Deep Neural Network Regression for Normalized Digital Surface Model Generation With Sentinel-2 Imagery

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Deep Neural Network Regression for Normalized Digital Surface Model Generation With Sentinel-2 Image... 

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Print ISSN: 1939-1404

Electronic ISSN: 2151-1535

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This is a link to an IEEE article that describes approaches using deep learning models for the extraction of high-resolution (<=10m) normalized digital surface models (nDSMs) from low-resolution Sentinel-2 (SAR) data.  Today, high spatial resolution nDSMs cannot be queried globally on an open-source basis.   The approach starts with a neural network architecture based on an enhanced U-net approach. 

U-net is available in ArcGIS, and can be modified using the ArcGIS.learn module.  See How U-net works? | ArcGIS API for Python | Esri Developer.

[ShareUser]

This is a link to an IEEE article that describes approaches using deep learning models for the extraction of high-resolution (<=10m) normalized digital surface models (nDSMs) from low-resolution Sentinel-2 (SAR) data.  Today, high spatial resolution nDSMs cannot be queried globally on an open-source basis.   The approach starts with a neural network architecture based on an enhanced U-net approach. 

U-net is available in ArcGIS, and can be modified using the ArcGIS.learn module.  See How U-net works? | ArcGIS API for Python | Esri Developer.