Remote Sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest (RF), were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use (LCLU) classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications has significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.

Post-disaster damage assessment is urgent for rapid save and rescue missions. Satellite and airborne imagery platforms take vertical images, in which only buildings’ roofs are observable. Unmanned aerial vehicles (UAVs) provide an alternative platform that can obtain oblique images from a scene but suffer from occlusion (hiding from view) and a smaller field of view. As imagery from each platform has several pros and cons, a fusion framework is required. A new framework is proposed in this article, based on building environment and image processing techniques to utilize the advantages of different platforms while avoiding the disadvantages. In this approach, a facade is detected in UAV oblique images by fusing a geometrical transformation and environmental information from vertical and oblique images. Fusion of roof and facade features is used to improve damage scale estimation. Damage is estimated automatically for both roof-only and facade-plus-roof images that are sourced from satellite and fused data respectively. The proposed approach is applied to several different datasets including the Haiti earthquake of 2010, Hurricane Irene of 2011, Hurricane Sandy of 2012 and the Illinois tornadoes of 2015. The proposed automatic fusion framework led to enhanced damage scale estimation in comparison with the roof-only damage assessment in these cases.

The ability of the Canadian agriculture sector to make better decisions and manage its operations more competitively in the long term is only as good as the information available to inform decision-making. At all levels of Government, a reliable flow of information between scientists, practitioners, policy-makers, and commodity groups is critical for developing and supporting agricultural policies and programs. Given the vastness and complexity of Canada’s agricultural regions, space-based remote sensing is one of the most reliable approaches to get detailed information describing the evolving state of the country’s environment. Agriculture and Agri-Food Canada (AAFC)—the Canadian federal department responsible for agriculture—produces the Annual Space-Based Crop Inventory (ACI) maps for Canada. These maps are valuable operational space-based remote sensing products which cover the agricultural land use and non-agricultural land cover found within Canada’s agricultural extent. Developing and implementing novel methods for improving these products are an ongoing priority of AAFC. Consequently, it is beneficial to implement advanced machine learning and big data processing methods along with open-access satellite imagery to effectively produce accurate ACI maps. In this study, for the first time, the Google Earth Engine (GEE) cloud computing platform was used along with an Artificial Neural Networks (ANN) algorithm and Sentinel-1, -2 images to produce an object-based ACI map for 2018. Furthermore, different limitations of the proposed method were discussed, and several suggestions were provided for future studies. The Overall Accuracy (OA) and Kappa Coefficient (KC) of the final 2018 ACI map using the proposed GEE cloud method were 77% and 0.74, respectively. Moreover, the average Producer Accuracy (PA) and User Accuracy (UA) for the 17 cropland classes were 79% and 77%, respectively. Although these levels of accuracies were slightly lower than those of the AAFC’s ACI map, this study demonstrated that the proposed cloud computing method should be investigated further because it was more efficient in terms of cost, time, computation, and automation.

In this study, wetland trends in Alberta were investigated in the past four decades using Landsat satellite imagery to produce updated information about wetland changes and to prevent further degradation of these valuable natural resources. All the processing steps and analyses were conducted in Google earth engine (GEE) to produce 16 wetland maps from 1984 to 2020. A comprehensive change analysis showed 1 ) approximately 18% of the province was subjected to change; 2 ) in terms of wetland classes, there was a decreasing trend for the Shallow Water and Swamp classes and an increasing trend for the Fen and Marsh classes; 3 ) in terms of nonwetland classes, there was a considerable decreasing trend for the Forest class and increasing trend for the Grassland/Shrubland class; 4 ) wetland loss was approximately 22 000 km ² , which was mainly due to the conversion of wetlands to Forest and Grassland/Shrubland; 5) wetland gain was approximately 24 000 km ² , which was mainly due to the conversion from the Forest class to wetlands, especially the Swamp and Fen classes; 6) the highest class transition was from Cropland to Grassland/Shrubland and vice versa (29 000 km ² ), from Forest to different wetland classes (18 000 km ² ), and from Fen to Forest (6000 km ² ). In summary, the results of this study provided the first comprehensive information on wetland trends in Alberta over the past 37 years and will assist policymakers to adjust the required/established policies to mitigate the potential wetland changes due to anthropogenic activities and climate-related events.

Land Use/Land Cover (LULC) maps can be effectively produced by cost-effective and frequent satellite observations. Powerful cloud computing platforms are emerging as a growing trend in the high utilization of freely accessible remotely sensed data for LULC mapping over large-scale regions using big geodata. This study proposes a workflow to generate a 10 m LULC map of Europe with nine classes, ELULC-10, using European Sentinel-1/-2 and Landsat-8 images, as well as the LUCAS reference samples. More than 200 K and 300 K of in situ surveys and images, respectively, were employed as inputs in the Google Earth Engine (GEE) cloud computing platform to perform classification by an object-based segmentation algorithm and an Artificial Neural Network (ANN). A novel ANN-based data preparation was also presented to remove noisy reference samples from the LUCAS dataset. Additionally, the map was improved using several rule-based post-processing steps. The overall accuracy and kappa coefficient of 2021 ELULC-10 were 95.38% and 0.94, respectively. A detailed report of the classification accuracies was also provided, demonstrating an accurate classification of different classes, such as Woodland and Cropland. Furthermore, rule-based post processing improved LULC class identifications when compared with current studies. The workflow could also supply seasonal, yearly, and change maps considering the proposed integration of complex machine learning algorithms and large satellite and survey data.