![]() Without sampling, the elevation profile would be just a line connecting two elevations. Why is this necessary? Imagine that the user draws a line with only two definition points (red dots). In the next step, we need to generate a set of points along the line. By the path, we mean a linestring with several definition points. We will use mapbox-gl-js library along with mapbox-gl-draw plugin which allows us to do it. The first thing we need to do is to create a simple web app with a map and add the ability to draw paths. The application we are going to build is a web application that allows us to draw a path and retrieve elevation profiles along that pathīefore we dive into the code, it is beneficial to understand the process of retrieving elevation from Terrain-RGB tiles. This article attempts to offer guidance and outline steps that need to be taken in order to get an elevation profile from the Terrain-RGB tileset. It is possible indeed! However, it is not straightforward and one can get sidetracked by many GIS-related challenges. ![]() You might have wondered if it is possible to access the elevation values and how to get the elevation profile in an HTML/javascript application. GLIMS (Global Land Ice Measurements from Space) is an initiative designed to monitor the world's glaciers primarily using data from optical satellite instruments.The MapTiler Terrain-RGB tileset contains elevations encoded in a simple png image format as RGB values. The currently used dataset was published in March 2019.įor polygons of glaciers we use open data from the GLIMS project. It results in a very detailed land cover, even in the most remote parts of Canada. These data are further processed and reclassified to fit in our schema. The land features of the CanVec series contain landscape features of Canada such as islands, shoreline delineation, wooded areas, saturated soil features, and landform features (esker, sand, etc.). The latest version is from June 2021.Ĭanadian open data offer the Land Features dataset as part of the CanVec series. Land Cover - Woodland is a dataset that gathers woodland datasets from each of the states with irregular updates according to USGS standards. Data have six classes: crop, forest, grass, scrub, snow, tree.įor woodland in the USA, we use a data source produced by The United States Geological Survey (USGS). ESA Landcover v1.1 is processed and vectorized into a data source which we use in our MapTiler Planet for global landcover from zoom 0 to zoom 9. Global landcover is a derivated product from imagery made by ESA as a part of the ESA Climate Change Initiative and in particular its Land Cover project. This means that MapTiler users now can see building footprints even in the tiniest mountain village in Japan. Thanks to our partnership with a Japanese company Mierune, we got access to a dataset from The Geospatial Information Authority of Japan (GSI). The building footprint is also updated on monthly basis. This causes one of the biggest steps forward in our maps Check out the difference between OpenMapTiles and MapTiler Planet in the US, Canada, Australia, Philippines, Malaysia, Uganda, Tanzania, Kenya or Nigeria. They combine the positional accuracy of Microsoft’s footprint data creation and authoritative tags such as name and address provided by authoritative sources in collaboration with Esri. The building footprints are a joint product of Microsoft Building Footprints and the Esri Community Maps Program. Our data from OSM are updated once a month after they’ve been scanned for vandalism, topology errors, and inconsistency. ![]() To avoid having low-quality or even misleading edits and inconsistency in data, we collaborate with our partners and use additional tools and technologies on top of OSM to drive a higher level of detail, quality, and accuracy on the map. Every day OSM data receive millions of updates from community contributors. OSM is a collaborative project to create a free editable map of the world and offers street-scale quality data with regular updates. OpenStreetMap data is the main data source of our maps. We always use the latest version of Natural Earth, currently 4.1.0 (). ![]() This dataset contains cultural vector data - such as countries, administrative divisions, urban polygons, or water boundaries - and physical vector data - such as the ocean, rivers, lakes minor islands, or glaciated areas. Due to its small scale, the detail and quality of these vector data are perfect for showing the entire planet up to zoom 6. Natural Earth is a public dataset at 1:10m, 1:50m, and 1:110 million scales. Using local data sources increases the quality of the whole dataset and brings the base maps to their best for our customers. MapTiler Planet is compiled from many data sources. ![]()
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