Thereafter, I served for many years in the faculty of the Dept. of Aerospace Engineering Sciences, with specialization in Remote Sensing, Earth and Space Science, as a Fulbright fellow. I received my PhD in 2013 from the University of Colorado, Boulder, USA, from the Dept.
SpaceNet datasets can be accessed on AWS here. To read more about the launch of SpaceNet, see coverage on GISCafe, TechCrunch, MIT Technology Review, and Popular Science. SpaceNet plans a massive increase in both images and labeled features to be made available over the platform in the future. Incidentally, the name SpaceNet is inspired from ImageNet, a similar database of images created to help spur early advancements in computer vision. Application examples include detection of each building as a separate object and determining a bounding box around it, and semantic segmentation to partition the image into regions of pixels that can be given a common label, such as “building”, “forest”, “road”, or “water”.
#SPACENET DATASET CODE#
Scripts are already cropping up on GitHub for manipulating and using the satellite imagery data on SpaceNet: see code examples from Development Seed here and from CosmiQ Works here. NVIDIA has also released a detailed case study of analysis of SpaceNet data using their Deep Learning GPU Training System ( DIGITS) platform, demonstrating the power and capability of GPU-based deep learning algorithms applied over high resolution satellite imagery.
This dataset is being made public to advance the development of algorithms to automatically extract geometric features such as roads, building footprints, and points of interest using satellite imagery. True color WV-2 high resolution imagery sample from the SpaceNet repository, along with corresponding building footprints.
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