Application of snakes and dynamic programming optimisation technique in modeling of buildings in informal settlement areas

Rüther, Heinz; Martine, Hagai M; Mtalo, E.G

doi:10.1016/s0924-2716(02)00062-x

Cited by 68 publications

(39 citation statements)

References 9 publications

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“…It has been argued that snakes provide a more robust and elastic option for locating the boundaries of features in remote sensing imagery [126]. Rüther et al [74] used snakes to extract dwellings from contours derived from an nDSM of the Marconi Beam and Manzese slum settlements in Cape Town, South Africa and Dar es Salaam in Tanzania respectively. In that study, an extraction accuracy of 62% was reported with an overall 81% rooftop shape extraction accuracy.…”

Section: Building Feature Extractionmentioning

confidence: 99%

“…According to Ioannidis et al [92], simple DSM approaches include not only slum dwellings but also other artifacts such as vegetation, which leads to reduced extraction accuracy. Also, stereo image matching techniques traditionally used for generating DSM suffer from insufficient ground sampling data, poor image quality and degradation from shadows and occlusions, which obstruct the outlines of buildings [72,74,128]. Contour models can alleviate some of these limitations, however, they have been criticized for first having to be initialized [129], and encounter difficultly in distinguishing objects with similar height [92].…”

Section: Building Feature Extractionmentioning

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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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: Building Feature Extractionmentioning

confidence: 99%

Section: Building Feature Extractionmentioning

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

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“…A qualitative method checks the quality of estimated dominant directions and derived footprints by visually comparing the extracted footprints with those in maps and aerial photographs. The quantitative method examines the accuracy by extending the count-based metric method proposed by Shufelt and Mckeown [3] and area-based metric method suggested by Ruther et al [4]. The count-based metric method quantifies commission and omission errors in the number of buildings identified.…”

Section: Resultsmentioning

confidence: 99%

“…5b. This operation compares the ratio R of the area of the triangle P 2 P P 3 to that of triangle P 1 P P 4 . If the R is less than the threshold T Ratio, the four points P 1 , P 2 , P 3 , and P 4 are replaced with points P 1 , P and P 4 .…”

Section: Estimated Dominant Directions Are the Same As The Directionsmentioning

confidence: 99%

Automatic Construction of Building Footprints From Airborne LIDAR Data

Zhang

Yan

Chen

2006

IEEE Trans. Geosci. Remote Sensing

243

111

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Abstract-This paper presents a framework that applies a series of algorithms to automatically extract building footprints from airborne LIDAR measurements. In the proposed framework, the ground and non-ground LIDAR measurements are first separated using a progressive morphological filter. Then, building measurements are identified from non-ground measurements using a region growing algorithm based on the planefitting technique. Finally, raw footprints for segmented building measurements are derived by connecting boundary points, and the raw footprints are further simplified and adjusted to remove noise caused by irregularly spaced LIDAR measurements. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. Quantitative analysis showed that the total of omission and commission errors for extracted footprints for both institutional and residential areas was about 12%. The results demonstrated that the proposed framework identified building footprints well.

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“…Related to our work, two publications are to be mentioned in particular. In the first one, informal settlement areas are the subject of research, as well, but a different optimization technique, the dynamic programming optimization, is applied to semi-automatically extract buildings from aerial photographs [26]. The second one uses the same optimization method as we do, mixed-integer linear programming (cf.…”

Section: Introduction To Optimization and Technical Operations Researchmentioning

confidence: 99%

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

et al. 2018

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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.

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Application of snakes and dynamic programming optimisation technique in modeling of buildings in informal settlement areas

Cited by 68 publications

References 9 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

Automatic Construction of Building Footprints From Airborne LIDAR Data

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

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