Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery

Kit, Oleksandr; Lüdeke, Matthias

doi:10.1016/j.isprsjprs.2013.06.009

Cited by 73 publications

(63 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As suggested by Kit and Lüdeke [17], this is in part related to the unique nature of slums, which means that the development of fully automated slum identification methods continues to be imperfect. Kuffer et al [60] also suggests that the limited number of multitemporal studies on slums could be due to limitations with acquiring data on these settlements, as well as obtaining local knowledge to supplement this data overtime.…”

Section: Temporal Growth Of Slumsmentioning

confidence: 99%

“…In that study, the authors used a box counting routine to extract lacunarity values for varying box sizes, with box sizes close to 100 m 2 leading to convergence between slums and formal settlements, noting that higher lacunarity values were found to be associated with informal settlements. Other studies have additionally combined image preprocessing routines with lacunarity to help distinguish between formal and slum areas (e.g., [17,51]). …”

Section: Multi-scale Approachesmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Section: Temporal Growth Of Slumsmentioning

confidence: 99%

Section: Multi-scale Approachesmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…In the previously shown results, a period of five years was chosen to take into consideration the dramatic changes in slum areas over time as described in the introduction for Hyderabad where the area of slums has grown by 70% within seven years [7]. This change can also be observed in the comparison of the results for different points in time in Section 4.2.…”

Section: Impact Of Varying Calculation Periodsmentioning

confidence: 99%

“…This assumption is not far-fetched because slums develop very quickly. For example, the area of slums in Hyderabad, India, has grown by 70% within seven years [7]. The consequence of the lack of basic water infrastructure in slums is, among others, a high rate of child mortality [4].…”

Section: Introductionmentioning

confidence: 99%

A Holistic Concept to Design Optimal Water Supply Infrastructures for Informal Settlements Using Remote Sensing Data

et al. 2018

View full text Add to dashboard Cite

Ensuring access to water and sanitation for all is Goal No. 6 of the 17 UN Sustainability Development Goals to transform our world. As one step towards this goal, we present an approach that leverages remote sensing data to plan optimal water supply networks for informal urban settlements. The concept focuses on slums within large urban areas, which are often characterized by a lack of an appropriate water supply. We apply methods of mathematical optimization aiming to find a network describing the optimal supply infrastructure. Hereby, we choose between different decentral and central approaches combining supply by motorized vehicles with supply by pipe systems. For the purposes of illustration, we apply the approach to two small slum clusters in Dhaka and Dar es Salaam. We show our optimization results, which represent the lowest cost water supply systems possible. Additionally, we compare the optimal solutions of the two clusters (also for varying input parameters, such as population densities and slum size development over time) and describe how the result of the optimization depends on the entered remote sensing data.

show abstract

“…Recently researchers have explored how to capitalize on remotely sensed data to augment census and survey data. Additionally, remote sensing has been used to characterize living conditions of poor urban neighborhoods such as slums, informal settlements, marginal areas and low income neighborhoods through a combination of fine and coarse resolution data and often ancillary data [86][87][88]. Poverty and sub-standard housing in complex, cluttered, uncontrolled, and fast growing urbanized regions can be measured with very high spatial resolution remotely sensed data and associated geospatial techniques [89], however many challenges remain.…”

Section: Social and Economic Indicesmentioning

confidence: 99%

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

et al. 2014

View full text Add to dashboard Cite

This paper reviews how remotely sensed data have been used to understand the impact of urbanization on global environmental change. We describe how these studies can support the policy and science communities' increasing need for detailed and up-to-date information on the multiple dimensions of cities, including their social, biological, physical, and infrastructural characteristics. Because the interactions between urban and surrounding areas are complex, a synoptic and spatial view offered from remote sensing is integral to measuring, modeling, and understanding these relationships. Here we focus on OPEN ACCESSRemote Sens. 2014, 6 3880 three themes in urban remote sensing science: mapping, indices, and modeling. For mapping we describe the data sources, methods, and limitations of mapping urban boundaries, land use and land cover, population, temperature, and air quality. Second, we described how spectral information is manipulated to create comparative biophysical, social, and spatial indices of the urban environment. Finally, we focus how the mapped information and indices are used as inputs or parameters in models that measure changes in climate, hydrology, land use, and economics.

show abstract

Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery

Cited by 73 publications

References 25 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

A Holistic Concept to Design Optimal Water Supply Infrastructures for Informal Settlements Using Remote Sensing Data

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

Contact Info

Product

Resources

About