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The GeoforGood Summit 2019 drew its curtains close on 19 Sep 2019 and as a first time attendee, I was amazed to see the number of new developments announced at the summit. The summit — being a first of its kind — combined the user summit and the developers summit into one to let users benefit from the knowledge of new tools and developers understand the needs of the user. Since my primary focus was on large scale geospatial modeling, I attended the workshops and breakout sessions related to Google Earth Engine only. With that, let’s look at 3 new exciting developments to hit Earth Engine Updated documentation on machine learning Documentation really? Yes! As an amateur Earth Engine user myself, my number one complaint of the tool has been its abysmal quality of documentation spread between its app developers site, Google Earth’s blog, and their stack exchange answers. So any updates to the documentation is welcome. I am glad that the documentation has been updated to help the ever-exploding user base of geospatial data scientists interested in implementing machine learning and deep learning models. The documentation comes with its own example Colab notebooks. The Example notebooks include supervised classification, unsupervised classification, dense neural network, convolutional neural network, and deeplearning on Google Cloud. I found that these notebooks were incredibly useful to me to get started as there are quite a few non-trivial data type conversions ( int to float32 and so on) in the process flow. Earth Engine and AI Platform Integration Nick Clinton and Chris Brown jointly announced the much overdue Earth Engine + Google AI Platform integration. Until now, users were essentially limited to running small jobs on Google Colab’s virtual machine (VM) and hoping that the connection with the VM doesn’t time out (which usually lasts for about 4 hours). Other limitations include lack of any task monitoring or queuing capabilities. Not anymore! The new ee.Model() package let’s users communicate with a Google Cloud server that they can spin up based on their own needs. Needless to say, this is a HUGE improvement over the previous primitive deep learning support provided on the VM. Although it was free, one could simply not train, validate, predict, and deploy any model larger than a few layers. It had to be done separately on the Google AI Platform once the .TFRecord objects were created in their Google bucket. With this cloud integration, that task has been simplified tremendously by letting users run and test their models right from the Colab environment. The ee.Model() class comes with some useful functions such as ee.Model.fromAIPlatformPredictor() to make predictions on Earth Engine data directly from your model sitting on Google Cloud. Lastly, since your model now sits in the AI Platform, you can cheat and use your own models trained offline to predict on Earth Engine data and make maps of its output. Note that your model must be saved using tf.contrib.saved_model format if you wish to do so. The popular Keras function model.save_model('model.h5') is not compatible with ee.Model(). Moving forward, it seems like the team plans to stick to the Colab Python IDE for all deep learning applications. However, it’s not a death blow for the loved javascript code editor. At the summit, I saw that participants still preferred the javascript code editor for their non-neural based machine learning work (like support vector machines, random forests etc.). Being a python lover myself, I too go to the code editor for quick visualizations and for Earth Engine Apps! I did not get to try out the new ee.Model() package at the summit but Nick Clinton demonstrated a notebook where a simple working example has been hosted to help us learn the function calls. Some kinks still remain in the development— like limiting a convolution kernel to only 144 pixels wide during prediction because of “the way earth engine communicates with cloud platform” — but he assured us that it will be fixed soon. Overall, I am excited about the integration because Earth Engine is now a real alternative for my geospatial computing work. And with the Earth Engine team promising more new functions in the ee.Model() class, I wonder if companies and labs around the world will start migrating their modeling work to Earth Engine. Cooler Visualizations! Matt Hancher and Tyler Erickson displayed some new functionality related to visualizations and I found that it made it vastly simpler to make animated visuals. With ee.ImageCollection.getVideoThumbURL() function, you can create your own animated gifs within a few seconds! I tried it on a bunch of datasets and the speed of creating the gifs was truly impressive. Say bye to exporting each iteration of a video to your drive because these gifs appear right at the console using the print() command! Shown above is an example of global temperature forecast by time from the ‘NOAA/GFS0P25’ dataset. The code for making the gif can be found here. The animation is based on the example shown in the original blog post by Michael DeWitt and I referred to this gif-making tutorial on the developers page to make it. I did not get to cover all the new features and functionality introduced at the summit. For that, be on the lookout for event highlights on Google Earth’s blog. Meanwhile, you can check out the session resources from the summit for presentations and notebooks on topics that you are interested in. Presentation and resources Published in Medium