Image Based Characterization of Formal and Informal Neighborhoods in an Urban Landscape

Graesser, Jordan; Cheriyadat, Anil M.; Vatsavai, Ranga Raju; Chandola, Varun; Long, Jiachuan; Bright, Eddie A

doi:10.1109/jstars.2012.2190383

Cited by 133 publications

(128 citation statements)

References 42 publications

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“…One issue is that texture measures extracted for slums tend to vary across different locations, even within the same slum. This can be explained by physical differences in slums, for example, size, shape, orientation and building materials used to construct dwellings [75,77,102], the shape [103] and size of windows used to extract texture, as well as the spatial resolution of the imagery [104]. Consequently, textural patterns extracted from one image may not be applicable to another image [105].…”

Section: Image Texture Analysismentioning

confidence: 99%

“…Such approaches have also been applied to slums. Graesser et al [75], for example, applied a See5 decision tree to a set of 230 variables derived from various statistical approaches to study slums in cities in different parts of the world. That study reported overall accuracies of 91%, 89%, 92% and 85% for the cities of Caracas, Venezuela, Kabul and Kandahar in Afghanistan, and La Paz, Bolivia respectively, using all variables.…”

Section: Data Miningmentioning

confidence: 99%

“…As very few studies have used data mining tools and techniques to detect and map slums, the ability to generalize these methods is yet to be fully explored. Most studies reviewed have focused their analysis on very specific areas with even fewer studies, such as Graesser et al [75] and Vatsavai [76], applying methods to different geographic areas. This is particularly important for data mining approaches that utilize specifically empirically derived parameters using training data [134], making the generalization of such methods problematic.…”

Section: Data Miningmentioning

confidence: 99%

See 2 more Smart Citations

A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities

et al. 2018

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Slums are a global urban challenge, with less developed countries being particularly impacted. To adequately detect and map them, data is needed on their location, spatial extent and evolution. High-and very high-resolution remote sensing imagery has emerged as an important source of data in this regard. The purpose of this paper is to critically review studies that have used such data to detect and map slums. Our analysis shows that while such studies have been increasing over time, they tend to be concentrated to a few geographical areas and often focus on the use of a single approach (e.g., image texture and object-based image analysis), thus limiting generalizability to understand slums, their population, and evolution within the global context. We argue that to develop a more comprehensive framework that can be used to detect and map slums, other emerging sourcing of geospatial data should be considered (e.g., volunteer geographic information) in conjunction with growing trends and advancements in technology (e.g., geosensor networks). Through such data integration and analysis we can then create a benchmark for determining the most suitable methods for mapping slums in a given locality, thus fostering the creation of new approaches to address this challenge.

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Section: Image Texture Analysismentioning

confidence: 99%

Section: Data Miningmentioning

confidence: 99%

Section: Data Miningmentioning

confidence: 99%

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A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities

et al. 2018

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“…This list of image features (Figure 7) is generated based on distinguishing features reported in slum mapping studies (e.g., [4,10,22,23,56,[58][59][60]), as well as by considering the local characteristics of deprived areas in Mumbai.…”

Section: Extraction Of Features To Map the Diversity Of Deprivationmentioning

confidence: 99%

Capturing the Diversity of Deprived Areas with Image-Based Features: The Case of Mumbai

et al. 2017

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Many cities in the Global South are facing rapid population and slum growth, but lack detailed information to target these issues. Frequently, municipal datasets on such areas do not keep up with such dynamics, with data that are incomplete, inconsistent, and outdated. Aggregated census-based statistics refer to large and heterogeneous areas, hiding internal spatial differences. In recent years, several remote sensing studies developed methods for mapping slums; however, few studies focused on their diversity. To address this shortcoming, this study analyzes the capacity of very high resolution (VHR) imagery and image processing methods to map locally specific types of deprived areas, applied to the city of Mumbai, India. We analyze spatial, spectral, and textural characteristics of deprived areas, using a WorldView-2 imagery combined with auxiliary spatial data, a random forest classifier, and logistic regression modeling. In addition, image segmentation is used to aggregate results to homogenous urban patches (HUPs). The resulting typology of deprived areas obtains a classification accuracy of 79% for four deprived types and one formal built-up class. The research successfully demonstrates how image-based proxies from VHR imagery can help extract spatial information on the diversity and cross-boundary clusters of deprivation to inform strategic urban management.

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“…Builtup areas have been identified from remotely sensed imagery, such as low-and moderate-resolution multispectral imagery, and high-resolution panchromatic imagery. A number of approaches for extracting built-up areas from remotely sensed imagery have been proposed using planar texture, shape, and spectral features, such as the Pantex index (Pesaresi et al, 2008), normalized difference built-up index (Xu, 2008), improved Harris corner (Tao et al, 2013), morphological differential attribute profile (Pesaresi et al, 2013), SIFT (Sirmacek and Unsalan, 2009), Gabor filters (Sirmacek and Unsalan, 2010), and vegetation indices (Graesser et al, 2012). Compared with low-and moderate-resolution spaceborne imagery, highresolution spaceborne imagery contains more detailed information for obtaining more granular and precise urban area identification results.…”

Section: Introductionmentioning

confidence: 99%

Using Combination of Planar and Height Features for Detecting Built-Up Areas From High-Resolution Stereo Imagery

Peng¹,

Cai²,

Tan³

2017

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

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ABSTRACT:Within-class spectral variation and between-class spectral confusion in remotely sensed imagery degrades the performance of builtup area detection when using planar texture, shape, and spectral features. Terrain slope and building height are often used to optimize the results, but extracted from auxiliary data (e.g. LIDAR data, DSM). Moreover, the auxiliary data must be acquired around the same time as image acquisition. Otherwise, built-up area detection accuracy is affected. Stereo imagery incorporates both planar and height information unlike single remotely sensed images. Stereo imagery acquired by many satellites (e.g. Worldview-4, Pleiades-HR, ALOS-PRISM, and ZY-3) can be used as data source of identifying built-up areas. A new method of identifying highaccuracy built-up areas from stereo imagery is achieved by using a combination of planar and height features. The digital surface model (DSM) and digital orthophoto map (DOM) are first generated from stereo images. Then, height values of above-ground objects (e.g. buildings) are calculated from the DSM, and used to obtain raw built-up areas. Other raw built-up areas are obtained from the DOM using Pantex and Gabor, respectively. Final high-accuracy built-up area results are achieved from these raw built-up areas using the decision level fusion. Experimental results show that accurate built-up areas can be achieved from stereo imagery. The height information used in the proposed method is derived from stereo imagery itself, with no need to require auxiliary height data (e.g. LIDAR data). The proposed method is suitable for spaceborne and airborne stereo pairs and triplets.

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Image Based Characterization of Formal and Informal Neighborhoods in an Urban Landscape

Cited by 133 publications

References 42 publications

A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities

A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities

Capturing the Diversity of Deprived Areas with Image-Based Features: The Case of Mumbai

Using Combination of Planar and Height Features for Detecting Built-Up Areas From High-Resolution Stereo Imagery

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