I’d like information about a particular satellite mission! Take a look at our Sentinel Hub brochure for more information. Crops 7. From a per class effort perspective, I spent over 70% of the overall time on vehicles, standing water, and structures, and I spent the least time on crops. The proliferation of satellite imagery has given us a radically improved understanding of our planet. In addition, I also oversampled some of the rare classes in some of the ensemble models. Download PDF Abstract: This paper describes our approach to the DSTL Satellite Imagery Feature Detection challenge run by Kaggle. (3)"Host" is the host(s) of the Competitions. Waterway 8. USA. Title: Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition. As far as band usage is concerned, I mostly used panchromatic RGB + M-band and some of the SWIR (A) bands. The imagery provides an excellent view of the inauguration stands/seats along the west side of the Capitol as well as an overview of the Capitol grounds. Welcome to Alexa's Site Overview. This paper describes our approach to the DSTL Satellite Imagery Feature Detection challenge run by Kaggle. List of machine learning competitions for satellite imagery and remote sensing. However, in the real world, clouds and competition for satellite time are significant obstacles. As mentioned earlier, for vehicles I trained and predicted only on patches/windows with roads and/or buildings — this helped to cut down the amount of images needed for training, and allowed for significant oversampling of vehicle patches. Previously known as Flash Earth. The agency will foster partnerships with MITRE and Melwood to provide jobs for people with disabilities. The remaining (20%) was on developing the post and pre-processing flows. In this blog post we wish to present our deep learning solution and share the lessons that we have learnt in the process with you. Furthermore, since the challenge tasks will involve "in the wild" forms of classic computer vision problems, these datasets have the potential to become valuable testbeds for the design of robust vision algorithms, beyond the area of remote sensing. The sliding window steps are shown below: Oversampling standing water and waterway together was a good idea since it helped to reduce the amount of class confusion between the two, with reduced artifacts (particularly for standing water predictions). The task was to locate 10 different types of objects: 1. satellite mission database; I’d like to search for and download free satellite imagery for an area of interest! The idea is that networks that merge both small+large are able to predict better polygons (since there is no class confusion). Natural Language Processing and Word Embeddings (Part 1), Shortest Path Problem in Search of Algorithmic Solution, Face Identification: Siamese Convolutional Neural Nets, Fake and Genuine Currency Clustering using KMeans, Why Big Data And Machine Learning Are Important In Our Society, Continuous Proximal Policy Optimization Tutorial with OpenAI gym environment, On Learning and Learned Data Representation By Capsule Networks. Google satellite downloader is one of them it will facilitate you to download images on several zoom level. The first two were used for all the training/inferencing of all classes, while the last one (#3) was only run on crops. Since this was a neural network segmentation competition, most of time (80%+) was spent on tuning and training the different networks and monitoring the runs. Don’t worry, most other competitors are starting on the same ground as you, especially with some of the new developments. Get satellite imagery on your table without worrying about synchronization issues, storage, processing, de-compression algorithms, meta-data or sensor bands. Zoom Earth shows live weather satellite images updated in near real-time, and the best high-resolution aerial views of the Earth in a fast, zoomable map. Sorted by submission deadline. Multi-scaled patch / sliding window generation (256x256 & 288x288 primary, 224x224, 320x320 added for ensembling), and at edges the windows overlapped to cover the entire image. NGA said Monday that it is seeking automated approaches that can trace, delineate and describe circles in satellite imagery as part of the Circle Finder challenge. U-NET training & ensembling with a variety of models that permuted bands and scales. But the download speeds are still slow and sluggish. Google Earth is a computer program, formerly known as Keyhole EarthViewer, that renders a 3D representation of Earth based primarily on satellite imagery.The program maps the Earth by superimposing satellite images, aerial photography, and GIS data onto a 3D globe, allowing users to see cities and landscapes from various angles. Satellite images have reported an “extremely dangerous increase” in locust swarm activity in Kenya in the past week. Thus most of the foreign competition in the satellite remote sensing market is from imagery providers operating satellites built with substantial governmental funding, and in many cases built by domestic firms. Post-competition analysis showed that this approach helped large vehicle private LB score — which if I did not, would have dropped by -59%. As Canvas Ventures VC Ben Narasin told us in his “AI in Industry” podcast interview, AI is secondary to the business model and goals of the company. The Capella-2 satellite, developed by San Francisco-based Capella Space, … Secondly, many vehicles were very hard to distinguish between large and small classes both in terms of visibility (blurred) and mask areas. Focuses on the key global Commercial Satellite Imagery manufacturers, to define, describe and analyze the sales volume, value, market share, market competition landscape, SWOT analysis and development plans in next few years. This post-processing resolved class confusion between standing water and waterways, cleaned up artifacts on the roads, and gave some additional points to the large vehicle score. As primary data source RapidEye will operate an innovative space based geo-information system. Trees 6. To understand the structure of Commercial Satellite Imagery market by identifying its various sub segments. The National Geospatial-Intelligence Agency is offering $50,000 in prizes for artificial intelligence solutions designed to help detect circles in satellite images. Google Earth is a computer program, formerly known as Keyhole EarthViewer, that renders a 3D representation of Earth based primarily on satellite imagery.The program maps the Earth by superimposing satellite images, aerial photography, and GIS data onto a 3D globe, allowing users to see cities and landscapes from various angles. Vehicles — I did some special work here to train and predict only on frames with roads and buildings. Once again, thank you to Dstl and Kaggle for hosting and organizing this terrific image segmentation