A Multi-layer CRF Based Methodology for Improving Crowdsourced Street Semantics

Jilani, Musfira; Corcoran, Padraig; Bertolotto, Michela

doi:10.1145/3283207.3283210

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…An example is OSM, which still suffers from variable data quality. Now, the use of contextual information has been proposed to improve the quality of OSM [12], [22], but we raise the following questions with respect to this proposal: What data is good enough? and Is the data available?…”

Section: B Discussionmentioning

confidence: 99%

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Exploring Budgeted Learning for Data-Driven Semantic Inference via Urban Functions

2020

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The performance of a machine learning algorithm is dependent on the quality of the available data for model development. However, in practical situations, the availability of the data is variable and can be limited. This limitation creates a budget problem for data-driven techniques and the objective in such situations is to develop the best model given the available data. In this article, we examine the budgeted learning problem for spatial data within the urban context. We demonstrate the effectiveness of a novel approach for inferring the attributes of spatial data when the data for the model is budgeted. This is achieved using urban functions-which describe the designated use of a geographical space-to infer the types of streets in a city. We evaluated the approach by comparing the performance of the model using the data in each urban function (the budget) against the results from the aggregate of all the functions (all data). The results indicate that with our model, individual urban functions are sufficient to infer the type attributes of streets.

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Section: B Discussionmentioning

confidence: 99%

“…This is an open geo-spatial database where users can freely make changes to objects. This project has been acclaimed to provide a more complete or up-to-date view of a place than authoritative data-sets [12]. It has also been invaluable in scenarios such as disaster management [18], [19].…”

Section: Background a Budgeted Learning And Spatial Semantic Infmentioning

confidence: 99%

Exploring Budgeted Learning for Data-Driven Semantic Inference via Urban Functions

2020

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“…The method in [41] uses graphical models with geometrical and spatial features, such that the parameters of the model are learned by Structured Support Vector Machines (SSVM) [42]. More recently, the authors in [43] propose a multi-layer CRF (Conditional Random Field) model to perform hierarchical classification of street types into coarse and fine-grained classes. In [44] the authors propose a graph convolutional network based method for driving speed estimation of road segments.…”

Section: Extracting Roads From Aerial Imagesmentioning

confidence: 99%

OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing

Muñoz

Srivastava

Tuia

et al. 2021

IEEE Geosci. Remote Sens. Mag.

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OpenStreetMap (OSM) is a community-based, freely available, editable map service that was created as an alternative to authoritative ones. Given that it is edited mainly by volunteers with different mapping skills, the completeness and quality of its annotations are heterogeneous across different geographical locations. Despite that, OSM has been widely used in several applications in Geosciences, Earth Observation and environmental sciences. In this work, we present a review of recent methods based on machine learning to improve and use OSM data. Such methods aim either 1) at improving the coverage and quality of OSM layers, typically using GIS and remote sensing technologies, or 2) at using the existing OSM layers to train models based on image data to serve applications like navigation or land use classification. We believe that OSM (as well as other sources of open land maps) can change the way we interpret remote sensing data and that the synergy with machine learning can scale participatory map making and its quality to the level needed to serve global and up-to-date land mapping. A preliminary version of this manuscript has been presented in the first authors dissertation [1].

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