• Corpus ID: 209374387

Identify Solar Panels in Low Resolution Satellite Imagery with Siamese Architecture and Cross-Correlation

@inproceedings{Wang2019IdentifySP,
  title={Identify Solar Panels in Low Resolution Satellite Imagery with Siamese Architecture and Cross-Correlation},
  author={Zhengcheng Wang and Zhecheng Wang and Arun Majumdar and Ram Rajagopal},
  year={2019}
}
Understanding solar adoption trends and their underlying dynamics requires a comprehensive and granular time-series solar installation database which is unavailable today and expensive to create manually. To this end, we leverage a deep siamese network that automatically identifies solar panels in historical low-resolution (LR) satellite images by comparing the target image with its high-resolution exemplar at the same location. To resolve the potential displacement between solar panels in the… 
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Predicting PV Areas in Aerial Images with Deep Learning
  • M. Zech, J. Ranalli
  • Environmental Science, Computer Science
    2020 47th IEEE Photovoltaic Specialists Conference (PVSC)
  • 2020
TLDR
A Fully Convolutional Neural Network is trained and applied to identify PV sites from aerial images of Oldenburg, Germany acquired from Google Maps, able to accurately estimate location and shape of PV plants in the north European town ofOldenburg.

References

SHOWING 1-10 OF 11 REFERENCES
DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States
SiamRPN++: Evolution of Siamese Visual Tracking With Very Deep Networks
TLDR
This work proves the core reason Siamese trackers still have accuracy gap comes from the lack of strict translation invariance, and proposes a new model architecture to perform depth-wise and layer-wise aggregations, which not only improves the accuracy but also reduces the model size.
Siamese Neural Networks for One-Shot Image Recognition
TLDR
A method for learning siamese neural networks which employ a unique structure to naturally rank similarity between inputs and is able to achieve strong results which exceed those of other deep learning models with near state-of-the-art performance on one-shot classification tasks.
Fully-Convolutional Siamese Networks for Object Tracking
TLDR
A basic tracking algorithm is equipped with a novel fully-convolutional Siamese network trained end-to-end on the ILSVRC15 dataset for object detection in video and achieves state-of-the-art performance in multiple benchmarks.
Learning Deep Features for Discriminative Localization
In this work, we revisit the global average pooling layer proposed in [13], and shed light on how it explicitly enables the convolutional neural network (CNN) to have remarkable localization ability…
End-to-End Representation Learning for Correlation Filter Based Tracking
TLDR
This work is the first to overcome this limitation by interpreting the Correlation Filter learner, which has a closed-form solution, as a differentiable layer in a deep neural network, which enables learning deep features that are tightly coupled to the Cor correlation filter.
Deep Residual Learning for Image Recognition
TLDR
This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
TLDR
This work revisits both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network.
Learning a similarity metric discriminatively, with application to face verification
TLDR
The idea is to learn a function that maps input patterns into a target space such that the L/sub 1/ norm in the target space approximates the "semantic" distance in the input space.
Signature Verification Using A "Siamese" Time Delay Neural Network
TLDR
An algorithm for verification of signatures written on a pen-input tablet based on a novel, artificial neural network called a "Siamese" neural network, which consists of two identical sub-networks joined at their outputs.
...
1
2
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