It can be trained end-to-end from few images and outperform the prior best method on the ISBI cell tracking challenge 2015. U-Net is one of the most successful and popular convolutional neural network architecture for medical image segmentation. My model is based on a variant of fully convolutional neural network, U-Net, which is developed by. I solved the problem as a semantic segmentation task in computer vision. I individually investigated and evaluated the similar approach on Spacenet Challenge dataset (my first submission with the approach is on May 13, a little earlier than their publication). , published on May 17, also investigates the use of OpenStreetMap for semantic labeling of satellite image. My best individual model simply uses OpenStreetMap layers and multispectral layers as the input of the deep neural network simultaneously (as described in Figure1).įigure 1: Best individual model with using OpenStreetMap and Pan-sharpened Multispectral data. In addition, I found the use of OpenStreetMap data is effective for predicting the building footprint. My final submission is the averaging ensemble from individually trained three U-Net models. I applied a modified U-Net model, one of deep neural network model for image segmentation. The polygon of building footprint proposed by the algorithm is considered as a true positive if its IOU (Intersection over Union, Jaccard index) score is higher than 0.5. The algorithm is evaluated based on F-score. SpaceNet Challenge Round2 asks its participants to submit an algorithm that inputs satellite images (of Las Vegas, Paris, Shanghai and Khartoum) and outputs polygons of building footprints. It consists of an online repository of freely available satellite imagery, co-registered map layers to train algorithms, and public challenges that aim to accelerate innovation in machine leanring. SpaceNet is a collaboration between DigitalGlobe (a commercial vendor of space imagery and geospatial content), CosmiQ Works (a division of In-Q-Tel Lab) and NVIDIA (the world leading company in visual computing technologies). For training a deep neural network model, the computational time on p2.xlarge (Tesla K80) is two times longer than my personal graphic card (GeForce GTX 1080).Adding OpenStreetMap layers into the input of U-Net model significantly improves F-score.and visit Maxar sites must be fully vaccinated for COVID-19 no later than January 18, 2022, except in cases where legally entitled to an exception. employees and international employees who work in the U.S. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected veteran status, age, or any other characteristic protected by law. Maxar Technologies values diversity in the workplace and is an equal opportunity/affirmative action employer. Must be enrolled in a bachelor's degree in Computer Science, Data Science, Software Engineering or related programįamiliarity with geospatial data formats and Earth Observation dataĮxperience using Amazon Web Services (AWS), specifically for data hosting Support data engineering and algorithm development and testing tasks.Īssist with authoring publications, to include research papers, blog posts and infographics. Work with SpaceNet Program Managers and Technical Lead to define SpaceNet project topics. The individual will be involved in defining SpaceNet challenge topics, developing and testing algorithms used to support a range of automated extraction workflows from Earth observation data and assist with drafting SpaceNet research publications. In this role, you will support the development and execution of our Geospatial Machine Learning project, SpaceNet. Maxar is seeking a Geospatial Machine Learning intern to join our team virtually for the Fall 2022 semester.
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