3-band Worldview-2 images are standard natural color images, which means they have three channels containing reflected light intensity in thin spectral bands around the red, green and blue light wavelengths (659, 546 and 478 nanometres (nm) respectively). Worldview-2 is sensitive to light in a wide range of wavelengths. Each image covers 200m 2 on the ground and has a pixel resolution of ~50cm. The first Area of Interest (AOI) released in the SpaceNet dataset contains two sets of over 7000 images by the DigitalGlobe Worldview-2 satellite over Rio de Janeiro, Brazil. We hope that this demonstration of automated building detection will inspire other novel applications of deep learning to the SpaceNet data. In this post we demonstrate how DIGITS can be used to train two different types of convolutional neural network for detecting buildings in the SpaceNet 3-band imagery. NVIDIA is proud to support SpaceNet by demonstrating an application of the SpaceNet data that is made possible using GPU-accelerated deep learning. State-of-the-art Artificial Intelligence tools like deep learning show promise for enabling automated extraction of this information with high accuracy. This information can be used in important applications like real-time mapping for humanitarian crisis response, infrastructure change detection for ensuring high accuracy in the maps used by self-driving cars or figuring out precisely where the world’s population lives.
The SpaceNet release is unprecedented: it’s the first public dataset of multi-spectral satellite imagery at such high resolution (50 cm) with building annotations. This public dataset of high-resolution satellite imagery contains a wealth of geospatial information relevant to many downstream use cases such as infrastructure mapping, land usage classification and human geography estimation. DigitalGlobe, CosmiQ Works and NVIDIA recently announced the launch of the SpaceNet online satellite imagery repository.
0 kommentar(er)
