- Check-In 3
-Introduction: We are re-implementing a 2019 paper by Zhoajian Hunag and colleagues in which the authors trained an image segmentation model using ImageNet data in order to calculate urban roof area and measure potential solar power production in Wuhan. We chose this paper because the training set is readily available and open-source, meaning our main task is to collect satellite images of Providence to run the model on. The authors’ objective was to use deep learning for image segmentation in order to study urban solar energy potential. This is a segmentation problem.
-Challenges: While the U-Net architecture the authors use is widely cited, referenced, and simple to find publicly available implementations of, their techniques for data collection, preprocessing, and visualization are not. The biggest challenges we are facing thus far relate to the code for these three functions. We are also still deciding how to implement the solar incidence and photovoltaic efficiency simulations that will allow us to take our model’s segmented roof areas and convert them into solar output scores.
-*Insights: * Are there any concrete results you can show at this point? -Implementation of the U-Net architecture -Emailed authors -Downloaded training data -Begun writing code to sample and preprocess images of Providence from Google Earth -Set up GCP VM
-Plan: Are you on track with your project? What do you need to dedicate more time to?
- Data collection and preprocessing
- Training model -Testing model
- Applying model to Providence
-What are you thinking of changing, if anything?
- If we complete the tasks above in a reasonable amount of time we’d like to dedicate our remaining time and resources toward a) visualization and b) model interpretation
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