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Automated Damage Assessment using Deep Learning
License: GNU General Public License v3.0
This project forked from gulfaraz/caladrius
Automated Damage Assessment using Deep Learning
License: GNU General Public License v3.0
Do I need to save the pictures before and after the disaster in the original data set into the Before and After folders respectively?
Are the labels of the pictures before and after the disaster stored in a labels file?
Current implementation applies random transformations to before and after images.
The same transformation should be applied on before and after images.
caladrius/caladrius/model/data.py
Lines 30 to 32 in 8401c46
For debugging any code refactors or new features not directly related to training the neural network, it would be useful to be able to execute run.py
on a not-so-powerful laptop.
tolist
returns a single item and not a list if the tensor contains only one item.
App throws error variable must be iterable
on trainer.py
It seems that pydoc
works best if the docstrings are contained in an installed package
Refactor the report interface into React components
Setup a changelog file and start numbering versions
Get a standard satellite dataset so the model can be used globally
Setup a Docker image for the code
Show a heatmap layer In leaflet map widget.
Button to make the map full screen.
Information about administrative regions need to be added to the building coordinates file, so that the final report can group the damage assessment by region.
Some of the building images have large regions of empty pixels. In the case of the drone data, we should check if any of the other files provide better coverage, otherwise discard the building.
To create a useful damage report, the address that corresponds to each building shape should be included in the main shape file.
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