esy-osmfilter – A Python Library to Efficiently Extract OpenStreetMap Data

Pluta, Adam; Lünsdorf, Ontje

doi:10.5334/jors.317

Cited by 9 publications

(6 citation statements)

References 4 publications

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“…In the past, OSM data has contributed to the field of energy system modeling, for example, in the creation of power grid models [MMS + 17] or the optimization of flexibility options for urban areas [AMVA17a], [AMVA17b]. With the esy-osmfilter [PL20a] we used a Python library to easily access and filter data from OpenStreetMap. We have used this library for the creation of osmscigrid [PL20b], another library, which is capable of converting OSM pipeline data directly into SciGRID_gas data format for easier integration of OSM datasets.…”

Section: B Non-cc-by Compatiblementioning

confidence: 99%

“…We have used this library for the creation of osmscigrid [PL20b], another library, which is capable of converting OSM pipeline data directly into SciGRID_gas data format for easier integration of OSM datasets. Pluta and Lünsdorf [PL20a] have stated that the European gas pipeline data content from OSM was rapidly growing between 2014-2019. However, we saw that this trend has stagnated in the last two years, possibly due to the current COVID-19 pandemic.…”

Section: B Non-cc-by Compatiblementioning

confidence: 99%

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SciGRID_gas - Data Model of the European Gas Transport Network

Pluta

Medjroubi

Diettrich

et al. 2022

2022 Open Source Modelling and Simulation of Energy Systems (OSMSES)

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The current transition in the European energy sector towards climate neutrality requires detailed and reliable energy system modeling. The quality and relevance of the energy system modeling highly depend on the availability and quality of model input datasets. However, detailed and reliable datasets are still missing, especially for the gas infrastructure. In this contribution, we present our approach for developing an opensource model of the gas transport network in Europe. Various freely available data sources were used to collect gas transport data. The datasets from multiple sources were merged, and further, statistical methods were used to generate missing data.As a result, we successfully created a gas transport network model only using open-source data. The SciGRID_gas model contains 206,000 km of pipeline data which is roughly in accordance to former estimations. In addition, datasets of compressor stations, LNG terminals, storages, production sites, gas power plants, border points, and demand time series are provided. Finally, we have discussed data gaps and how they can potentially be closed.

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Section: B Non-cc-by Compatiblementioning

confidence: 99%

Section: B Non-cc-by Compatiblementioning

confidence: 99%

SciGRID_gas - Data Model of the European Gas Transport Network

Pluta

Medjroubi

Diettrich

et al. 2022

2022 Open Source Modelling and Simulation of Energy Systems (OSMSES)

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“…The substation geolocation with voltage level will be extracted from Open Street Maps (OSM). To be achieve computation on typical 16GB RAM computers, a relatively new efficient OSM extraction package esy-osmfilter will be applied that is four times faster than the common alternative, OSMOSIS [34]. Initial results of the OSM substation extraction process (see Fig.…”

Section: Network and Substation Modellingmentioning

confidence: 99%

PyPSA meets Africa: Developing an open source electricity network model of the African continent

et al. 2021

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Electricity network modelling and grid simulations form a key enabling element for the integration of newer and cleaner technologies such as renewable energy generation and electric vehicles into the existing grid and energy system infrastructure. This paper reviews the models of the African electricity systems and highlights the gaps in the open model landscape. Using PyPSA (an open Power System Analysis package), the paper outlines the pathway to a fully open model and data to increase the transparency in the African electricity system planning. Optimisation and modelling can reveal viable pathways to a sustainable energy system, aiding strategic planning for upgrades and policymaking for accelerated integration of renewable energy generation and smart grid technologies such as battery storage in Africa.

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“…The data we will use for testing was created from images of the NEL (Nordeuropäische Erdgasleitung) pipeline in Northern Germany (see Figure 3) to check how well the model generalizes to an unknown data set. The geo-referenced data of the NEL pipeline was obtained from OpenStreetMap (OSM) using the esy-osmfilter [7] Python package.…”

Section: Data Sourcementioning

confidence: 99%

“…The OpenStreetMap project has already proven to be a legitimate source for data mining projects regarding the European power grid, e.g., [5,6]. However, equal efforts for collecting gas transport pipelines in OpenStreetMap currently seem less promising, as the respective data is still very sparse [7]. Since gas transport pipelines are buried underground and thus are not identifiable with the naked eye, they are hard to map without additional efforts.…”

Section: Introductionmentioning

confidence: 99%

Detecting Pipeline Pathways in Landsat 5 Satellite Images with Deep Learning

Dasenbrock¹,

Pluta²,

Zech³

et al. 2021

Energies

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Energy system modeling is essential in analyzing present and future system configurations motivated by the energy transition. Energy models need various input data sets at different scales, including detailed information about energy generation and transport infrastructure. However, accessing such data sets is not straightforward and often restricted, especially for energy infrastructure data. We present a detection model for the automatic recognition of pipeline pathways using a Convolutional Neural Network (CNN) to address this lack of energy infrastructure data sets. The model was trained with historical low-resolution satellite images of the construction phase of British gas transport pipelines, made with the Landsat 5 Thematic Mapper instrument. The satellite images have been automatically labeled with the help of high-resolution pipeline route data provided by the respective Transmission System Operator (TSO). We have used data augmentation on the training data and trained our model with four different initial learning rates. The models trained with the different learning rates have been validated with 5-fold cross-validation using the Intersection over Union (IoU) metric. We show that our model can reliably identify pipeline pathways despite the comparably low resolution of the used satellite images. Further, we have successfully tested the model’s capability in other geographic regions by deploying satellite images of the NEL pipeline in Northern Germany.

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esy-osmfilter – A Python Library to Efficiently Extract OpenStreetMap Data

Cited by 9 publications

References 4 publications

SciGRID_gas - Data Model of the European Gas Transport Network

SciGRID_gas - Data Model of the European Gas Transport Network

PyPSA meets Africa: Developing an open source electricity network model of the African continent

Detecting Pipeline Pathways in Landsat 5 Satellite Images with Deep Learning

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