competition — I believe this is by far the most exciting (and busy, due to the number of classes) competition I have had, and I am sure this is true for many others too. AERIAL/SATELLITE IMAGERY: The NOAA Data Access Viewer holds satellite, aerial and LiDAR imagery. Roads 4. pancroma.com Competitive Analysis, Marketing Mix and Traffic . I’d like information about a particular satellite mission! The Host may be SIGNATE, Inc. (hereinafter referred to as the "Company") or the Company’s client companies, affiliated companies, schools or organizations, etc. Credit: NASA/METI/AIST/Japan Space Systems/U.S./Japan ASTER Science … All the Best 06/19/2017 ∙ by Vladimir Iglovikov, et al. (hereinafter referred to as the "Client(s)"). Dstl’s Satellite Imagery competition, which ran on Kaggle from December 2016 to March 2017, challenged Kagglers to identify and label significant features like … Small vehicles Sample image from the training set wit… Explore worldwide satellite imagery and 3D buildings and terrain for hundreds of cities. Users can explore the globe by entering addresses and … More details here! The datasets created and released for this competition may serve as reference benchmarks for future research in satellite image analysis. Immediately after that I was looking forward to the next image segmentation challenge, and this was the perfect opportunity. During the day, I design high-speed circuits at a semiconductor startup — e.g. Even though I don’t have direct deep learning research or work experience, the main area of my work that has really helped me in these machine/deep learning competitions is planning and building (coding) lots and lots of design automation flows very quickly. I also had a learning rate policy step which dropped the learning rate at around 0.2 of the initial rate for every 30 epochs. For the A-bands I mostly did not use all the bands, but randomly skipped a few bands to save training time and RAM. Lillian Kay Petersen, 17, has won the Regeneron Science Talent Search, a top science and math competition for high school seniors. He has been involved in data science and deep learning competitions since early 2016 out of his personal interest for automation and machine learning. satellite imagery is available at 0.8m high-resolution imagery products with a 23.4km swath; both space and ground segments deliver guaranteed timely information; Applied Fields. I would have added some ensembling to crops, added heat-map based averaging (and increase the test overlap windows at some expense of runtime), dilated structures training mask (which helped structure scoring for some competitors), and removed most of the expensive rare scale (320x320, for example) ensembling on tracks. Images , animations , and Google Earth files from the Hurricane Satellite (HURSAT) for hurricanes, typhoons, and tropical cyclones from 1983 through 2009. The public and private LB score for this class seemed competitive relative to other teams who may have used deep learning methods. First, enter in your area of interest. I ended up with a intersection of NDWI and CCCI masks (with boundary contact checking to filter out standing water / building artifacts) rather than using deep learning approaches, thus freeing up training resources for other classes. Having more systems helps in terms of creating experiments and ensemble permutations, but it’s not absolutely necessary if you have a strong flow or network. I first tried bounding boxes, then polygon approximation, and then polygon with erosion in OpenCV. By restricting training and prediction to only patches containing buildings and roads, I was naturally able to allow for oversampling of vehicle patches, and narrow down the scope of scenery for the network to focus on. Capella Space Capella Space is now capable of producing high resolution radar images of the Earth’s . Ensembling involved the use of mask arithmetic averaging (most classes), unions (only on standing water and large vehicles), intersections (only on waterways using NDWI and CCCI). The competition task was to create a 50 drone New Year animation with a maximum length of 5 minutes using Blender animation software. Satellite images (also Earth observation imagery, spaceborne photography, or simply satellite photo) are images of Earth collected by imaging satellites operated by governments and businesses around the world. I made tweaks to the original architecture with batch-normalization on the downstream paths + dropout on the post-merge paths, and all activation layers switched to Exponential Linear Unit (ELU). The other important classes which I spent a chunk of time on were the two vehicle classes. For small vehicles, it was basically just to take the average ensemble of small vehicle predictions, and remove whichever contours overlapped with large vehicles and/or over the area threshold. As a training set, they provided 25 high-resolution satellite images representing 1 km2areas. Only one fold per model was used to cut down on runtime in all cases. In the recent Kaggle competition Dstl Satellite Imagery Feature Detection our deepsense.ai team won 4th place among 419 teams. For standing water, one of the main issues with standing water was class confusion with waterways. Patience picked up from running and tweaking long circuit simulations at work over days/weeks were transferable and analogous to neural network training too. Satellite Image Analysis Reference Guide Datasets. Over the course of the competition, I definitely achieved this goal since this competition was extra fun — viewing pictures of natural scenery is therapeutic and kept me motivated everyday to improve my methodology. Having said that, I am still a beginner in many areas in data science — and still learning, of course. Optimization wise I used the Jaccard loss directly with Adam as optimizer (I did not get much improvement from NAdam). Moreover, data science is an ever evolving field. I believe stabilizing these two (actually three) classes — standing water, large and small vehicles made a large impact on my final score relative to other top competitors. In an effort to spur on machine learning advances in the satellite imagery field, Planet has launched a satellite data competition on Kaggle for the Amazon basin. The key competition that introduced me to the tools and techniques needed to win was Kaggle’s “Ultrasound Nerve Segmentation” that ended in August 2016 (and I saw many familiar names from that competition in this one too!). Imagery from NOAA's Environmental Visualization Laboratory showing imagery related to current weather and climate events. Executivegov, published by Executive Mosaic, is a leading nongovernmental … Slingshot Aerospace submissions trying to tune... Archive includes imagery from NOAA GOES ( Geostationary Operational Environmental satellite ) is. 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