A data fusion-based framework to integrate multi-source VGI in an authoritative land use database

Liu, Lanfa; Olteanu‐Raimond, Ana‐Maria; Jolivet, Laurence; Bris, Arnaud Le; See, Linda

doi:10.1080/17538947.2020.1842524

Cited by 11 publications

(5 citation statements)

References 71 publications

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“…The UN SDG indicators use data from citizen science to monitor progress on the SDGs [35]. On the LULC side, Liu et al have shown how data collected through a web-based and mobile app called Paysages can be integrated with data fusion approaches into the official LULC map of the French National Mapping Agency [36]. Hence, there is considerable interest in using data from citizen science in official data sets.…”

Section: Discussionmentioning

confidence: 99%

Crowdsourcing LUCAS: Citizens Generating Reference Land Cover and Land Use Data with a Mobile App

Bayas

See

Bartl

et al. 2020

Land

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There are many new land use and land cover (LULC) products emerging yet there is still a lack of in situ data for training, validation, and change detection purposes. The LUCAS (Land Use Cover Area frame Sample) survey is one of the few authoritative in situ field campaigns, which takes place every three years in European Union member countries. More recently, a study has considered whether citizen science and crowdsourcing could complement LUCAS survey data, e.g., through the FotoQuest Austria mobile app and crowdsourcing campaign. Although the data obtained from the campaign were promising when compared with authoritative LUCAS survey data, there were classes that were not well classified by the citizens. Moreover, the photographs submitted through the app were not always of sufficient quality. For these reasons, in the latest FotoQuest Go Europe 2018 campaign, several improvements were made to the app to facilitate interaction with the citizens contributing and to improve their accuracy in LULC identification. In addition to extending the locations from Austria to Europe, a change detection component (comparing land cover in 2018 to the 2015 LUCAS photographs) was added, as well as an improved LC decision tree. Furthermore, a near real-time quality assurance system was implemented to provide feedback on the distance to the target location, the LULC classes chosen and the quality of the photographs. Another modification was a monetary incentive scheme in which users received between 1 to 3 Euros for each successfully completed quest of sufficient quality. The purpose of this paper is to determine whether citizens can provide high quality in situ data on LULC through crowdsourcing that can complement LUCAS. We compared the results between the FotoQuest campaigns in 2015 and 2018 and found a significant improvement in 2018, i.e., a much higher match of LC between FotoQuest Go Europe and LUCAS. As shown by the cost comparisons with LUCAS, FotoQuest can complement LUCAS surveys by enabling continuous collection of large amounts of high quality, spatially explicit field data at a low cost.

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Section: Discussionmentioning

confidence: 99%

Crowdsourcing LUCAS: Citizens Generating Reference Land Cover and Land Use Data with a Mobile App

Bayas

See

Bartl

et al. 2020

Land

Self Cite

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“…POIs generally refer to all geographical objects that can be abstracted as points and are mainly used to represent various service facilities in urban spaces. POI data cover spatial distribution and attribute the information of geographical objects, which are widely used in urban spatial structure and function analysis [ 27 , 28 , 29 ], vitality evaluation [ 30 , 31 ], and land cover verification [ 32 , 33 ]. POIs can be easily obtained by a special software development kit (SDK) provided by the Amap open platform ( , accessed on 1 January 2022), the acquisition methods of which include key-word search, peripheral search, ID search, etc.…”

Section: Study Area and Data Sourcesmentioning

confidence: 99%

Urban Form and Function Optimization for Reducing Carbon Emissions Based on Crowd-Sourced Spatio-Temporal Data

Cao

Qiu

Wang

et al. 2022

IJERPH

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The low-carbon city has become an important global urban development-oriented goal. One important aspect of urban space is low-carbon urban planning, which has a vital role in urban carbon emissions. Which types of urban form and function allocations are conducive to reducing carbon emissions is therefore a key issue. In this study, the Futian and Luohu Districts of Shenzhen, Guangdong Province, China, are taken as an example to investigate this issue. Firstly, a “head/tail” breaks method based on the third fractal theory is adopted to obtain the minimum evaluation parcel of urban space. Then, the Landscape Shape Index (LSI), Fragmentation Index (C), Shannon’s Diversity Index (SHDI), and Density of Public Facilities (Den) are used to evaluate the form and function allocation of each parcel. In addition, the CO2 concentration distribution in this study area is acquired from remote sensing satellite data. Finally, the relationships between urban form, function allocation, and CO2 concentration are obtained. The results show that the lower the urban form index or the higher the urban function index, the less the CO2 concentration. To verify this conclusion, three experiments are designed and carried out. In experiment A, the CO2 concentration of the tested area is reduced by 14.31% by decreasing the LSI and C by 6.1% and 9.4%, respectively. In experiment B, the CO2 concentration is reduced by 15.15% by increasing the SHDI and Den by 16.3% and 12.1%, respectively. In experiment C, the CO2 concentration is reduced by 27.72% when the urban form and function are adjusted in the same was as in experiments A and B.

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“…The Dempster-Shafer theory has been applied for fusing land cover information from different remote sensing imagery [55] and for urban change detection [56], but not to urban green space mapping in particular. In the context of volunteered geographic information, Comber et al [57] evaluated different methods including Dempster-Shafer theory for fusing classifications of multiple volunteers for land cover mapping and Liu et al [58] used Dempster-Shafer theory to update authoritative land use data with data collected from volunteers via in-situ and online mapping campaigns. Applications of Dempster-Shafer theory for fusing OSM and remote sensing data did not exists at the time of this writing.…”

Section: Related Workmentioning

confidence: 99%

Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

Ludwig

Hecht

Lautenbach

et al. 2021

IJGI

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Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model.

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A data fusion-based framework to integrate multi-source VGI in an authoritative land use database

Cited by 11 publications

References 71 publications

Crowdsourcing LUCAS: Citizens Generating Reference Land Cover and Land Use Data with a Mobile App

Crowdsourcing LUCAS: Citizens Generating Reference Land Cover and Land Use Data with a Mobile App

Urban Form and Function Optimization for Reducing Carbon Emissions Based on Crowd-Sourced Spatio-Temporal Data

Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

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