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, 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 classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications have 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.

/pdf/google-earth-engine-cloud-computing-platform-for-remote-9tftqyvl8w.pdf

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

The monitoring and assessment of land use/land cover (LULC) change over large areas are significantly important in numerous research areas, such as natural resource protection, sustainable development, and climate change However, accurately extracting LULC only using the spectral features of satellite images is difficult owing to landscape heterogeneities over large areas To improve the accuracy of LULC classification, numerous studies have introduced other auxiliary features to the classification model The Google Earth Engine (GEE) not only provides powerful computing capabilities, but also provides a large amount of remote sensing data and various auxiliary datasets However, the different effects of various auxiliary datasets in the GEE on the improvement of the LULC classification accuracy need to be elucidated along with methods that can optimize combinations of auxiliary datasets for pixel- and object-based classification Herein, we comprehensively analyze the performance of different auxiliary features in improving the accuracy of pixel- and object-based LULC classification models with medium resolution We select the Yangtze River Delta in China as the study area and Landsat-8 OLI data as the main dataset Six types of features, including spectral features, remote sensing multi-indices, topographic features, soil features, distance to the water source, and phenological features, are derived from auxiliary open-source datasets in GEE We then examine the effect of auxiliary datasets on the improvement of the accuracy of seven pixels-based and seven object-based random forest classification models The results show that regardless of the types of auxiliary features, the overall accuracy of the classification can be improved The results further show that the object-based classification achieves higher overall accuracy compared to that obtained by the pixel-based classification The best overall accuracy from the pixel-based (object-based) classification model is 9420% (9601%) The topographic features play the most important role in improving the overall accuracy of classification in the pixel- and object-based models comprising all features Although a higher accuracy is achieved when the object-based method is used with only spectral data, small objects on the ground cannot be monitored However, combined with many types of auxiliary features, the object-based method can identify small objects while also achieving greater accuracy Thus, when applying object-based classification models to mid-resolution remote sensing images, different types of auxiliary features are required Our research results improve the accuracy of LULC classification in the Yangtze River Delta and further provide a benchmark for other regions with large landscape heterogeneity

https://www.mdpi.com/2072-4292/13/3/453/pdf

Accuracy Improvements to Pixel-Based and Object-Based LULC Classification with Auxiliary Datasets from Google Earth Engine

/pdf/determination-of-mucilage-in-the-sea-of-marmara-using-remote-4pfpifwjvf.pdf

Determination of Mucilage in The Sea of Marmara Using Remote Sensing Techniques with Google Earth Engine

Large-scale mapping of date palm trees is vital for their consistent monitoring and sustainable management, considering their substantial commercial, environmental, and cultural value. This study presents an automatic approach for the large-scale mapping of date palm trees from very-high-spatial-resolution (VHSR) unmanned aerial vehicle (UAV) datasets, based on a deep learning approach. A U-Shape convolutional neural network (U-Net), based on a deep residual learning framework, was developed for the semantic segmentation of date palm trees. A comprehensive set of labeled data was established to enable the training and evaluation of the proposed segmentation model and increase its generalization capability. The performance of the proposed approach was compared with those of various state-of-the-art fully convolutional networks (FCNs) with different encoder architectures, including U-Net (based on VGG-16 backbone), pyramid scene parsing network, and two variants of DeepLab V3+. Experimental results showed that the proposed model outperformed other FCNs in the validation and testing datasets. The generalizability evaluation of the proposed approach on a comprehensive and complex testing dataset exhibited higher classification accuracy and showed that date palm trees could be automatically mapped from VHSR UAV images with an F-score, mean intersection over union, precision, and recall of 91%, 85%, 0.91, and 0.92, respectively. The proposed approach provides an efficient deep learning architecture for the automatic mapping of date palm trees from VHSR UAV-based images.

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

This paper presents a novel scene text detection method based on conditional random field (CRF) framework. We estimate the confidence of Maximally Stable Extremal Region (MSER) being text by leveraging convolutional neural network (CNN) to define the unary cost item. In addition, we establish the neighboring interactions for MSERs using four different features including color, shape, stroke and spatial features to define the pairwise cost item. Considering the special layout of texts appearing in natural scene images, we employ context information to recover missing text MSER candidates. Furthermore, text MSERs are grouped into candidate text lines which are verified with shape-specific classifiers by integrating gray and binary features. Experimental results on four public benchmark datasets show that the proposed method achieves the comparable performance.

