2/7/2024 0 Comments Google maps satellite 2000![]() Second, Landsat 8- to 16-day data were used to time-composite 10-band (blue, green, red, near-infrared, short-wave infrared band 1, short-wave infrared band 2, thermal infrared, enhanced vegetation index, normalized difference water index, and normalized difference vegetation index) Landsat 30-m resolution data cubes for every 2- to 4-month time period during 3- to 4-year periods (stated as nominal-year 2015 or, simply, 2015), along with two additional 30-m resolution bands (Shuttle Radar Topography Mission elevation, and slope) in each of the 74 AEZs. First, the world was segmented into 74 agroecological zones (AEZs). The five key steps involved nine distinct phases. The work, which involved a paradigm shift in how global cropland-extent maps are produced, involved the following five key steps: (1) petabyte-scale computing that involved multiyear, 8- to 16-day, time-series Landsat 30-m resolution data for the global land surface (2) composition of analysis-ready data (ARD) cubes (3) creation of a large global-reference data hub for machine learning (4) use of multiple machine-learning algorithms (MLAs) by writing software and computing in the cloud and (5) Google Earth Engine (GEE) cloud computing. Given these realities, the overarching goal of this study was to produce a Landsat satellite-derived global cropland-extent product at 30-m resolution. The most fundamental cropland product is the high-resolution cropland-extent map because all higher level cropland products, such as crop-watering method (that is, whether crops are irrigated or rainfed), crop types, cropping intensities, cropland fallows, crop productivity, and crop-water productivity, are dependent on a precise and accurate cropland-extent product. These limitations can be overcome by producing high-resolution cropland-extent maps using satellite-sensor data, such as Landsat 30-m resolution or higher. Existing maps have limitations, in that they are (1) mapped using coarse-resolution remote-sensing data, resulting in the lack of precise mapping location of croplands and their accuracies (2) derived by collecting and collating national statistical data that are often subjective, leading to substantial uncertainties in cropland-area estimates, as well as their locations and (3) extracted from one or more classes of a land use–land cover product in which cropland classes are not the focus of mapping, leading to their mixing with other classes and creating significant errors of omission and commission. We believe that these changes have the potential to eliminate 100 million hard-braking events in routes driven with Google Maps each year, so you can rely on Maps to get you from A to B quickly - but also more safely.Global food and water security analysis and management require precise and accurate global cropland-extent maps. ![]() We’ll automatically recommend that route if the ETA is the same or the difference is minimal. With this update, we’ll take the fastest routes and identify which one is likely to reduce your chances of encountering a hard-braking moment. Here’s how it works: Every time you get directions in Maps, we calculate multiple route options to your destination based on several factors, like how many lanes a road has and how direct a route is. Soon, Google Maps will reduce your chances of having hard-braking moments along your drive thanks to help from machine learning and navigation information. ![]() According to research from experts at the Virginia Tech Transportation Institute, these hard-braking moments - incidents along a route that cause a driver to sharply decelerate - can be a leading indicator of car crash likelihood. ![]() As you approach a busy intersection, the traffic slows suddenly and you have to slam on your brakes. ![]()
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