DeepSolar tracker: towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping

by   Gabriel Kasmi, et al.

Photovoltaic (PV) energy is key to mitigating the current energy crisis. However, distributed PV generation, which amounts to half of the PV energy generation, makes it increasingly difficult for transmission system operators (TSOs) to balance the load and supply and avoid grid congestions. Indeed, in the absence of measurements, estimating the distributed PV generation is tough. In recent years, many remote sensing-based approaches have been proposed to map distributed PV installations. However, to be applicable in industrial settings, one needs to assess the accuracy of the mapping over the whole deployment area. We build on existing work to propose an automated PV registry pipeline. This pipeline automatically generates a dataset recording all distributed PV installations' location, area, installed capacity, and tilt angle. It only requires aerial orthoimagery and topological data, both of which are freely accessible online. In order to assess the accuracy of the registry, we propose an unsupervised method based on the Registre national d'installation (RNI), that centralizes all individual PV systems aggregated at communal level, enabling practitioners to assess the accuracy of the registry and eventually remove outliers. We deploy our model on 9 French départements covering more than 50 000 square kilometers, providing the largest mapping of distributed PV panels with this level of detail to date. We then demonstrate how practitioners can use our unsupervised accuracy assessment method to assess the accuracy of the outputs. In particular, we show how it can easily identify outliers in the detections. Overall, our approach paves the way for a safer integration of deep learning-based pipelines for remote PV mapping. Code is available at


FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning

In recent decades, wildfires, as widespread and extremely destructive na...

DeepRec: An Open-source Toolkit for Deep Learning based Recommendation

Deep learning based recommender systems have been extensively explored i...

Scalable Label-efficient Footpath Network Generation Using Remote Sensing Data and Self-supervised Learning

Footpath mapping, modeling, and analysis can provide important geospatia...

PyTorch-Hebbian: facilitating local learning in a deep learning framework

Recently, unsupervised local learning, based on Hebb's idea that change ...

Code-free development and deployment of deep segmentation models for digital pathology

Application of deep learning on histopathological whole slide images (WS...

METER-ML: A Multi-sensor Earth Observation Benchmark for Automated Methane Source Mapping

Reducing methane emissions is essential for mitigating global warming. T...

Can We Reliably Improve the Robustness to Image Acquisition of Remote Sensing of PV Systems?

Photovoltaic (PV) energy is crucial for the decarbonization of energy sy...

Please sign up or login with your details

Forgot password? Click here to reset