1. Week 5
Cecilia La Place

2. Progress so far Papers read:
Dev et al - Short-term Prediction of Localized Cloud Motion Using Ground- Based Sky Imagers
Chu et al - Camera as weather sensor: Estimating weather information from single images
Model training
Split 1 has been training for 13 epochs
Preliminary testing has been done

3. Dev et al
Solves for cloud motion tracking and prediction for applications in solar energy generation, satellite communication, atmospheric research, and more.
Optical flow vectors estimate the direction and orientation of cloud movement between two frames
All estimations are under the assumption that there won’t be significant change in the cloud shape
Predictions are best with shorter lead times (prediction in 2 minutes etc) than with longer lead times (predictions in 10 minutes)
Approach was over small areas of sky, it might be better with larger areas covered for longer predictions

4. Chu et al
Seeks to answer weather property classification from single images by creating a dataset and an application to prove the usefulness of the solution
Built the Image2Weather dataset using images from image sites such as flickr and associate them with specific weather based on the metadata
Used Lu et al for sky segmentation (to label pixels as sky and not sky) for its simple implementation, and random forest for weather classification (weather type, temperature, humidity)
Built a weather-aware landmark classifier to apply their solution

5. Model
Found that Refinenet cannot use Camera 4679 properly (still being analyzed as to why)
Created 5 train-eval-test splits to average the results
Started training on Split 1
Stopped at 13 epochs to run preliminary evaluations and determine if it needs to train longer
Camera 858 mIOU
Continued training on Split 1 to epoch 20 and ran evaluation
Beginning training on Split 2

6. Camera 858’s segmentation map as of epoch 13 and original image

7. Camera 1093’s segmentation map as of epoch 13 and original image