![]() ![]() Despite the enormous efforts spent, these tasks cannot be considered solved, yet. Spacenet buildinglabels example drivers#Further research drivers are very high-resolution data from new sensors and advanced processing techniques that rely on increasingly mature machine learning techniques. ![]() Focus is on detailed 2D semantic segmentation that assigns labels to multiple object categories. What makes this task challenging is the very heterogeneous appearance of objects like buildings, streets, trees and cars in very high-resolution data, which leads to high intra-class variance while the inter-class variance is low. I've served as mentor in the NASA International Space App Challenge Islamabad events in April 2015 and April 2016.One of the major topics in photogrammetry is the automated extraction of urban objects from data acquired by airborne sensors. ![]() I was an invited speaker at the TEDxIslamabad event held in Nov., 2014. I am an alumnus of Pakistan National Physics Talent Contest (NPTC), an alumnus of the Lindau Nobel Laureate Meetings, a Fulbright alumnus, and the Pakistan National Point of Contact for Space Generation Advisory Council (SGAC). I am a reviewer for IEEE Transactions on Geoscience & Remote Sensing, Forest Ecosystems, GIScience & Remote Sensing, Journal of African Earth Sciences, and Italian Journal of Agronomy. My research interests are: Remote sensing, Synthetic Aperture Radar (SAR) imagery and interferometric data processing & analysis, Visible/Infrared/High-resolution satellite image processing & analysis, Oceanography, Earth system study and modelling, LIDAR data processing and analysis, Scientific programming. My general expertise is in Remote Sensing where I have worked with various remote sensing datasets through my career, while for my PhD thesis I specifically worked on Remote Sensing using SAR (Synthetic Aperture Radar) and Oceanography, working extensively on development of techniques to measure ocean surface currents from space-borne SAR intensity images and interferometric data. of Space Science at Institute of Space Technology (IST), Islamabad, Pakistan, where I have been a founding member of the Geospatial Research & Education Lab (GREL). Currently, I'm an Assistant Professor in the Dept. of Aerospace Engineering Sciences, University of Colorado, Boulder, USA, under a Fulbright fellowship. I received my PhD (2013) in Remote Sensing, Earth and Space Science at 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 buildinglabels example 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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