An Evaluation System for Building Footprint Extraction From Remotely Sensed Data

Zeng, Chao; Wang, Jinfei; Lehrbass, Brad

doi:10.1109/jstars.2013.2256882

Cited by 29 publications

(38 citation statements)

References 35 publications

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“…There are 1,075,210 reference water pixels for the Bow River site. The resulting accuracy for each input band combination is compared in terms of their user's accuracy, producer's accuracy [23], and Quality [35], as shown in Table 2.  User's accuracy (UA) = overlapped area/detected water area  Producer's accuracy (PA) = overlapped area/reference water area  Quality (Q) = overlapped area/(detected water area + reference water area − overlapped area) From Table 2, the accuracy (Q) of unsupervised classifications is 20% higher than direct thresholding MNDWI, which suggests that unsupervised classification outperforms direct thresholding for water body detection.…”

Section: Unsupervised Classification Accuracymentioning

confidence: 99%

A Natural-Rule-Based-Connection (NRBC) Method for River Network Extraction from High-Resolution Imagery

Zeng

Bird²,

Luce

et al. 2015

Remote Sensing

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This study proposed a natural-rule-based-connection (NRBC) method to connect river segments after water body detection from remotely sensed imagery. A complete river network is important for many hydrological applications. While water body detection methods using remote sensing are well-developed, less attention has been paid to connect discontinuous river segments and form a complete river network. This study designed an automated NRBC method to extract a complete river network by connecting river segments at polygon level. With the assistance of an image pyramid, neighbouring river segments are connected based on four criteria: gap width (Tg), river direction consistency (Tθ), river width consistency (Tw), and minimum river segment length (Tl). The sensitivity of these four criteria were tested, analyzed, and proper criteria values were suggested using image scenes from two diverse river cases. The comparison of NRBC and the alternative morphological method demonstrated NRBC's advantage of natural rule based selective connection. We refined a river centerline extraction method and show how it outperformed three other existing centerline extraction methods on the test sites. The extracted river polygons and centerlines have a multitude of end uses including rapidly mapping flood extents, monitoring surface water supply, and the provision of validation data for OPEN ACCESSRemote Sens. 2015, 7 14056 simulation models required for water quantity, quality and aquatic biota assessments. The code for the NRBC is available on GitHub.

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Section: Unsupervised Classification Accuracymentioning

confidence: 99%

A Natural-Rule-Based-Connection (NRBC) Method for River Network Extraction from High-Resolution Imagery

Zeng

Bird²,

Luce

et al. 2015

Remote Sensing

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“…These can be distinguished by the number and types of different quality parameters used: a good survey is provided by Foody [36] in the general context of land cover classification. In the specific context of this paper, a very good overview of existing quality evaluation methods is given by Zeng et al [37] who observe that a common approach that they describe as matched rates employs parameters originally derived from image classification (Foody [36]), i.e., completeness, correctness and quality. These are determined based on reference models (e.g., Rutzinger et al [38]; Truon-Hong and Laefer [39]).…”

Section: Quality Evaluationmentioning

confidence: 99%

“…These parameters can also be applied to extracted roof planes. The second approach considered by Zeng et al [37] is shape similarity which describes a more complicated category of metrics which may be subjective and poorly defined especially for complex shapes. For this, metrics like area and perimeter difference (Song and Haithcoat [40]), tangent function or moment-derived parameters can be used.…”

Section: Quality Evaluationmentioning

confidence: 99%

“…Other researchers have compared each segment of a contour line by checking if it lies inside a buffer zone around the corresponding segment of the reference model (Truong-Hong and Laefer [39]). The last approach identified by Zeng et al [37] is positional accuracy which is often determined by the root mean square error (RMSE) for comparing any extracted information and a higher accuracy reference, frequently employed in other areas of geospatial processing such as DEM quality assessment (e.g., Fisher and Tate [41]). It is clear from the literature that many different parameters have to be used, however, these may be strongly correlated and it is difficult to use many of these simultaneously.…”

Section: Quality Evaluationmentioning

confidence: 99%

“…It is clear from the literature that many different parameters have to be used, however, these may be strongly correlated and it is difficult to use many of these simultaneously. One response exemplified by Zeng et al [37] is to develop a multi-criteria evaluation method due to ambiguities and errors when using simple metrics. This sophisticated evaluation system incorporates different quality measures like geometric elements (shape, displacement) in addition to the more common detection rates in a hierarchical structure.…”

Section: Quality Evaluationmentioning

confidence: 99%

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Quality Assessment of Roof Planes Extracted from Height Data for Solar Energy Systems by the EAGLE Platform

et al. 2015

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Due to the increasing scarcity of fossil fuels and the upwards trend in energy costs over time, many countries-especially in Europe-have begun to modify their energy policies aiming to increase that percentage obtained from renewable energies. The EAGLE (FP7 program, European Commission) has developed a web-based platform to promote renewable energy systems (RES) in the public and private sectors, and to deliver a comprehensive information source for all interested users. In this paper, a comprehensive quality assessment of extracted roof planes suitable for solar energy installations (photovoltaic, solar thermal) from height data derived automatically from both LiDAR (Light Detection and Ranging) and aerial images will be presented. A shadow analysis is performed regarding the daily path of the sun including the shading effects of nearby objects (chimneys, dormers, vegetation, buildings, topography, etc.). A quality assessment was carried out for both LiDAR and aerial images of the same test sites in UK and Germany concerning building outline accuracy, extraction rate of roof planes and the accuracy of their geometric parameters (inclination and aspect angle, size). The benefit is an optimized system to extract roof planes for RES with a high level of detail, accuracy and flexibility (concerning different commonly available data sources) including an estimation of quality of the results which is important for individual house owners as well as for regional applications by governments or solar energy companies to judge their usefulness.

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Mapping Urban Footprint Using Machine Learning and Public Domain Datasets

Barman

Mustak

2023

Advances in Geographical and Environmental Sciences

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An Evaluation System for Building Footprint Extraction From Remotely Sensed Data

Cited by 29 publications

References 35 publications

A Natural-Rule-Based-Connection (NRBC) Method for River Network Extraction from High-Resolution Imagery

A Natural-Rule-Based-Connection (NRBC) Method for River Network Extraction from High-Resolution Imagery

Quality Assessment of Roof Planes Extracted from Height Data for Solar Energy Systems by the EAGLE Platform

Mapping Urban Footprint Using Machine Learning and Public Domain Datasets

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