Next Generation Mapping: Combining Deep Learning, Cloud Computing, and Big Remote Sensing Data

Parente, Leandro; Taquary, Evandro Carrijo; Silva, Ana Paula; Souza, Carlos M.; Ferreira, Laerte Guimarães

doi:10.3390/rs11232881

Cited by 53 publications

(28 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As demonstrated by the resulting clean surface reflectance mosaics (Figure 3) and accurate reflectance spectral data for benthic targets (Figure 4), our GEE clean-water mosaic provided reliable Sentinel-2 imagery for bathymetric modeling. All processing was conducted in the GEE platform which took advantage of powerful cloud computation capabilities [47].…”

Section: Discussionmentioning

confidence: 99%

Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine

Knapp

Lyons

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Global shallow water bathymetry maps offer critical information to inform activities such as scientific research, environment protection, and marine transportation. Methods that employ satellite-based bathymetric modeling provide an alternative to conventional shipborne measurements, offering high spatial resolution combined with extensive coverage. We developed an automated bathymetry mapping approach based on the Sentinel-2 surface reflectance dataset in Google Earth Engine. We created a new method for generating a clean-water mosaic and a tailored automatic bathymetric estimation algorithm. We then evaluated the performance of the models at six globally diverse sites (Heron Island, Australia; West Coast of Hawaiʻi Island, Hawaiʻi; Saona Island, Dominican Republic; Punta Cana, Dominican Republic; St. Croix, United States Virgin Islands; and The Grenadines) using 113,520 field bathymetry sampling points. Our approach derived accurate bathymetry maps in shallow waters, with Root Mean Square Error (RMSE) values ranging from 1.2 to 1.9 m. This automatic, efficient, and robust method was applied to map shallow water bathymetry at the global scale, especially in areas which have high biodiversity (i.e., coral reefs).

show abstract

Section: Discussionmentioning

confidence: 99%

Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine

Knapp

Lyons

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…For instance, TensorFlow is a better option in the deep learning section, for which more complex models, larger training datasets, more input properties, or longer training times are required [47], [48]. TensorFlow models are developed, trained and deployed outside EE [49]. For easier interoperability, the EE API provides methods to import/export data in TFRecord format [47].…”

Section: ) Functionsmentioning

confidence: 99%

“…In particular, the existence of several vegetation indices in GEE allows conducting vegetation studies in efficient and quick manners. GEE has been widely used for vegetation mapping [57], [58], vegetation dynamics monitoring [59], [60], deforestation [61], [62], vegetation and forest expansion [63], [64], forest health monitoring [65], [66], forest mapping [67], [68], pasture monitoring [49], [69], and rangeland assessment [70], [71]. For instance, the full archive of the Landsat imagery was processed within GEE to map the vegetation dynamics from 1988 to 2017 in Queensland, Australia [59].…”

Section: A Vegetationmentioning

confidence: 99%

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

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

660

216

View full text Add to dashboard Cite

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.

show abstract

“…The first group of methods does not explicitly consider spatial or temporal dimensions; these models treat time series as a vector in a high-dimensional feature space. From this class of models, sits includes random forests [39], support vector machines [40], extreme gradient boosting [41], and multi-layer perceptrons [42]. The authors have used these methods with success for classifying large areas [23,43,44].…”

Section: Training Machine Learning Modelsmentioning

confidence: 99%

Satellite Image Time Series Analysis for Big Earth Observation Data

et al. 2021

View full text Add to dashboard Cite

The development of analytical software for big Earth observation data faces several challenges. Designers need to balance between conflicting factors. Solutions that are efficient for specific hardware architectures can not be used in other environments. Packages that work on generic hardware and open standards will not have the same performance as dedicated solutions. Software that assumes that its users are computer programmers are flexible but may be difficult to learn for a wide audience. This paper describes sits, an open-source R package for satellite image time series analysis using machine learning. To allow experts to use satellite imagery to the fullest extent, sits adopts a time-first, space-later approach. It supports the complete cycle of data analysis for land classification. Its API provides a simple but powerful set of functions. The software works in different cloud computing environments. Satellite image time series are input to machine learning classifiers, and the results are post-processed using spatial smoothing. Since machine learning methods need accurate training data, sits includes methods for quality assessment of training samples. The software also provides methods for validation and accuracy measurement. The package thus comprises a production environment for big EO data analysis. We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome, one of the world’s fast moving agricultural frontiers for the year 2018.

show abstract

Next Generation Mapping: Combining Deep Learning, Cloud Computing, and Big Remote Sensing Data

Cited by 53 publications

References 54 publications

Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine

Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine

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

Satellite Image Time Series Analysis for Big Earth Observation Data

Contact Info

Product

Resources

About