Expert system classification of urban land use/cover for Delhi, India

Wentz, Elizabeth A.; Nelson, David M.; Rahman, Atiqur; Stefanov, William L.; Roy, Shoursaseni Sen

doi:10.1080/01431160801905497

Cited by 69 publications

(22 citation statements)

References 71 publications

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“…In this paper, an expert system approach is chosen because of its maturity and because it has been used successfully at solving other problems (Holzmann et al, 1999;Rearden et al, 2007;Wentz et al, 2008). …”

Section: Concept Of the Classificationmentioning

confidence: 99%

Transportation mode-based segmentation and classification of movement trajectories

Biljecki

Ledoux

Oosterom

2013

International Journal of Geographical Information Science

133

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The knowledge of the transportation mode used by humans (e.g. bicycle, on foot, car, and train) is critical for travel behaviour research, transport planning and traffic management. Nowadays, new technologies such as the GPS have replaced traditional survey methods (paper diaries, telephone) since they are more accurate and problems such as underreporting are avoided. However, although the movement data collected (timestamped positions in digital form) have generally high accuracy, they do not contain the transportation mode. We present in this paper a new method for segmenting movement data into single-mode segments and to classify them according to the transportation mode used. Our fully automatic method differs from previous attempts for five reasons: (1) it relies on fuzzy concepts found in expert systems, i.e. membership functions and certainty factors; (2) it uses OpenStreetMap data to help the segmentation and classification process; (3) we can distinguish between 10 transportation modes (incl. between tram, bus, and car) and we propose a hierarchy; (4) it handles data with signal shortages and noise, and other real-life situations; (5) in our implementation, there is a separation between the reasoning and the knowledge, so that users can easily modify the parameters used and add new transportation modes. We have implemented the method and tested it with a 17-million point dataset collected in the Netherlands and elsewhere in Europe. The accuracy of the classification with the developed prototype, determined with the comparison of the classified results with the reference data derived from manual classification, is 91.6 percent.

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Section: Concept Of the Classificationmentioning

confidence: 99%

Transportation mode-based segmentation and classification of movement trajectories

Biljecki

Ledoux

Oosterom

2013

International Journal of Geographical Information Science

133

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“…Due to the complexity of urban landscapes, which often does not fit to the requirements of parametric approaches, an increasing number of land cover classifications is carried out using techniques such as decision trees (e.g., [109]) or neural networks (e.g., [110,111]), where neural network techniques can also be applied to derive sub-pixel land cover maps (e.g., [65,112]). Another example is the use of support vector machines (SVM).…”

Section: Multispectral Approachesmentioning

confidence: 99%

Can the Future EnMAP Mission Contribute to Urban Applications? A Literature Survey

et al. 2011

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With urban populations and their footprints growing globally, the need to assess the dynamics of the urban environment increases. Remote sensing is one approach that can analyze these developments quantitatively with respect to spatially and temporally large scale changes. With the 2015 launch of the spaceborne EnMAP mission, a new hyperspectral sensor with high signal-to-noise ratio at medium spatial resolution, and a 21 day global revisit capability will become available. This paper presents the results of a literature survey on existing applications and image analysis techniques in the context of urban remote sensing in order to identify and outline potential contributions of the future EnMAP mission. Regarding urban applications, four frequently addressed topics have been identified: urban development and planning, urban growth assessment, risk and vulnerability assessment and urban climate. The requirements of four application fields and associated image processing techniques used to retrieve desired parameters and create geo-information products have been reviewed. As a result, we identified promising research directions enabling the use of EnMAP for urban studies. First and foremost, research is required to analyze the spectral information content of an EnMAP pixel used to support material-based land cover mapping approaches. This information can subsequently be used to improve urban indicators, such as imperviousness. Second, we identified the global monitoring of urban areas as a promising field of investigation taking advantage of EnMAP's spatial coverage and revisit capability.Remote Sens. 2011, 3 1818However, owing to the limitations of EnMAPs spatial resolution for urban applications, research should also focus on hyperspectral resolution enhancement to enable retrieving material information on sub-pixel level.

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“…Remote sensing experts are implementing new techniques, such as texture analysis and data fusion, to identify urban features, building types and building density [14]. The key issue is that the urban environment is characterized by a combination of materials and land use classes.…”

Section: Urban Studiesmentioning

confidence: 99%

Remote Sensing in Social Science Research~!2009-12-28~!2010-03-26~!2010-06-25~!

Hall¹

2010

TORMSJ

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Abstract:Since the early days of satellite remote sensing in the 1950's, accessibility, quality, and scope of remote sensing image data has been continuously improving, making it a rich data source with a wide range of applications. Today, the use of remote sensing techniques and data is commonplace within many disciplines in the natural sciences. Although there are quite a few examples of remote sensing to be found in the social sciences, developments here have, on the whole, been less pronounced. This paper investigates 1) how remote sensing data has been put to use in social science studies, and 2) how social science could better utilize the huge potential of remote sensing data. The first part of the paper gives an overview of existing types of remote sensing techniques and data collection. The second part consistsof a review of social science applications of remote sensing data. In the conclusions it is argued that remote sensing data is at its most valuable in the social sciences when used in combination with traditional methods such as surveys, public records, interviews and direct observation.

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Expert system classification of urban land use/cover for Delhi, India

Cited by 69 publications

References 71 publications

Transportation mode-based segmentation and classification of movement trajectories

Transportation mode-based segmentation and classification of movement trajectories

Can the Future EnMAP Mission Contribute to Urban Applications? A Literature Survey

Remote Sensing in Social Science Research~!2009-12-28~!2010-03-26~!2010-06-25~!

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