Identification of Village Building via Google Earth Images and Supervised Machine Learning Methods

Guo, Zhiling; Shao, Xiaowei; Xu, Yang; Miyazaki, Hiroyuki; Ohira, Wataru; Shibasaki, Ryosuke

doi:10.3390/rs8040271

Cited by 61 publications

(43 citation statements)

References 37 publications

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“…However, GE imagery has been manipulated to reduce spectral information and improve the appearance when it is displayed on the surface of the virtual globe. Therefore, GE imagery only has three bands (red, green, and blue), even though the original imagery had more bands [26,31,32]. In this paper, "GE imagery" only represents the improved RGB imagery displayed on the surface of Google Earth, rather than its original imagery provided by commercial image operators.…”

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

Monitoring the Invasion of Spartina alterniflora Using Multi-source High-resolution Imagery in the Zhangjiang Estuary, China

Liu

et al. 2017

Remote Sensing

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Spartina alterniflora (S. alterniflora) is one of the most harmful invasive plants in China. Google Earth (GE), as a free software, hosts high-resolution imagery for many areas of the world. To explore the use of GE imagery for monitoring S. alterniflora invasion and developing an understanding of the invasion process of S. alterniflora in the Zhangjiang Estuary, the object-oriented method and visual interpretation were applied to GE, SPOT-5, and Gaofen-1 (GF-1) images. In addition, landscape metrics of S. alterniflora patches adjacent to mangrove forests were calculated and mangrove gaps were recorded by checking whether S. alterniflora exists. The results showed that from 2003-2015, the areal extent of S. alterniflora in the Zhangjiang Estuary increased from 57.94 ha to 116.11 ha, which was mainly converted from mudflats and moved seaward significantly. Analyses of the S. alterniflora expansion patterns in the six subzones indicated that the expansion trends varied with different environmental circumstances and human activities. Land reclamation, mangrove replantation, and mudflat aquaculture caused significant losses of S. alterniflora. The number of invaded gaps increased and S. alterniflora patches adjacent to mangrove forests became much larger and more aggregated during 2003-2015 (the class area increased from 12.13 ha to 49.76 ha and the aggregation index increased from 91.15 to 94.65). We thus concluded that S. alterniflora invasion in the Zhangjiang Estuary had seriously increased and that measures should be taken considering the characteristics shown in different subzones. This study provides an example of applying GE imagery to monitor invasive plants and illustrates that this approach can aid in the development of governmental policies employed to control S. alterniflora invasion.

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Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

Monitoring the Invasion of Spartina alterniflora Using Multi-source High-resolution Imagery in the Zhangjiang Estuary, China

Liu

et al. 2017

Remote Sensing

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“…Therefore, our study findings do not necessarily apply to these communities. Recent advances in rural remote population density mapping that utilize roof reflectance data obtained from remote sensing imagery (Guo et al, 2016; Varshney et al, 2015) demonstrate a promising alternative to the Open Street Map polygon method.…”

Section: Discussionmentioning

confidence: 99%

Indicators of improved water access in the context of schistosomiasis transmission in rural Eastern Region, Ghana

Kulinkina

Kosinski

Plummer³

et al. 2017

Science of The Total Environment

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Populations with poor access to water, sanitation and hygiene (WASH) infrastructure are disproportionately affected by the neglected tropical diseases (NTDs). As a result, WASH has gained increasing prominence in integrated control and elimination of NTDs, including schistosomiasis. In order to identify underserved populations, relevant measures of access to WASH infrastructure at sub–national or local levels are needed. We conducted a field survey of all public water sources in 74 rural communities in the Eastern Region of Ghana and computed indicators of water access using two methods: one based on the design capacity and another on the spatial distribution of water sources. The spatial method was applied to improved and surface water sources. According to the spatial method, improved water sources in the study area were well–distributed within communities with 95% (CI95%: 91, 98) of the population having access within 500 m when all, and 87% (CI95%: 81, 93) when only functional water sources were considered. According to the design capacity–based method, indicator values were lower: 63% (CI95%: 57, 69) for all and 49% (CI95%: 43, 55) for only functional sources. Surface water access was substantial with 62% (CI95%: 54, 71) of the population located within 500 m of a perennial surface water source. A negative relationship was observed between functional improved water access and surface water access within 300 m. In this context, perceived water quality of the improved sources was also important, with a 17% increase in surface water access in towns with one reported water quality problem as compared to towns with no problems. Our study offers a potential methodology to use water point mapping data to identify communities in need of improved water access to achieve schistosomiasis risk reduction.

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“…However, these studies are just the beginning of CNN-based RS image classification research. RS image classification is still facing unprecedented and significant challenges, and a number of issues are in need to be thought and investigated in depth, which we summarize as follows: Buildings (Li, Zhang, et al,2016;Qu, Ren, Liu, & Li, 2017;Guo et al, 2016);…”

Section: Challenges and Conclusionmentioning

confidence: 99%

A survey of remote sensing image classification based on CNNs

et al. 2019

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With the development of earth observation technologies, the acquired remote sensing images are increasing dramatically, and a new era of big data in remote sensing is coming. How to effectively mine these massive volumes of remote sensing data are new challenges. Deep learning provides a new approach for analyzing these remote sensing data. As one of the deep learning models, convolutional neural networks (CNNs) can directly extract features from massive amounts of imagery data and is good at exploiting semantic features of imagery data. CNNs have achieved remarkable success in computer vision. In recent years, quite a few researchers have studied remote sensing image classification using CNNs, and CNNs can be applied to realize rapid, economical and accurate analysis and feature extraction from remote sensing data. This paper aims to provide a survey of the current state-of-the-art application of CNN-based deep learning in remote sensing image classification. We first briefly introduce the principles and characteristics of CNNs. We then survey developments and structural improvements on CNN models that make CNNs more suitable for remote sensing image classification, available datasets for remote sensing image classification, and data augmentation techniques. Then, three typical CNN application cases in remote sensing image classification: scene classification, object detection and object segmentation are presented. We also discuss the problems and challenges of CNN-based remote sensing image classification, and propose corresponding measures and suggestions. We hope that the survey can facilitate the advancement of remote sensing image classification research and help remote-sensing scientists to tackle classification tasks with the state-of-art deep learning algorithms and techniques.

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Identification of Village Building via Google Earth Images and Supervised Machine Learning Methods

Cited by 61 publications

References 37 publications

Monitoring the Invasion of Spartina alterniflora Using Multi-source High-resolution Imagery in the Zhangjiang Estuary, China

Monitoring the Invasion of Spartina alterniflora Using Multi-source High-resolution Imagery in the Zhangjiang Estuary, China

Indicators of improved water access in the context of schistosomiasis transmission in rural Eastern Region, Ghana

A survey of remote sensing image classification based on CNNs

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