𝐆𝐨𝐨𝐠𝐥𝐞 𝐄𝐚𝐫𝐭𝐡 𝐄𝐧𝐠𝐢𝐧𝐞
Open in Telegram
967
Subscribers
+324 hours
+287 days
+14030 days
Posts Archive
𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐈𝐦𝐚𝐠𝐞 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐄𝐱𝐩𝐨𝐫𝐭 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐝 𝐑𝐆𝐁 𝐈𝐦𝐚𝐠𝐞 𝐭𝐨 𝐃𝐫𝐢𝐯𝐞; 𝐆𝐨𝐨𝐠𝐥𝐞 𝐄𝐚𝐫𝐭𝐡 𝐄𝐧𝐠𝐢𝐧𝐞
@google_earth_engine
𝐌𝐮𝐭𝐚𝐧𝐠𝐚, 𝐎., & 𝐊𝐮𝐦𝐚𝐫, 𝐋. (2019). 𝐆𝐨𝐨𝐠𝐥𝐞 𝐄𝐚𝐫𝐭𝐡 𝐄𝐧𝐠𝐢𝐧𝐞 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬. 𝐑𝐞𝐦𝐨𝐭𝐞 𝐒𝐞𝐧𝐬𝐢𝐧𝐠, 11(5), 591. 𝐝𝐨𝐢:10.3390/rs11050591
@google_earth_engine
𝐆𝐨𝐫𝐞𝐥𝐢𝐜𝐤, 𝐍., 𝐇𝐚𝐧𝐜𝐡𝐞𝐫, 𝐌., 𝐃𝐢𝐱𝐨𝐧, 𝐌., 𝐈𝐥𝐲𝐮𝐬𝐡𝐜𝐡𝐞𝐧𝐤𝐨, 𝐒., 𝐓𝐡𝐚𝐮, 𝐃., & 𝐌𝐨𝐨𝐫𝐞, 𝐑. (2017). 𝐆𝐨𝐨𝐠𝐥𝐞 𝐄𝐚𝐫𝐭𝐡 𝐄𝐧𝐠𝐢𝐧𝐞: 𝐏𝐥𝐚𝐧𝐞𝐭𝐚𝐫𝐲-𝐬𝐜𝐚𝐥𝐞 𝐠𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐟𝐨𝐫 𝐞𝐯𝐞𝐫𝐲𝐨𝐧𝐞. 𝐑𝐞𝐦𝐨𝐭𝐞 𝐒𝐞𝐧𝐬𝐢𝐧𝐠 𝐨𝐟 𝐄𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭, 202, 18827. 𝐝𝐨𝐢:10.1016/j.rse.2017.06.031
@google_earth_engine
𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐆𝐨𝐨𝐠𝐥𝐞 𝐄𝐚𝐫𝐭𝐡 𝐄𝐧𝐠𝐢𝐧𝐞 𝐟𝐨𝐫 𝐞𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐚𝐥 𝐦𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠
@google_earth_engine
𝐆𝐄𝐄 𝐓𝐮𝐭𝐨𝐫𝐢𝐚𝐥
𝐇𝐨𝐰 𝐭𝐨 𝐝𝐞𝐯𝐞𝐥𝐨𝐩 𝐚𝐧 𝐄𝐚𝐫𝐭𝐡 𝐄𝐧𝐠𝐢𝐧𝐞 𝐚𝐩𝐩 𝐟𝐨𝐫 𝐦𝐚𝐩𝐩𝐢𝐧𝐠 𝐬𝐮𝐫𝐟𝐚𝐜𝐞 𝐰𝐚𝐭𝐞𝐫 𝐝𝐲𝐧𝐚𝐦𝐢𝐜𝐬
@google_earth_engine
Mateo-García, G., Gómez-Chova, L., Amorós-López, J., Muñoz-Marí, J., & Camps-Valls, G. (2018). Multitemporal Cloud Masking in the Google Earth Engine. Remote Sensing, 10(7), 1079. doi:10.3390/rs10071079
@google_earth_engine
4. Linear fit model (Linear Regression) across multiple raster layers.
The simplest approach to linear modelling in earth engine is linear fit, which calculates the least squares estimate of a linear function of one variable with a constant term. The data should be set up as a two-band input image, where the first band is the independent variable and the second band is the dependent variable. In this tutorial the dependent variable is Modis NDVI and the independent variable is time.
@google_earth_eangin
Access to the global SPEI Drought Index data in Netcdf format (on a scale of 1 to 48 months) at the following address:
http://digital.csic.es/handle/10261/128892
Learn machine learning algorithms
#Classification
#regression
#Reduce_dimensions ...
Algorithms are taught in theory and coded in software
🛰 @Google_Earth_Engine
Google Earth Engine Tutorial: #1 Supervised Classification
This Google Earth Engine video tutorial will show you how to do a Supervised Classification using the Sentinel-2 image and Random Forest Algorithm. Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples (Wikipedia). Random forests or random decision forests are an ensemble learning method for classification, regression, and other tasks that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random decision forests correct for decision trees' habit of overfitting to their training set (Wikipedia).
@google_earth_engine
Zhou, B., Okin, G. S., & Zhang, J. (2020). Leveraging Google Earth Engine (GEE) and machine learning algorithms to incorporate in situ measurement from different times for rangelands monitoring. Remote Sensing of Environment, 236, 111521. doi:10.1016/j.rse.2019.111521
Google Earth Engine Download Satellite Bands and Stack in Qgis.
@google_earth_engine
