Fine-grained population estimation

Bast, Hannah; Storandt, Sabine; Weidner, Simon

doi:10.1145/2820783.2820828

Cited by 13 publications

(6 citation statements)

References 15 publications

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“…Before carrying out any experiments, it is obvious that the number of storeys appears to be a very useful predictor of the building's height, but it can be problematic to obtain in practice (Fan et al, 2014;Bast et al, 2015). This is also evident from our study area for which for a third of buildings we do not have this attribute.…”

Section: Building Attributes (Cadastre)mentioning

confidence: 89%

Generating 3D city models without elevation data

Biljecki

Ledoux

Stoter

2017

Computers, Environment and Urban Systems

122

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Elevation datasets (e.g. point clouds) are an essential but often unavailable ingredient for the construction of 3D city models. We investigate in this paper to what extent can 3D city models be generated solely from 2D data without elevation measurements. We show that it is possible to predict the height of buildings from 2D data (their footprints and attributes available in volunteered geoinformation and cadastre), and then extrude their footprints to obtain 3D models suitable for a multitude of applications. The predictions have been carried out with machine learning techniques (random forests) using 10 different attributes and their combinations, which mirror different scenarios of completeness of real-world data. Some of the scenarios resulted in surprisingly good performance (given the circumstances): we have achieved a mean absolute error of 0.8 m in the inferred heights, which satisfies the accuracy recommendations of CityGML for LOD1 models and the needs of several GIS analyses. We show that our method can be used in practice to generate 3D city models where there are no elevation data, and to supplement existing datasets with 3D models of newly constructed buildings to facilitate rapid update and maintenance of data.

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Section: Building Attributes (Cadastre)mentioning

confidence: 89%

Generating 3D city models without elevation data

Biljecki

Ledoux

Stoter

2017

Computers, Environment and Urban Systems

122

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“…While the data consumption of OSM mainly comes from map rendering, geocoding, and smart routing, its analytical value has yet to be explored. The previous OSM data analytical work mainly focuses on the measurement of content bias [8] or predictive analysis such as fine-grained population estimation [3]. In this work, we integrate OSM data with geo-tagged social media for semantic annotation.…”

Section: Related Workmentioning

confidence: 99%

“…However, many objects only have a simple point based representation due to limited information or due to the small extent of the objects. For example, less than half of churches in OSM have boundaries 3 .…”

Section: Introductionmentioning

confidence: 99%

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Annotating Geographical Objects in OpenStreetMap with Geo-tagged Social Media

Chen

Wang

2016

Proceedings of the 9th ACM SIGSPATIAL Workshop on Location-Based Social Networks

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Recent years have witnessed an explosion of geospatial data, especially in the form of Volunteered Geographic Information (VGI). As a prominent example, OpenStreetMap (OSM) creates a free editable map of the world from a large number of contributors. On the other hand, social media platforms such as Twitter or Instagram supply dynamic social feeds at population level. As much of such data is geo-tagged, there is a high potential on integrating social media with OSM to enrich OSM with semantic annotations, which will complement existing objective description oriented annotations to provide a broader range of annotations. In this paper, we propose a comprehensive framework on integrating social media data and VGI data to derive knowledge about geographical objects, specifically, top relevant annotations from tweets for objects in OSM. We first integrate geo-tagged tweets with OSM data with scalable spatial queries running on MapReduce. We propose a frequency based method for annotating boundary based geographic objects, and a probability based method for annotating point based geographic objects, with consideration of noise. We evaluate our methods using a large geo-tagged tweets corpus and representative geographic objects from OSM, which demonstrates promising results through ground-truth comparison and case studies. We are able to produce up to 80% correct names for geographical objects and discover implicitly relevant information, such as popular exhibitions of a museum, the nicknames or visitors' impression to a tourism attraction.

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“…LiDAR data were used to detect building characteristics, such as footprint and volume to obtain detailed population data [31,32]. Bast et al (2015) disaggregated population on the building level using OSM and population counts on the municipality level [33]. The population was recently also disaggregated to a 10 m × 10 m grid for the whole of Germany.…”

Section: Introductionmentioning

confidence: 99%

Population Disaggregation on the Building Level Based on Outdated Census Data

Pajares

Nieto

Meng

et al. 2021

IJGI

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A wide range of disciplines require population data with high spatial resolution. In particular, accessibility instruments for active mobility need data on the building access level. Data availability varies by context. Spatially detailed national census counts often present the challenge that they are outdated. Therefore, this study proposes a novel approach to hybrid population disaggregation. It updates outdated census tracts and disaggregates population on the building access level. Open and widely available data sets are used. A bottom-up population estimation for new development areas is combined with a top-down dasymetric mapping process to update outdated census tracts. A particular focus lies on the high flexibility of the developed procedure. Accordingly, users can utilize diverse data and adapt settings to a specific study context. Instead of requiring ubiquitous 3D building data, often unavailable free of charge, the approach suggests collecting building levels only in new development areas. The open-source software development was done using PostgreSQL/PostGIS as part of the co-creative development of the accessibility instrument GOAT in three German municipalities. A comparison with reference data from the population registry of one district was realized. On the building level, an R2 of 0.82, and on the grid level (100 m × 100 m), an R2 of 0.89 is reached. The approach stands out when land-use information is outdated; however, a spatially detailed census grid exists, but no ubiquitous 3D building information is available. Enhancements are proposed, such as improving the dasymetric mapping with machine learning and remote sensing techniques. Moreover, more reliable detection of new building development in already built-up areas is suggested to account better for urban densification.

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Fine-grained population estimation

Cited by 13 publications

References 15 publications

Generating 3D city models without elevation data

Generating 3D city models without elevation data

Annotating Geographical Objects in OpenStreetMap with Geo-tagged Social Media

Population Disaggregation on the Building Level Based on Outdated Census Data

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