Week 5 Cecilia La Place

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

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

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

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 - 54.09 Continued training on Split 1 to epoch 20 and ran evaluation Beginning training on Split 2

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

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