CRF based text detection for natural scene images using convolutional

. Surface water plays an important role in ecological circulation. Global climate change and urbanization affect the distribution and quality of water. In order to obtain surface water information quickly and accurately, this study uses Google Earth Engine (GEE) as a data processing tool, 309 Landsat 8 series images from 2016 to 2019 are selected to calculate 4 different water indexes, including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Automated Water Extraction Index (AWEIsh) and Multi- Band Water Index (MBWI) to extract surface water in Pearl River Basin. In order to remove the influence of other ground objects, Normalized Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Digital Surface Model (DSM) are combined with the above four water indexes, and threshold segmentation is used to eliminate the influence of vegetation, buildings and mountains. Finally, take the advantage of morphological filtering algorithm to eliminate non-water pixels. The results show that GEE is able to extract surface water in a very short time; AWEIsh has the highest overall accuracy of 94.12%, which is 7.20% higher than the classical NDWI method; There is no significant difference in the width and shape of rivers from 2015 to 2018; The locations of the rivers extracted by the four methods are consistent with the 1 : 100,000 river system basic data of 2015 provided by the Ministry of Water Resources of the People’s Republic of China.

/pdf/delineation-water-of-pearl-river-basin-using-landsat-images-2uz90g2k9i.pdf

Delineation water of pearl river basin using landsat images from google earth engine

. The dynamic change of urban thermal environment caused by the change of land use type has become one of the important problems of urban ecological environment protection. In Guilin city as research area, based on the Google Earth Engine (GEE), the random forest algorithm was used to classify the land use classification of Landsat remote sensing images in 2010, 2014 and 2018, and the mono-window algorithm was used to calculate the surface temperature. The surface vegetation was solved according to the NDVI pixel binary model. Coverage, and finally dynamic statistics and comparative analysis of land use, vegetation cover and surface temperature. The main results as follows. (1) From 2010 to 2018, the average temperature in the main urban area of Guilin is on the rise (increased by 1.29 °C), and the temperature zones in each class are converted from low temperature zone, lower temperature zone and medium temperature zone to higher temperature zone and high temperature zone. (2) Lower temperature zone and the low temperature zone is mainly distributed in vegetation and water body coverage areas, while the medium temperature zone, higher temperature zone and the high temperature zone are mainly distributed in construction land and unused land cover area. (3) High vegetation cover area in 2014–2018 (reduced by 31.34%) The main reason for the sharp decline is the substantial increase in the area of construction land (expansion 30.19%). (4) GEE-based random forest algorithm Land use classification had higher classification accuracy (more than 80% in all three periods). The results can provide scientific basis for improving urban thermal environment and scientific reference for the development strategy of Guilin city.

/pdf/quantitative-remote-sensing-analysis-of-thermal-environment-4dctpn9m9k.pdf

Quantitative Remote Sensing Analysis of Thermal Environment Changes in the Main Urban Area of Guilin Based on Gee

. This study takes Guangxi Huixian National Wetland Park as the research area, and uses the UAV image and ground measured tag data as the data source. The SegNet model is used to extract the wetland vegetation information in the study area, further verification multiple classification SegNet model and fusion multiple SegNet model of single/double classification precision of the two ways of extracting karst wetland vegetation information. The experimental results show that the Kappa coefficient of the multi-segmented SegNet model is 0.68, while the multi-class SegNet model has a classification effect of 0.59. The classification effect of the karst wetland vegetation information extracted by multiple single/double-class SegNet models is more than the multi-classification. The SegNet model has high precision.

https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W10/375/2020/isprs-archives-XLII-3-W10-375-2020.pdf

Segnet-based extraction of wetland vegetation information from uav images

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Delineation water of pearl river basin using landsat images from google earth engine

Quantitative Remote Sensing Analysis of Thermal Environment Changes in the Main Urban Area of Guilin Based on Gee

Segnet-based extraction of wetland vegetation information from uav images