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