Afforestation and reforestation are popular strategies for mitigating climate change by enhancing carbon sequestration. However, the effectiveness of these efforts is often self-reported by project developers, or certified through processes with limited external validation. This leads to concerns about data reliability and project integrity. In response to increasing scrutiny of voluntary carbon markets, this study presents a dataset on global afforestation and reforestation efforts compiled from primary (meta-)information and augmented with time-series satellite imagery and other secondary data. Our dataset covers 1,289,068 planting sites from 45,628 projects spanning 33 years. Since any remote sensing-based validation effort relies on the integrity of a planting site's geographic boundary, this dataset introduces a standardized assessment of the provided site-level location information, which we summarize in one easy-to-communicate key indicator: LDIS -- the Location Data Integrity Score. We find that approximately 79\% of the georeferenced planting sites monitored fail on at least 1 out of 10 LDIS indicators, while 15\% of the monitored projects lack machine-readable georeferenced data in the first place. In addition to enhancing accountability in the voluntary carbon market, the presented dataset also holds value as training data for e.g. computer vision-related tasks with millions of linked Sentinel-2 and Planetscope satellite images.

US space agency NASA has collaborated with Google to help local government monitor and predict the air quality. The duo will build advanced machine learning-based algorithms and link space data with Google Earth Engine data streams to generate high-resolution air quality maps in near real-time. Signed under the Space Act Agreement, Google and Nasa have committed to a 2 years Annex agreement under which they will leverage their expertise to help local governments make informed decisions about daily air quality monitoring and forecasts. The results will create city-scale, near real-time estimation and forecasting of harmful pollutants, such as nitrogen dioxide and fine particulate matter present in the atmosphere. Data will be collected from Google's Street View mapping vehicles, surface monitoring stations and other Earth monitoring satellites.

This article will guide the step-by-step process to publish a web app featuring split panels. The web app is created using the Earth Engine Cloud-computing platform. Earth Engine makes tons of satellite images available to analyze and display. It also provides web app publication. The web app we are going to discuss today is split panels.

Earth Engine, also referred to as Google Earth Engine, provides a cloud-computing platform for Remote Sensings, such as satellite image processing. We can use Javascript or Python to code Earth Engine. There are many kinds of Remote Sensing analyses available to run. In this article, we will discuss specifically Machine Learning for land cover classification based on satellite images. Before we get into the details, I want to describe more on Remote Sensing common knowledge because I assume some readers have Data Science, Machine Learning, or Statistics backgrounds.

But they are a few problems relating to using a traditional R CNN. Mainly that processing an image takes a lot of time. As the model is extracting 2000 regions to check. Prediction with the model takes around 40 seconds. A better version of R CNN was created by the same person.

This tutorial shows you how to perform supervised classification (e.g., Classification and Regression Trees [CART]) in Earth Engine. The Classifier package handles supervised classification by traditional ML algorithms running in Earth Engine. The training data is a FeatureCollection with a property storing the class label and properties storing predictor variables. Class labels should be consecutive, integers starting from 0. If necessary, use remap() to convert class values to consecutive integers. The predictors should be numeric.

This tutorial shows you how to perform unsupervised classification (e.g., KMeans clustering) in Earth Engine. The ee.Clusterer package handles unsupervised classification (or clustering) in Earth Engine. These algorithms are currently based on the algorithms with the same name in Weka. More details about each Clusterer are available in the reference docs in the Code Editor. Clusterers are used in the same manner as classifiers in Earth Engine.

Google Earth Engine for Machine Learning & Change Detection Students will gain access to and a thorough knowledge of the Google Earth Engine platform. Get introduced and advance JavaScript skills on Google Earth Engine platform. This course is designed to take users who use GIS for basic geospatial data/GIS/Remote Sensing analysis to perform more advanced geospatial analysis tasks using a variety of different data and applying Machine Learning state of the art algorithms. In addition to improving your skills in JavaScript, this course will make you proficient in Google Earth Engine for land use and land cover (LULC) mapping and change detection. As a result, you will be introduced to the exciting capabilities of Google Earth Engine which is a global leader for cloud computing in Geosciences!

Training and inference using ee.Classifier or ee.Clusterer is generally effective up to a request size of approximately 100 megabytes. This is only an approximate guideline due to additional overhead around the request, but note that for b 100 (i.e. Since Earth Engine processes 256x256 image tiles, inference requests on imagery must have b 400 (again assuming 32-bit precision of the imagery). Examples of machine learning using the Earth Engine API can be found on the Supervised Classification page or the Unsupervised Classification page. Regression is generally performed with an ee.Reducer as described on this page, but see also ee.Reducer.RidgeRegression.

With dozens of public satellites in orbit and many more scheduled over the next decade, the size and complexity of geospatial imagery continues to grow. It has become increasingly difficult to manage this flood of data and use it to gain valuable insights. That's why we're excited to announce that we're bringing two of the most important collections of public, cost-free satellite imagery to Google Cloud: Landsat and Sentinel-2. The Landsat mission, developed under a joint program of the USGS and NASA, is the longest continuous space-based record of Earth's land in existence, dating back to 1972 with the Landsat 1 satellite. Landsat imagery sets the standard for Earth observation data due to the length of the mission and the rich data provided by its multispectral sensors.