Spatial parameters for transportation: A multi-modal approach for modelling the urban spatial structure using deep learning and remote sensing

Stiller, Dorothee; Wurm, Michael; Stark, Thomas; d’Angelo, Pablo; Stebner, Karsten; Dech, Stefan; Taubenböck, Hannes

doi:10.5198/jtlu.2021.1855

Cited by 6 publications

(9 citation statements)

References 92 publications

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“…Another consideration is that there are significant differences in the errors based on the size, population density and land use of the spatial unit. This corresponds with previous studies that also found relationships between higher errors and lower percentages of residential land use and higher mixed or other land uses [11] and population density [21]. Therefore, the PLOS ONE physical configuration of an urban landscape affects the performance of the disaggregation methods.…”

Section: Plos Onesupporting

confidence: 89%

“…Therefore, when VHR data are available, the dasymetric method is found to be the best option: the accuracies are better, it takes less computational effort, and it is more straightforward and easier to apply. When land use data are available, the categorical dasymetric approach is found to provide the best results: accuracies improve considerable if the number of stories is available, confirming results of Stiller et al [11].…”

Section: Plos Onesupporting

confidence: 79%

“…Within this group, three main methods can be conceptualized, which are explained in more detail in methods section. The dasymetric method employs geospatial auxiliary variables to allocate the population, which can be binary if it relies on built-up areas or volume [11,16,17,20,21,37], categorical if it considers the land use [20,38,39] or it can be based on the built-up density [21,40]. A recent contribution is Disaggregator, a tool that automatizes and facilitates the dasymetric disaggregation of geodata [41].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, Wan et al [46] examined the performance of alternative sources of ancillary data, including imperviousness, land cover, road networks and night-time lights, on the accuracy of the dasymetric method. Only two studies performed a multi-fold validation, evidencing a high influence in the measured accuracies [11,21]. However, none of these studies made a complete comparison of all the factors influencing the evaluation of disaggregation methods.…”

Section: Introductionmentioning

confidence: 99%

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Empiric recommendations for population disaggregation under different data scenarios

et al. 2022

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High-resolution population mapping is of high relevance for developing and implementing tailored actions in several fields: From decision making in crisis management to urban planning. Earth Observation has considerably contributed to the development of methods for disaggregating population figures with higher resolution data into fine-grained population maps. However, which method is most suitable on the basis of the available data, and how the spatial units and accuracy metrics affect the validation process is not fully known. We aim to provide recommendations to researches that attempt to produce high-resolution population maps using remote sensing and geospatial information in heterogeneous urban landscapes. For this purpose, we performed a comprehensive experimental research on population disaggregation methods with thirty-six different scenarios. We combined five different top-down methods (from basic to complex, i.e., binary and categorical dasymetric, statistical, and binary and categorical hybrid approaches) on different subsets of data with diverse resolutions and degrees of availability (poor, average and rich). Then, the resulting population maps were systematically validated with a two-fold approach using six accuracy metrics. We found that when only using remotely sensed data the combination of statistical and dasymetric methods provide better results, while highly-resolved data require simpler methods. Besides, the use of at least three relative accuracy metrics is highly encouraged since the validation depends on level and method. We also analysed the behaviour of relative errors and how they are affected by the heterogeneity of the urban landscape. We hope that our recommendations save additional efforts and time in future population mapping.

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Section: Plos Onesupporting

confidence: 89%

Section: Plos Onesupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Empiric recommendations for population disaggregation under different data scenarios

et al. 2022

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“…Data on land-use for the identification of residential buildings can be substituted for other European countries with data from the European Urban Atlas (https://land.copernicus.eu/local/urban-atlas). For other countries, land-use data from the OpenStreetMap project have been successfully incorporated to assign land-use to buildings [63]. Another crucial data set for modeling of the energy demand, however, is the construction period of the building.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Heat Demand Modeling

Wurm

Droin

Stark

et al. 2021

IJGI

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Cities are responsible for a large share of the global energy consumption. A third of the total greenhouse gas emissions are related to the buildings sector, making it an important target for reducing urban energy consumption. Detailed data on the building stock, including the thermal characteristics of individual buildings, such as the construction type, construction period, and building geometries, can strongly support decision-making for local authorities to help them spatially localize buildings with high potential for thermal renovations. In this paper, we present a workflow for deep learning-based building stock modeling using aerial images at a city scale for heat demand modeling. The extracted buildings are used for bottom-up modeling of the residential building heat demand based on construction type and construction period. The results for DL-building extraction exhibit F1-accuracies of 87%, and construction types yield an overall accuracy of 96%. The modeled heat demands display a high level of agreement of R2 0.82 compared with reference data. Finally, we analyze various refurbishment scenarios for construction periods and construction types, e.g., revealing that the targeted thermal renovation of multi-family houses constructed between the 1950s and 1970s accounts for about 47% of the total heat demand in a realistic refurbishment scenario.

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Peri-urban growth into natural hazard-prone areas: mapping exposure transformation of the built environment in Nairobi and Nyeri, Kenya, from 1948 to today

Fekete

2022

Nat Hazards

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Kenya experiences massive urban growth, also into natural hazard-prone areas, exposing settlements and the natural environment to riverine and pluvial floods and other natural hazards. While Nairobi as the capital and principal city has been extensively analysed regarding urban growth and flood hazard in some central parts, awareness of growing peri-urban areas has not been studied as much. The results are of interest to other locations in Kenya and worldwide, too, since the current research and disaster risk practice focus is still too much on megacities and city centres. Therefore, the study compares urban growth into hazard areas in urban rims of Nairobi and Nyeri, Kenya. A change assessment from 1948 to 2020 is conducted by aerial images, declassified satellite images, and recent data. Urban growth rates are 10- to 26-fold, while growth into flood exposed areas ranges from 2- to 100-fold. This study reveals unused opportunities for expanding existing land-use change analysis back to the 1940s in data-scarce environments.

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Spatial parameters for transportation: A multi-modal approach for modelling the urban spatial structure using deep learning and remote sensing

Cited by 6 publications

References 92 publications

Empiric recommendations for population disaggregation under different data scenarios

Empiric recommendations for population disaggregation under different data scenarios

Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Heat Demand Modeling

Peri-urban growth into natural hazard-prone areas: mapping exposure transformation of the built environment in Nairobi and Nyeri, Kenya, from 1948 to today

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