![]() ![]() ![]() Secondly, we will detail procedures for static mapping and exporting results as a GeoTIFF.įinally, the folium library will be introduced to make interactive maps. An application of this procedure will be done to extract land surface temperature in an urban and a rural area near the city of Lyon, France to illustrate the heat island effect. After some setup and some exploration of the Earth Engine Data Catalog, we’ll see how to handle geospatial datasets with pandas and make some plots with matplotlib.įirst, we’ll see how to get the timeseries of a variable for a region of interest. In this tutorial, an introduction to the Google Earth Engine Python API is presented. How can we manipulate these petabytes of data?.What data are available and where can it be found?.When using these geospatial data, a few questions arise: These geospatial data are used every day by scientists and engineers of all fields, to predict weather, prevent disasters, secure water supply, or study the consequences of climate change. ![]() wind speed, groundwater recharge), have become freely available from multiple national agencies and universities (e.g. land surface temperature, vegetation) or the output of large scale, even global models (e.g. Within the last decade, a large amount of geospatial data, such as satellite data (e.g. ![]()
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