Retrieving urban areas on Google Earth images: application to towns of West Africa

Méring, Catherine; Baro, Johanna; Upegui, Erika

doi:10.1080/01431161.2010.512311

Cited by 42 publications

(21 citation statements)

References 5 publications

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“…The high spatial resolution images released from GE, as a free and open data source, have provided great supports for the traditional land use/cover mapping [17,18]. They have been either treated as ancillary data to collect the training or testing samples for land use/cover classification and validation or used as a visualization tool for land use/cover maps [19,20].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

et al. 2013

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Google Earth (GE) releases free images in high spatial resolution that may provide some potential for regional land use/cover mapping, especially for those regions with high heterogeneous landscapes. In order to test such practicability, the GE imagery was selected for a case study in Wuhan City to perform an object-based land use/cover classification. The classification accuracy was assessed by using 570 validation points generated by a random sampling scheme and compared with a parallel classification of QuickBird (QB) imagery based on an object-based classification method. The results showed that GE has an overall classification accuracy of 78.07%, which is slightly lower than that of QB. No significant difference was found between these two classification results by the adoption of Z-test, which strongly proved the potentials of GE in land use/cover mapping. Moreover, GE has different discriminating capacity for specific land use/cover types. It possesses some advantages for mapping those types with good spatial characteristics in terms of geometric, shape and context. The object-based method is recommended for imagery classification when using GE imagery for mapping land use/cover. However, GE has some limitations for those types classified by using only spectral characteristics largely due to its poor spectral characteristics. OPEN ACCESSRemote Sens. 2013, 5 6027

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Section: Introductionmentioning

confidence: 99%

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

et al. 2013

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“…Google imagery has already been used profitably in previous studies (Chang et al 2009;Mering et al 2010;Ploton et al 2012;Taylor and Lovell 2012): it has the great advantage of providing free recent very high resolution true color images. For the study area, photointerpretation was performed on Digital Globe 50 cm resolution scenes acquired on November 9 2014.…”

Section: Satellite Imagesmentioning

confidence: 99%

A Simplified Hydrological Method for Flood Risk Assessment at Sub-basin Level in Niger

Fiorillo

Tarchiani

2017

Renewing Local Planning to Face Climate Change in the Tropics

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Flood events are increasing year by year in the Sahel, mainly caused by climate and land use changes. New strategies and tools are necessary to optimize flooding risk reduction plans. This paper presents a new hydrological method (FREM, Flooding Risk Evaluation Method), based on the curve number runoff estimation. The method can be adopted for small-medium basins and is based on the integration of remote sensing techniques with field surveys and participatory mapping. It consists of preliminary identification of the areas and sub-basins that most contribute to the flood risk; scenarios can then be developed in order to: (i) optimize the placement of traditional water retention structures in the elementary sub-basins that contribute most to the overall risk, (ii) assess the contribution of each hydraulic structure to reduce the total risk, (iii) give a priority ranking to these structures identifying those most urgent. The main advantages of this method are that it is easy to use and can be implemented using free available land cover, soil and morphology data and open-source GIS (Geographic Information System) software. A case study for the Ouro Gueladjo basin (Tillabery Region, Niger) is presented.

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“…In recent years, Google Earth imagery has been quickly employed in land use mapping and urban area retrieval (Mering et al 2010). Google Earth imagery with high spatial resolution is an open data source free to the public.…”

Section: Introductionmentioning

confidence: 99%

Population Estimation in Singapore Based on Remote Sensing and Open Data

Guo

Cao

Wang

2017

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

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ABSTRACT:Population estimation statistics are widely used in government, commercial and educational sectors for a variety of purposes. With growing emphases on real-time and detailed population information, data users nowadays have switched from traditional census data to more technology-based data source such as LiDAR point cloud and HighResolution Satellite Imagery. Nevertheless, such data are costly and periodically unavailable. In this paper, the authors use West Coast District, Singapore as a case study to investigate the applicability and effectiveness of using satellite image from Google Earth for extraction of building footprint and population estimation. At the same time, volunteered geographic information (VGI) is also utilized as ancillary data for building footprint extraction. Open data such as Open Street Map（OSM）could be employed to enhance the extraction process. In view of challenges in building shadow extraction, this paper discusses several methods including buffer, mask and shape index to improve accuracy. It also illustrates population estimation methods based on building height and number of floor estimates. The results show that the accuracy level of housing unit method on population estimation can reach 92.5%, which is remarkably accurate. This paper thus provides insights into techniques for building extraction and fine-scale population estimation, which will benefit users such as urban planners in terms of policymaking and urban planning of Singapore.

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Retrieving urban areas on Google Earth images: application to towns of West Africa

Cited by 42 publications

References 5 publications

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

A Simplified Hydrological Method for Flood Risk Assessment at Sub-basin Level in Niger

Population Estimation in Singapore Based on Remote Sensing and Open Data

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