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This report presents the results of the DeepSolaris project that was carriedout under the ESS action 'Merging Geostatistics and Geospatial Information inMember States'. During the project several deep learning algorithms wereevaluated to detect solar panels in remote sensing data. The aim of the projectwas to evaluate whether deep learning models could be developed, that workedacross different member states in the European Union. Two remote sensing datasources were considered: aerial images on the one hand, and satellite images onthe other. Two flavours of deep learning models were evaluated: classificationmodels and object detection models. For the evaluation of the deep learningmodels we used a cross-site evaluation approach: the deep learning models wheretrained in one geographical area and then evaluated on a different geographicalarea, previously unseen by the algorithm. The cross-site evaluation wasfurthermore carried out twice: deep learning models trained on he Netherlandswere evaluated on Germany and vice versa. While the deep learning models wereable to detect solar panels successfully, false detection remained a problem.Moreover, model performance decreased dramatically when evaluated in across-border fashion. Hence, training a model that performs reliably acrossdifferent countries in the European Union is a challenging task. That beingsaid, the models detected quite a share of solar panels not present in currentsolar panel registers and therefore can already be used as-is to help reducedmanual labor in checking these registers.

Monitoring Spatial Sustainable Development: semi-automated analysis of  Satellite and Aerial Images for Energy Transition and Sustainability  Indicators