The migration of training samples towards dynamic global land cover mapping

Huang, Huabing; Wang, Jie; Liu, Caixia; Lu, Liang; Li, Cong-Cong; Gong, Peng

doi:10.1016/j.isprsjprs.2020.01.010

Cited by 93 publications

(66 citation statements)

References 28 publications

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“…Therefore, to ensure high-quality results, it is inevitable to develop and enhance the existing image processing algorithms within GEE protocols. In this regard, researchers have employed GEE to develop various efficient and useful image processing algorithms, such as cloud masking [12], [149], data selection and enhancement [13], [150], image-based sensor calibration [151], [152], and training sample migration [153]. For instance, [150] introduced weighted Whittaker with a dynamic parameter (wWHd) de-noising method within GEE to reconstruct the vegetation phenology based on 500m MODIS EVI products.…”

Section: H Image Processingmentioning

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

585

216

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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.

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Section: H Image Processingmentioning

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

585

216

View full text Add to dashboard Cite

show abstract

“…The quantity and quality of training data play an essential role in the production of LULC maps. Yet, collecting sufficient and precise training data requires considerable efforts, especially at large scales and multiple periods 27 . Several attempts have been proposed methods that allow to collect cost-effectively high-quality training data.…”

Section: Introductionmentioning

confidence: 99%

“…While the authors applied a bi-temporal spectral measurement to decrease the bias of extracted training data, the accuracy of these data may not be ensured due to the inherent classification errors of the previous maps 29 . To enhance the effectiveness of training data collection, Huang et al (2020) used spectral similarity and distance indicators to detect the changed and unchanged training sites, and thus kept the unchanged ones as migrated training data 27 . The measurement was applied for the availability of Landsat TM images.…”

Section: Introductionmentioning

confidence: 99%

First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam

Phan

Trung

Truong

et al. 2021

Sci Rep

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Extensive studies have highlighted a need for frequently consistent land cover information for interdisciplinary studies. This paper proposes a comprehensive framework for the automatic production of the first Vietnam-wide annual land use/land cover (LULC) data sets (VLUCDs) from 1990 to 2020, using available remotely sensed and inventory data. Classification accuracies ranged from 85.7 ± 1.3 to 92.0 ± 1.2% with the primary dominant LULC and 77.6 ± 1.2% to 84.7 ± 1.1% with the secondary dominant LULC. This confirmed the potential of the proposed framework for systematically long-term monitoring LULC in Vietnam. Results reveal that despite slight recoveries in 2000 and 2010, the net loss of forests (19,940 km2) mainly transformed to croplands over 30 years. Meanwhile, productive croplands were converted to urban areas, which increased approximately ten times. A threefold increase in aquaculture was a major driver of the wetland loss (1914 km2). The spatial–temporal changes varied, but the most dynamic regions were the western north, the southern centre, and the south. These findings can provide evidence-based information on formulating and implementing coherent land management policies. The explicitly spatio-temporal VLUCDs can be benchmarks for global LULC validation, and utilized for a variety of applications in the research of environmental changes towards the Sustainable Development Goals.

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“…Therefore, we need efficient solutions to generate training samples to support the production of timely and reliable crop information. Transfer learning (Tuia et al, 2011), crowdsourcing initiatives (Fritz et al 2009), and utilization of existing inventories to guide the labeling of the new training samples (Huang et al 2020) are promising solutions to address this challenge.…”

Section: Resultsmentioning

confidence: 99%

Dynamic Time Warping for Crops Mapping

Belgiu

Zhou

Marshall

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Dynamic Time Warping (DTW) has been successfully used for crops mapping due to its capability to achieve good classification results when a reduced number of training samples and irregular satellite image time series is available. Despite its recognized advantages, DTW does not account for the duration and seasonality of crops and local differences when assessing the similarity between two temporal sequences. In this study, we implemented a Weighted Derivative modification of DTW (WDDTW) and compared it with DTW and Time Weighted Dynamic Time Warping (TWDTW) for crops mapping. We show that WDDTW outperformed DTW achieving an overall accuracy of 67 %, whereas DTW obtained an accuracy of 57%. Yet, TWDTW performed better than both methods obtaining an accuracy of 88%.

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The migration of training samples towards dynamic global land cover mapping

Cited by 93 publications

References 28 publications

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

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

First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam

Dynamic Time Warping for Crops Mapping

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