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

Yang, Liping; Driscol, Joshua; Sarigai, Sarigai; Wu, Qiusheng; Lippitt, Christopher D.; Morgan, Melinda

doi:10.3390/s22062416

Cited by 39 publications

(17 citation statements)

References 162 publications

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“…For the most commonly used evaluation metrics in the context of combined GEE, AI, and RS literatures, see Appendix C of our recent paper [264] at https://doi.org/10.3390/s2 2062416 (accessed on 1 May 2022).…”

Section: Appendix a The Accompanying Interactive Web App Tool For The...mentioning

confidence: 99%

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: Appendix a The Accompanying Interactive Web App Tool For The...mentioning

confidence: 99%

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

et al. 2022

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“…The NDWI helps to identify water surface effectively; however, it is sensitive to built-up areas [64]. The reason is that water, and shadows from buildings and clouds have similar properties, which makes it difficult to differentiate between water and non-water surfaces [49]. To address this limitation, Xu [36] developed the MNDWI by replacing the NIR that was previously used in NDWI by the shortwave infrared (SWIR) band.…”

Section: Water Indicesmentioning

confidence: 99%

“…These techniques were developed to improve the accuracy of water body detection, especially small water bodies in complex terrains, and to overcome the limitations of spectral resolution in high-resolution images (e.g., Ikonos and Quickbird). According to Yang et al [49], convolutional neural network-based models are the most frequently used in water body detection. Machine learning methods were found to outperform the water index-based method in specific areas and terrains [42][43][44][45][46].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Riverbank Erosion Hotspots along the Mekong River in Cambodia Using Remote Sensing and Hazard Exposure Mapping

Tha

Piman

Bhatpuria

et al. 2022

Water

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The large-scale monitoring of riverbank erosion is challenging because of human, equipment, and financial limitations, particularly in developing countries. This study aims to detect riverbank erosion and identify riverbank erosion hotspots along the Mekong River in Cambodia. A riverbank erosion rate map was developed using satellite images from Landsat 5, 7, and 8 (1990–2020) using the modified normalized difference water index (MNDWI) at a resolution of 30 m and Sentinel-2 (2016–2021) using the normalized difference water index (NDWI) at a resolution of 10 m. Detecting riverbanks in satellite images using a water index depends greatly on image resolution and water threshold. The riverbank lines were validated using Google Earth images. In the data used in December 2017, the root mean square error (RMSE) of Sentinel-2 was 6.00 m, while the RMSE of Landsat was 6.04 m. In the data used in January 2019, the RMSE of Sentinel-2 was 4.12 m, while the RMSE of Landsat was 5.90 m. The hotspots were identified by overlaying the riverbank erosion rate map and the exposure map of population density and land cover. Field surveys and interviews were conducted to verify riverbank erosion hotspots in the Ruessei Srok and Kaoh Soutin communes. The results showed that within the last 30 years (1990–2020), the riverbank eroded more than 1 km in a direction perpendicular to the river in some segments of the Mekong River in Cambodia. The highest average annual erosion rate was in the Ruessei Srok Commune in Kampong Cham Province, at approximately 43 m/yr. Most eroded areas were farmland and rural residential areas. The riverbank hotspots are situated mainly in the lower part of the Mekong River, where the population is dense, and the erosion rate is high. Riverbank erosion hotspots with a very high impact level and ongoing active erosion include the Peam Kaoh Sna, Kampong Reab, Kaoh Soutin, and Ruessei Srok communes in Kampong Cham Province. This study provides an efficient tool for using satellite images to identify riverbank erosion hotpots in a large river basin. The riverbank erosion hotspot map is essential for decision-makers to prioritize interventions to reduce the risk of riverbank erosion and to improve the livelihood of the people residing along the Mekong River.

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“…Semantic segmentation is a critical technology in remote sensing image interpretation, as it enables pixel-by-pixel classification of objects in images. Due to its advanced classification capabilities, semantic segmentation has found wide application in many fields, such as landslide monitoring, 1 , 2 plant disease monitoring, 3 and water quality monitoring, 4 in the field of disaster monitoring. In urban planning, it has been utilized for road detection, 5 building change detection, 6 illegal building detection, 7 3D scene reconstruction 8 , 9 .…”

Section: Introductionmentioning

confidence: 99%

Semantic segmentation of remote sensing images based on dilated convolution and spatial-channel attention mechanism

Bao

Chang

et al. 2023

J. Appl. Rem. Sens.

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The rich context information and multiscale ground information in remote sensing images are crucial to improving the semantic segmentation accuracy. Therefore, we propose a remote sensing image semantic segmentation method that integrates multilevel spatial channel attention and multi-scale dilated convolution, effectively addressing the issue of poor segmentation performance of small target objects in remote sensing images. This method builds a multilevel characteristic fusion structure, combining deep-level semantic characteristics with the details of the shallow levels to generate multiscale feature diagrams. Then, we introduce the dilated convolution of the series combination in each layer of the atrous spatial pyramid pooling structure to reduce the loss of small target information. Finally, using convolutional conditional random field to describe the context information on the space and edges to improve the model's ability to extract details. We prove the effectiveness of the model on the three public datasets. From the quantitative point of view, we mainly evaluate the four indicators of the model's F1 score, overall accuracy (OA), Intersection over Union (IoU), and Mean Intersection over Union (MIoU). On GID dataset, F1 score, OA, and MIoU reach 87.27, 87.80, and 77.70, respectively, superior to most mainstream semantic segmentation networks.

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

Cited by 39 publications

References 162 publications

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

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

Assessment of Riverbank Erosion Hotspots along the Mekong River in Cambodia Using Remote Sensing and Hazard Exposure Mapping

Semantic segmentation of remote sensing images based on dilated convolution and spatial-channel attention mechanism

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