Deep learning approach for Sentinel-1 surface water mapping leveraging Google Earth Engine

Mayer, Timothy; Poortinga, Ate; Bhandari, Biplov; Nicolau, Andréa Puzzi; Markert, Kel; Thwal, Nyein Soe; Markert, Amanda; Haag, Arjen; Kilbride, John; Chishtie, Farrukh; Wadhwa, Amit; Clinton, Nicholas; Saah, David

doi:10.1016/j.ophoto.2021.100005

Cited by 40 publications

(23 citation statements)

References 71 publications

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“…However, as compute becomes publicly available in cloud-based platforms like GEE, obtaining large amounts of labeled training data remains a key bottleneck to using DL models. One novel way to make the data labeling process less time-and resource-intensive was illustrated in [156], where the authors used current water maps and a segmentation algorithm to automatically collect data labels from Sentinel-1 imagery. These data are then used to train variations of U-Net in an offline environment.…”

Section: Web Interface Tools To Support ML Explorationmentioning

confidence: 99%

“…Having digitized boundaries for individual ecological features is the first step to monitoring them and measuring how they have changed over time. While the authors in [156] demonstrated a novel way of creating data labels via segmentation algorithms, this is still semantic segmentation and the data labels required additional verification. The authors in [233], however, showed this is possible.…”

Section: Vectorizing Data Boundariesmentioning

confidence: 99%

“…Still, the MSCNN model had issues classifying small/thin water bodies and water scenes with mixed pixel classes. One way to make the data labeling process less time-and resource-intensive for DL was illustrated in [156], where the authors used current water maps and a segmentation algorithm to automatically collect data labels from Sentinel-1 imagery. This data were then used to train variations of U-Net in an offline environment.…”

Section: Abbreviationsmentioning

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: Web Interface Tools To Support ML Explorationmentioning

confidence: 99%

Section: Vectorizing Data Boundariesmentioning

confidence: 99%

Section: Abbreviationsmentioning

confidence: 99%

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

et al. 2022

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“…Sentinel-1 (S-1), an active Synthetic Aperture Radar (SAR) C-band sensor, was used in this framework. This sensor has the ability to collect data in distinct polarizations and operate in multiple acquisition modes at various ground sampling distances (GSD) (Mayer et al, 2021). The S-1 provides Level-1 Interferometric Wide Swath (IW) mode and Ground Range Detected (GRD) data set with a spatial resolution of 10 m (Potin et al, 2012).…”

Section: Sentinel-1mentioning

confidence: 99%

Employing the agricultural classification and estimation service (ACES) for mapping smallholder rice farms in Bhutan

Mayer

Bhandari

Martínez

et al. 2023

Front. Environ. Sci.

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Creating annual crop type maps for enabling improved food security decision making has remained a challenge in Bhutan. This is in part due to the level of effort required for data collection, technical model development, and reliability of an on-the-ground application. Through focusing on advancing Science, Technology, Engineering, and Mathematics (STEM) in Bhutan, an effort to co-develop a geospatial application known as the Agricultural Classification and Estimation Service (ACES) was created. This paper focuses on the co-development of an Earth observation informed climate smart crop type framework which incorporates both modeling and training sample collection. The ACES web application and subsequent ACES modeling software package enables stakeholders to more readily use Earth observation into their decision making process. Additionally, this paper offers a transparent and replicable approach for addressing and combating remote sensing limitations due to topography and cloud cover, a common problem in Bhutan. Lastly, this approach resulted in a Random Forest “LTE 555” model, from a set of 3,600 possible models, with an overall test Accuracy of 85% and F-1 Score of .88 for 2020. The model was independently validated resulting in an independent accuracy of 83% and F-1 Score of .45 for 2020. The insight into the model perturbation via hyperparameter tuning and input features is key for future practitioners.

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“…However, online DL functionality is still not supported on GEE. To the best of our knowledge, the only piece of research integration of the Google AI platform with GEE is performed in [ 119 ]; however, as the authors reported, “data migration and computational demands are among the main present constraints in deploying these technologies in an operational setting”. Thus, the ideal solution is to develop DL models directly on the GEE platform.…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing

Yang

Driscol

Sarigai

et al. 2022

Sensors

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Water features (e.g., water quantity and water quality) are one of the most important environmental factors essential to improving climate-change resilience. Remote sensing (RS) technologies empowered by artificial intelligence (AI) have become one of the most demanded strategies to automating water information extraction and thus intelligent monitoring. In this article, we provide a systematic review of the literature that incorporates artificial intelligence and computer vision methods in the water resources sector with a focus on intelligent water body extraction and water quality detection and monitoring through remote sensing. Based on this review, the main challenges of leveraging AI and RS for intelligent water information extraction are discussed, and research priorities are identified. An interactive web application designed to allow readers to intuitively and dynamically review the relevant literature was also developed.

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Deep learning approach for Sentinel-1 surface water mapping leveraging Google Earth Engine

Cited by 40 publications

References 71 publications

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

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

Employing the agricultural classification and estimation service (ACES) for mapping smallholder rice farms in Bhutan

Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing

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