Some useful utils and a sample pipeline for AeroNet dataset for the Innopolis hackathon.
Utils allow to read and write the files (data_handling.py), and also contain a simple convolutionaal network on Keras (unet.py) with a generator for training and validation, and a Jupyter notebook with a sample pipeline of the data processing.
Data is located at Google drive https://drive.google.com/drive/folders/1sFidThVrPKYJ7N0fW848toz2OhRU95cG
This dataset contains satellite imagery before and after massive waildfires in California and partial markup.
prepared_data:
- Images with markup:
- ventura_train
- santa_rosa_train
contain 2 RGB images (pre- and post- event) organized in 3 seaprate channels each (pre_r, pre_g, pre_b etc.) and 3 raster masks of hte ground truth for the classes:
- all: all the buildings present in the pre_ image
- non_burned: all the buildings present in both pre_ and post_ images
- burned: all the buildings present in the pre_ image, but burned down in the post_ image.
All the masks are doubled by geojson vector files (convenient for GIS) All the images are georeferenced (web mercator)
- Images wihtour markup:
- ventura_test
- santa_rosa_test contain 2 RGB images organized as described above, without any ground truth. The images contain both burned and non-burned buildings.
raw satellite data fot the Santa Rosa area:
santa_rosa_raw.zip 2 large RGB files covering santa_rosa_test and santa_rosa_train areas as well as much more. They are georeferenced (lat-lon) and aligned to each other.