Modeling urban growth sustainability in the cloud by augmenting Google Earth Engine (GEE)

Liang, Jianyuan; Xie, Yichun; Sha, Zongyao; Zhou, Alicia Y.

doi:10.1016/j.compenvurbsys.2020.101542

Cited by 40 publications

(20 citation statements)

References 32 publications

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“…While determining ecosystem service values is complicated (many disciplines, many opinions, etc. ), GEE was used in [94] to illustrate a processing workflow for how LULC classes can be used to compute more complex ecosystem service values.…”

Section: Land Cover Classificationmentioning

confidence: 99%

“…In the future, one area of research that should be given much more focus is on implementing more complex production applications built on top of GEE that make use of modeling. For example, after mapping out urban areas using ML classification techniques, the authors in [94] then went further and implemented an ecosystem service value model on the platform. This is a creative way to use the available parallel processing capabilities of GEE and is an under-researched application area.…”

Section: Beyond Ml: Modeling In Geementioning

confidence: 99%

“…It will be easier to avoid errors like these if there are more open-source Python/R libraries that make it easier to connect GEE and local computers. GEE does provide JavaScript and Python APIs, but more work needs to be done to incorporate the wealth of well-tested AI algorithms already available in Python into these APIs [94].…”

Section: Open-source Gee-ai Library Developmentmentioning

confidence: 99%

“…Lack of data [32,46,54,67,75,94,120,126,127,[160][161][162]183,184,193,215,221]: This related to both a lack of field observations and curated RS datasets.…”

mentioning

confidence: 99%

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Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

et al. 2022

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Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning). Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. Google Earth Engine (GEE) provides a scalable, cloud-based, geospatial retrieval and processing platform. GEE also provides access to the vast majority of freely available, public, multi-temporal RS data and offers free cloud-based computational power for geospatial data analysis. Artificial intelligence (AI) methods are a critical enabling technology to automating the interpretation of RS imagery, particularly on object-based domains, so the integration of AI methods into GEE represents a promising path towards operationalizing automated RS-based monitoring programs. In this article, we provide a systematic review of relevant literature to identify recent research that incorporates AI methods in GEE. We then discuss some of the major challenges of integrating GEE and AI and identify several priorities for future research. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review.

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Section: Land Cover Classificationmentioning

confidence: 99%

Section: Beyond Ml: Modeling In Geementioning

confidence: 99%

Section: Open-source Gee-ai Library Developmentmentioning

confidence: 99%

“…Lack of data [32,46,54,67,75,94,120,126,127,[160][161][162]183,184,193,215,221]: This related to both a lack of field observations and curated RS datasets.…”

mentioning

confidence: 99%

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Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

et al. 2022

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“…Google Earth Engine (GEE) has been increasingly used in environmental and urban studies due to its cloud-based geospatial processing capability and accessibility to a large collection of geospatial datasets [63]. GEE allows to upload local (user's) data to the user repository and then make integrated analysis by taking both the available platform data resources and analytical tools.…”

Section: Computation Platformmentioning

confidence: 99%

Assessing Urban Greenness Fragmentation and Analysis of Its Associated Factors: A Case Study in Wuhan Metropolitan Area, China

Fang

Sha

Wang

et al. 2021

IJGI

Self Cite

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Green vegetation plays a vital role in urban ecosystem services. Rapid urbanization often tends to induce urban vegetation cover fragmentation (UVCF) in cities and suburbs. Mapping the changes in the structure (aggregation) and abundance of urban vegetation cover helps to make improved policies for sustainable urban development. In this paper, a new distance-based landscape indicator to UVCF, Frag, was proposed first. Unlike many other landscape indicators, Frag measures UVCF by considering simultaneously both the structure and abundance of vegetation cover at local scales, and thus provides a more comprehensive perspective in understanding the spatial distribution patterns in urban greenness cover. As a case study, the urban greenness fragmentation indicated by Frag was demonstrated in Wuhan metropolitan area (WMA), China in 2015 and 2020. Support vector machine (SVM) was then designed to examine the impact on the Frag changes from the associated factors, including urbanization and terrain characteristics (elevation and slope). The Frag changes were mapped at different scales and modeled by SVM from the selected factors, which reasonably explained the Frag changes. Sensitivity analysis for the SVM model revealed that urbanization showed the most dominant factor for the Frag changes, followed by terrain elevation and slope. We conclude that Frag is an effective scale-dependent indicator to UVCF that can reflect changes in the structure and abundance of urban vegetation cover, and that modeling the impact of the associated factors on UVCF via the Frag indicator can provide essential information for urban planners.

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Theoretical Foundations of Total Socioenvironmental System (TSES)

Xie

2023

Total Socioenvironmental Systems

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Modeling urban growth sustainability in the cloud by augmenting Google Earth Engine (GEE)

Cited by 40 publications

References 32 publications

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

Assessing Urban Greenness Fragmentation and Analysis of Its Associated Factors: A Case Study in Wuhan Metropolitan Area, China

Theoretical Foundations of Total Socioenvironmental System (TSES)

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