Can We Reliably Improve the Robustness to Image Acquisition of Remote Sensing of PV Systems?
Not on the map. Why?
Abstract (arXiv)
Photovoltaic (PV) energy is crucial for the decarbonization of energy systems. Due to the lack of centralized data, remote sensing of rooftop PV installations is the best option to monitor the evolution of the rooftop PV installed fleet at a regional scale. However, current techniques lack reliability and are notably sensitive to shifts in the acquisition conditions. To overcome this, we leverage the wavelet scale attribution method (WCAM), which decomposes a model's prediction in the space-scale domain. The WCAM enables us to assess on which scales the representation of a PV model rests and provides insights to derive methods that improve the robustness to acquisition conditions, thus increasing trust in deep learning systems to encourage their use for the safe integration of clean energy in electric systems.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 27 of its 54 references that are in the map. First posted on arXiv 21 Sep 2023.
Builds on
27 of 54 references.
References · 27
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
- 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,877
- 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,269
- 2017Understanding Black-box Predictions via Influence Functionscited by 1,741
- 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,432
- 2018On the Spectral Bias of Neural Networkscited by 1,341
- 2020In Search of Lost Domain Generalizationcited by 1,054
- 2019AugMix: A Simple Data Processing Method to Improve Robustness and Uncertaintycited by 901
- 2018RISE: Randomized Input Sampling for Explanation of Black-box Modelscited by 838
15 more
- 2018AutoAugment: Learning Augmentation Policies from Datacited by 730
- 2019Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networkscited by 368
- 2019High Frequency Component Helps Explain the Generalization of Convolutional Neural Networkscited by 310
- 2019A Fourier Perspective on Model Robustness in Computer Visioncited by 288
- 2021PixMix: Dreamlike Pictures Comprehensively Improve Safety Measurescited by 131
- 2017Measuring the tendency of CNNs to Learn Surface Statistical Regularitiescited by 87
- 2021Recent Advances in Domain Adaptation for the Classification of Remote Sensing Datacited by 70
- 2021Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysiscited by 65
- 2016Automatic Detection of Solar Photovoltaic Arrays in High Resolution Aerial Imagerycited by 14
- 2022When Neural Networks Fail to Generalize? A Model Sensitivity Perspectivecited by 10
- 2022A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadatacited by 9
- 2022Towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mappingcited by 7
- 2018Monitoring spatial sustainable development: Semi-automated analysis of satellite and aerial images for energy transition and sustainability indicatorscited by 6
- 2023Assessment of the Reliablity of a Model's Decision by Generalizing Attribution to the Wavelet Domaincited by 5
- 2019What you get is not always what you see: pitfalls in solar array assessment using overhead imagerycited by 3
Built on by
Citing papers per year: 2024: 2.
2 papers.