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GTC On-Demand

AI and DL Business Track (high level)
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Earth Observation From Space: Deep Learning based Satellite Image Analysis
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
 
Keywords:
AI and DL Business Track (high level), GIS, AI and DL Research, GTC Silicon Valley 2018 - ID S81028
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Earth Observation: Land Use and Land Cover Classification Using Satellite Images
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
 
Keywords:
Other, GTC Europe 2017 - ID 23480
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