No augmentation with ensembling was performed on validation or test data. This image of Palm Jumeirah in Dubai was captured by Capella’s Sequoia radar satellite. Slingshot Aerospace. The key to resolving this was to realize that from a common sense perspective — waterways always touch the boundary of the image, while standing water mostly does not (or has a small overlap area / dimension only). However, in the real world, clouds and competition for satellite time are significant obstacles. Secondly, many vehicles were very hard to distinguish between large and small classes both in terms of visibility (blurred) and mask areas. Users can explore the globe by entering addresses and … Participants were allowed to make any shapes and use any colours. U-NET training & ensembling with a variety of models that permuted bands and scales. For oversampled classes only 5% random patch were used. Roads 4. Knowledge accumulated from vision/deep learning related home projects and other statistical learning competitions has also helped me in this effort. In this interview, first place winner Kyle Lee gives a detailed overview of his approach in this image segmentation competition. Complex Imagery and Map Data Projects Completed at Once; Impossible Ones Take Just a Bit Longer. It took about three days to train and predict — assuming all models and all preprocessing scales can be run in parallel. In fact, if I am not mistaken, most — if not all — of the top competitors used some variant of the UNET. Additionally, both vehicle masks were cleaned by negating their masks with buildings, trees, and other classes. The primary goal of this challenge is accurate semantic segmentation of different … Contact us; Advertise with us; Shopping. "This particular challenge is difficult because many circular features are not going to be perfectly circular nor similar in size," said Jack Brandy, geospatial intelligence capabilities integration officer at NGA. In terms of submissions, I used a majority of the submissions trying to fine tune polygon approximation. As a training set, they provided 25 high-resolution satellite images representing 1 km2areas. Ultimately, I ended up using rasterio/shapely to perform polygon to WKT conversion. Our approach is based on an adaptation of fully convolutional neural network for multispectral data processing. 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. Moreover, I chose only RGB images, since in all other bands vehicles were either not visible, or displaced significantly). The overall winner, Graniot from Spain won €5,000 with their web application for agronomists and farmers to conduct weekly monitoring of their crops using European satellite technologies. rather than 0.49272. Compare that to OneWeb, a SpaceX competitor, whose satellite constellation will orbit at 1,200 … Matthew Nelson Optimization wise I used the Jaccard loss directly with Adam as optimizer (I did not get much improvement from NAdam). It should be noted that there are likely to be plenty of important space tech or satellite-related startups who don’t use artificial intelligence at all. "NGA mission success is contingent on a world-class workforce with a wide diversity of opinions and expertise,” NGA deputy director and 2020 Wash100 Award recipient Dr. Stacey Dixon. Keras with Theano backend + OpenCV / Rasterio / Shapely for polygon manipulation. (3)"Host" is the host(s) of the Competitions. The competition task was to create a 50 drone New Year animation with a maximum length of 5 minutes using Blender animation software. I used three desktops for this contest. Some of the solution sharing by the top competitors were absolutely fascinating as well — especially clever tricks with multi-scale imagery in a single network. clock-data recovery, locked loops, high-speed I/O, etc. VARIOUS UNET ARCHITECTURES FOR DIFFERENT CLASSES. 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. As primary data source RapidEye will operate an innovative space based geo-information system. Every minute, the world loses an area of forest the size of 48 football fields. It has enabled us to better … Moreover, the NDWI mask (generated as part of waterways) could be overlapped with the raw standing water predictions, and very close broken segments could be merged (convexHull) to form a complete contour that may touch the boundary of the image. However, the process of creating the digital images from them is different, so is the application of the images, at times. All other classes were using the correct shapely versions of the submission script. 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. Global 30 cm Satellite Imagery Offers a Highly Competitive Alternative to Aerial. Satellite images have reported an “extremely dangerous increase” in locust swarm activity in Kenya in the past week. From here, all you have to do is download. and depths were used depending on the various classes via cross-validation scores. One swarm measured 60 x 40 kilometres wide in the country’s northeast area, Intergovernmental Authority on Development (IGAD) said in a press release. All the Best Vehicles — I did some special work here to train and predict only on frames with roads and buildings. As I mentioned earlier I participated in one of the earliest segmentation challenges on Kaggle — the “Ultrasonic Nerve Segmentation”. For the A-bands I mostly did not use all the bands, but randomly skipped a few bands to save training time and RAM. Explore worldwide satellite imagery and 3D buildings and terrain for hundreds of cities. The proliferation of satellite imagery has given us a radically improved understanding of our planet. SSEC develops and utilizes instrumentation, algorithms, satellite ground and satellite archive systems to study the Earth and other planetary atmospheres. 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). During the day, I design high-speed circuits at a semiconductor startup — e.g. Don’t worry, most other competitors are starting on the same ground as you, especially with some of the new developments. Small vehicles Sample image from the training set wit… Google satellite downloader is one of them it will facilitate you to download images on several zoom level. The National Geospatial-Intelligence Agency is offering $50,000 in prizes for artificial intelligence solutions designed to help detect circles in satellite images. 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. In June, we concluded the second SpaceNet competition, a satellite imagery object detection competition hosted by CosmiQ Works, DigitalGlobe, and NVIDIA. Acquisitions in Satellite Imagery Machine Learning Companies. Authors: Vladimir Iglovikov, Sergey Mushinskiy, Vladimir Osin. You can use images from both outlets to study geography and survey land.