Episode Details

The research evaluates the accuracy and completeness of wind and solar energy infrastructure data in OpenStreetMap (OSM) for Belgium and Ireland, identifying common mapping errors and proposing techniques to enrich available data. By combining OSM data with CORINE Land Cover inventory, we study land use patterns around renewable energy infrastructures, facilitating environmental planning. We are witnessing the rise of a collective awareness of the importance of building a more environmentally sustainable future. Within this context, the relevance of renewable energy sources is widely acknowledged; however, to support the energy transition, the availability of reliable data about energy supply, infrastructures, and their environmental impacts is essential [1]. OpenStreetMap (OSM) emerges as a valuable data source to meet these requirements. In this work, we describe our research on evaluating the OSM database in a study of wind and solar energy infrastructures. As a case-study, we analyse two countries: Belgium and Ireland. Data within OSM is well-known to have a diverse level of completeness and granularity [2]. Considering OSM's vibrant mapping communities coupled with both (a) the environmental visibility of wind and solar energy infrastructures and (b) their relatively limited number compared to other built infrastructures, it is reasonable to assume that the majority of these installations are well mapped within OSM. OSM energy-related objects are mapped under the _“power”_ tag, with available key-value combinations to identify wind and solar sources. Wind turbines are commonly tagged as _“power=generator”_ and _“generator:source=wind”_, while solar farms are identified as _“power=plant”_ and _“plant:source=solar”_. By combining OSM data with the CORINE Land Cover (CLC) inventory we considers two research questions. Firstly, we seek to identify common mapping errors and tagging issues associated with wind and solar energy infrastructure representation within OSM. This involves examining geometries and tagging mistakes while evaluating the accuracy and completeness of these infrastructures. Secondly, we perform a geographical analysis to consider the distribution of infrastructures across various CLC land covers. We seek to detect patterns around land cover and renewable energy infrastructure. Our methodology is summarised as follows: OSM data, in PBF format, is downloaded from _GeoFabrik_. Initially here we just consider Ireland and Belgium due to local knowledge and their manageable data sizes. Analysis is performed using _Python_ and the _osmium_ library. Pending acceptance, source code will be made openly available on GitHub in documented Jupyter notebooks. We answer our research questions using three key steps: 1. Assess available wind and solar infrastructures in OSM for the correctness of OSM datatypes and potential geometric errors. 2. Differentiate between individual installations and larger "farms", to evaluate the quality and completeness of OSM data. 3. Investigate commonalities around land use surrounding renewable energy infrastructures using CLC. Initially, we extract all OSM objects with _“power”_ tag =_“plant”_ or _“generator”_, yielding a full listing of all available power sources. We filter solar and wind sources based on the _"generator:source"_ or _"plant:source"_ tags. The accuracy of the OSM datatypes and geometries is checked before moving to "farms" identification. To accurately differentiate single installations from farms, we analyze solar and wind sources separately due to their different geometric nature. We employ a point-in-polygon search with spatial indexes to assign nodes – of either wind or solar type – to intersecting areas of the same energy type. This process raises some geometric questions concerning the meaning of areas without nodes and nodes not linked to any area. We observed several issues: (a) not mapping individual panels within solar farms or defining turbines as circular ar