /slideshow/deep-learning-applications-to-satellite-imagery/121556962 intelaidc2018adamvanettenmetropolis524-181102143502
These are the slides from Intel's AI DevCon 2018 Conference. The video from the workshop is available online.The last few years has seen a significant increase in the launch of commercial and federal satellite imaging platforms. As these data become more widely available, so too have the data science challenges and research opportunities. In this hands-on workshop, CosmiQ Works and Intel AI Lab will introduce the business use cases and research questions around leveraging this imagery, as well as helpful tools and datasets to ease the friction. We will guide attendees through a hands-on exercise using the tools to train a small network on Intel® Xeon® Processors to detect buildings or road networks using the SpaceNet™ dataset. Join us to learn how to explore this exciting area of applied deep learning.]]>
These are the slides from Intel's AI DevCon 2018 Conference. The video from the workshop is available online.The last few years has seen a significant increase in the launch of commercial and federal satellite imaging platforms. As these data become more widely available, so too have the data science challenges and research opportunities. In this hands-on workshop, CosmiQ Works and Intel AI Lab will introduce the business use cases and research questions around leveraging this imagery, as well as helpful tools and datasets to ease the friction. We will guide attendees through a hands-on exercise using the tools to train a small network on Intel® Xeon® Processors to detect buildings or road networks using the SpaceNet™ dataset. Join us to learn how to explore this exciting area of applied deep learning.]]> Fri, 02 Nov 2018 14:35:02 GMT /slideshow/deep-learning-applications-to-satellite-imagery/121556962 rlewis48@slideshare.net(rlewis48) Deep Learning Applications to Satellite Imagery rlewis48 These are the slides from Intel's AI DevCon 2018 Conference. The video from the workshop is available online.The last few years has seen a significant increase in the launch of commercial and federal satellite imaging platforms. As these data become more widely available, so too have the data science challenges and research opportunities. In this hands-on workshop, CosmiQ Works and Intel AI Lab will introduce the business use cases and research questions around leveraging this imagery, as well as helpful tools and datasets to ease the friction. We will guide attendees through a hands-on exercise using the tools to train a small network on Intel® Xeon® Processors to detect buildings or road networks using the SpaceNet™ dataset. Join us to learn how to explore this exciting area of applied deep learning. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/intelaidc2018adamvanettenmetropolis524-181102143502-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> These are the slides from Intel&#39;s AI DevCon 2018 Conference. The video from the workshop is available online.The last few years has seen a significant increase in the launch of commercial and federal satellite imaging platforms. As these data become more widely available, so too have the data science challenges and research opportunities. In this hands-on workshop, CosmiQ Works and Intel AI Lab will introduce the business use cases and research questions around leveraging this imagery, as well as helpful tools and datasets to ease the friction. We will guide attendees through a hands-on exercise using the tools to train a small network on Intel® Xeon® Processors to detect buildings or road networks using the SpaceNet™ dataset. Join us to learn how to explore this exciting area of applied deep learning.
Deep Learning Applications to Satellite Imagery from rlewis48
]]> 276 2 https://cdn.slidesharecdn.com/ss_thumbnails/intelaidc2018adamvanettenmetropolis524-181102143502-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 State of the Map US 2018: Analytic Support to Mapping Contributors /slideshow/state-of-the-map-us-2018-analytic-support-to-mapping-contributors-120352786/120352786 sotmus18spacenetslidesharev3-181022221330
Significant advances in machine learning techniques for image classification, object detection and image segmentation have profound implications for crowdsourced mapping applications. Recent open source initiatives such as SpaceNet have strived to direct more research and development towards specific foundational mapping functions such as building detection and road network and routing identification. As these machine learning techniques mature, mapping contributors need to understand and engage the research community to help structure the application of these new techniques against a diverse of mapping challenges. Yet, currently, it is difficult translate mapping requirements to machine learning evaluation metrics, and vice versa. This presentation will discuss a proposed framework for defining levels of analyst augmentation that will allow mapping contributors and machine learning researchers to better understand each other and help direct the application of these advanced algorithms against mapping problems. Specifically, it will focus on relevant use case of mapping requirements, before, during and after a natural disaster and demonstrate a framework to understand what capabilities are nearing readiness.]]>
Significant advances in machine learning techniques for image classification, object detection and image segmentation have profound implications for crowdsourced mapping applications. Recent open source initiatives such as SpaceNet have strived to direct more research and development towards specific foundational mapping functions such as building detection and road network and routing identification. As these machine learning techniques mature, mapping contributors need to understand and engage the research community to help structure the application of these new techniques against a diverse of mapping challenges. Yet, currently, it is difficult translate mapping requirements to machine learning evaluation metrics, and vice versa. This presentation will discuss a proposed framework for defining levels of analyst augmentation that will allow mapping contributors and machine learning researchers to better understand each other and help direct the application of these advanced algorithms against mapping problems. Specifically, it will focus on relevant use case of mapping requirements, before, during and after a natural disaster and demonstrate a framework to understand what capabilities are nearing readiness.]]> Mon, 22 Oct 2018 22:13:30 GMT /slideshow/state-of-the-map-us-2018-analytic-support-to-mapping-contributors-120352786/120352786 rlewis48@slideshare.net(rlewis48) State of the Map US 2018: Analytic Support to Mapping Contributors rlewis48 Significant advances in machine learning techniques for image classification, object detection and image segmentation have profound implications for crowdsourced mapping applications. Recent open source initiatives such as SpaceNet have strived to direct more research and development towards specific foundational mapping functions such as building detection and road network and routing identification. As these machine learning techniques mature, mapping contributors need to understand and engage the research community to help structure the application of these new techniques against a diverse of mapping challenges. Yet, currently, it is difficult translate mapping requirements to machine learning evaluation metrics, and vice versa. This presentation will discuss a proposed framework for defining levels of analyst augmentation that will allow mapping contributors and machine learning researchers to better understand each other and help direct the application of these advanced algorithms against mapping problems. Specifically, it will focus on relevant use case of mapping requirements, before, during and after a natural disaster and demonstrate a framework to understand what capabilities are nearing readiness. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sotmus18spacenetslidesharev3-181022221330-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Significant advances in machine learning techniques for image classification, object detection and image segmentation have profound implications for crowdsourced mapping applications. Recent open source initiatives such as SpaceNet have strived to direct more research and development towards specific foundational mapping functions such as building detection and road network and routing identification. As these machine learning techniques mature, mapping contributors need to understand and engage the research community to help structure the application of these new techniques against a diverse of mapping challenges. Yet, currently, it is difficult translate mapping requirements to machine learning evaluation metrics, and vice versa. This presentation will discuss a proposed framework for defining levels of analyst augmentation that will allow mapping contributors and machine learning researchers to better understand each other and help direct the application of these advanced algorithms against mapping problems. Specifically, it will focus on relevant use case of mapping requirements, before, during and after a natural disaster and demonstrate a framework to understand what capabilities are nearing readiness.
State of the Map US 2018: Analytic Support to Mapping Contributors from rlewis48
]]> 204 1 https://cdn.slidesharecdn.com/ss_thumbnails/sotmus18spacenetslidesharev3-181022221330-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/intelaidc2018adamvanettenmetropolis524-181102143502-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/deep-learning-applications-to-satellite-imagery/121556962 Deep Learning Applicat... https://cdn.slidesharecdn.com/ss_thumbnails/sotmus18spacenetslidesharev3-181022221330-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/state-of-the-map-us-2018-analytic-support-to-mapping-contributors-120352786/120352786 State of the Map US 20...