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GTC ON-DEMAND

Presentation
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Abstract:
Learn how recent advances in AI can be used to map informal settlements, or slums, in developing countries. We'll show how slums can be mapped using machine learning with noisy annotations and multi-resolution, multi-spectral data. We'll discuss an effective end-to-end framework that detects and maps the locations of informal settlements using low-resolution, freely available Sentinel-2 satellite imagery. Our talk will examine different approaches based on multi-spectral information to identify roofing material types and show how our work can be used for slums all over the world. We'll also describe how multi-spectral, multi-resolution and multi-temporal satellite imagery can be used during natural disasters to quantify the impact on urban infrastructure. This session presents research undertaken in the NASA and ESA Frontier Development Lab.
Learn how recent advances in AI can be used to map informal settlements, or slums, in developing countries. We'll show how slums can be mapped using machine learning with noisy annotations and multi-resolution, multi-spectral data. We'll discuss an effective end-to-end framework that detects and maps the locations of informal settlements using low-resolution, freely available Sentinel-2 satellite imagery. Our talk will examine different approaches based on multi-spectral information to identify roofing material types and show how our work can be used for slums all over the world. We'll also describe how multi-spectral, multi-resolution and multi-temporal satellite imagery can be used during natural disasters to quantify the impact on urban infrastructure. This session presents research undertaken in the NASA and ESA Frontier Development Lab.  Back
 
Topics:
AI Application, Deployment & Inference, AI & Deep Learning Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9362
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Abstract:
Learn how recent advances in Earth observation are opening up a new exciting area for exploration of satellite image data with deep learning. Focusing on real-world scenarios, we will teach you how to analyze this exciting remote sensing data source with deep neural networks. An automated satellite image understanding is of high interest for various research fields and industry sectors such as the insurance, agriculture or investing industry. You will learn how to apply deep neural networks in natural disaster situations and for the classification of land-use, land-cover and building types.
Learn how recent advances in Earth observation are opening up a new exciting area for exploration of satellite image data with deep learning. Focusing on real-world scenarios, we will teach you how to analyze this exciting remote sensing data source with deep neural networks. An automated satellite image understanding is of high interest for various research fields and industry sectors such as the insurance, agriculture or investing industry. You will learn how to apply deep neural networks in natural disaster situations and for the classification of land-use, land-cover and building types.  Back
 
Topics:
AI & Deep Learning Business Track (High Level), GIS, AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S81028
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Abstract:
We address the challenge of land use and land cover classification using remote sensing satellite images. For this challenging task, we use the openly and freely accessible Sentinel-2 satellite images. Our novel dataset covers 13 different spectral bands and consists of 27,000 labeled images. We present an evaluation of this dataset using deep Convolutional Neural Networks (CNN). In addition, we compare our results to existing benchmark datasets. With the proposed new dataset, we achieved an overall accuracy of 98.57%. We demonstrate how the classification system can be used for detecting land use and land cover changes and how it can assist in improving geographical maps.
We address the challenge of land use and land cover classification using remote sensing satellite images. For this challenging task, we use the openly and freely accessible Sentinel-2 satellite images. Our novel dataset covers 13 different spectral bands and consists of 27,000 labeled images. We present an evaluation of this dataset using deep Convolutional Neural Networks (CNN). In addition, we compare our results to existing benchmark datasets. With the proposed new dataset, we achieved an overall accuracy of 98.57%. We demonstrate how the classification system can be used for detecting land use and land cover changes and how it can assist in improving geographical maps.  Back
 
Topics:
Other
Type:
Talk
Event:
GTC Europe
Year:
2017
Session ID:
23480
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