The technique works by taking a time series of data and aggregating pixel values rather than choosing one real pixel for output that may not fit well with its neighbors. Our team derived their own statistical and mathematical methods to derive the best pixel values from each image to use to represent that area. To create the MapTiler satellite imagery, we set up an entirely new big data processing pipeline. All this means that our satellite imagery is very consistent and up-to-date. With plenty of good data to work with and our new approach, we didn’t have to rely on older imagery or factor in data from other sources. Thanks to their constellation of two satellites, Sentinel-2A and Sentinel-2B, the revisit time for most of the Earth is amazingly short, only 5 days. The data is very recent, from 20, and at a 10m/px resolution it is detailed for global coverage. The Copernicus project provided the latest Sentinel-2 imagery required to get the best results. Working in ESA’s Business Incubation Center also boosted our ability to turn satellite imagery into something beautiful. MapTiler has a history of collaborating with the European Space Agency (ESA) and their Copernicus earth observation project, winning two Copernicus Masters Awards. So even when the pixels are from images taken at different times, a much smoother result is achieved in the final mosaic image. The new algorithm we developed to create the cloudless pixels includes color-toning, which has removed many of the stripe effects seen in other products. As a result, our imagery is far more natural, without the need to artificially boost or change the colors. We managed to do this and still avoid clouds and their shadows where possible. Smarter processing allowed us to seek pixels from greener times of the year, such as early summer or rainy seasons. Other methods cause colors to fade, relying on much older images and those from dry seasons or winter to aid the search for cloud-free pixels. The techniques we have used on the imagery have preserved the natural colors of the earth. See the planet looking more beautiful than ever, free from clouds and with the lush foliage of early summer and the rainy seasons. The two ways of working with raster layers are very similar, the advantages of using TileJson ( recommended option) is that we have more information associated with the data source (metadata) and if the service provider changes any metadata our application will always be synchronized and working.With the 2021 satellite imagery from MapTiler, you can take a virtual flight onboard a spaceship orbiting 786km above the Earth. Raster tiles (Mercator XYZ) are loaded with ol.source.XYZ function. To use the 256px tiles you must use this URL in your layer Use 256x256 raster tiles for compatibility with certain libraries. Replace YOUR_MAPTILER_API_KEY_HERE with your actual MapTiler API key. Raster tiles TileJSON are loaded with ol.source.TileJSON function. In a second way we have to use the XYZ source in this case, we must indicate the URL of the tiles that we want to load in our map and we will not have any metadata associated with the source. The first way is to use the TileJSON source function this function is in charge of interpreting the TileJson file and creating the source with all the options and metadata. Next we will explain two ways how to create a map in OpenLayers using your MapTiler maps. Use recommended 512x512 raster tiles with TileJSON or XYZ. Read more about zoomable maps and the pyramid scheme in this article. This clever trick allows you to browse just a small part of the map without loading it whole while maintaining a feeling of exploring a single large document. jpg format) placed next to each other, ordered in a pyramid scheme. Zoomable raster maps consist of many raster map tiles (in the. Raster tiles map are actually nothing else than raster images.
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