Accelerated mapping from the Sky: object detection with high resolution remote sensing images
Accelerated mapping from the Sky: object detection with high resolution remote sensing images


Rapid developments in remote sensing technology have brought significant opportunities by providing cutting- edge optical remote sensing data in a cost-effective manner. In particular, rich spatial details embedded in high-resolution remote sensing images provide great potential to understand our environment. In this tutorial, we will cover the basic characteristics of remote sensing images, and introduce open source software and deep learning libraries for geospatial data analysis. We will present the use of a convolutional neural network architecture to perform the tasks of object detection and segmentation with remote sensing images spanning very large geographical extents. We will also talk about the multi-GPU training that enables accelerated geospatial intelligence extraction.​


H. Lexie Yang is a machine learning and geospatial research scientist at the Oak Ridge National Laboratory. Her research interests focus on high performance machine learning approaches for large scale data analysis, transfer learning and data fusion for multi-modality/multi-source remote sensing images and automate feature learning with deep learning methods. She received a PhD in Civil Engineering from Purdue University.

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