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firemapping-model

Trained ResNet on Australian Bushfire linescan images to predict fire as a 0:1 and then predict the path of fire. This assists the firefighter department in allocating their limited manpower in areas where the fire will likely traverse.

https://github.com/jaina1008/firemapping-model

Science Score: 44.0%

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    CITATION.cff file
    Found CITATION.cff file
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    Found .zenodo.json file
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    Scientific vocabulary similarity
    Low similarity (9.9%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Trained ResNet on Australian Bushfire linescan images to predict fire as a 0:1 and then predict the path of fire. This assists the firefighter department in allocating their limited manpower in areas where the fire will likely traverse.

Basic Info
  • Host: GitHub
  • Owner: jaina1008
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 18.4 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 2
  • Open Issues: 1
  • Releases: 0
Created over 4 years ago · Last pushed over 4 years ago
Metadata Files
Readme Citation

README.md

FireMappingProject

Setting up the environment

  1. Clone the repo.

git clone https://github.com/FoundingTitan/Fire_Mapping_Project.git

  1. Copy the contents and paste it into the root of Google Drive.

/content/drive/MyDrive/Fire_Mapping_Project

  1. Open a colab session at https://colab.research.google.com/

Make sure the session is set to GPU

Runtime > Change runtime Type > Hardware accelerator (from None to GPU)

  1. Mount your google drive

from google.colab import drive drive.mount('/content/drive')

  1. In the first cell run

%cd /content/drive/MyDrive/Fire_Mapping_Project

Training

!python train.py

  • Additional arguments can be supplied

Use of additional arguments

Example form of --arg option !python -W ignore train.py --net attn_unet --epochs 200 > output_attn_unet.txt

  • Specify model type --net

    • unet
    • attn_unet

  • Specify number of epochs --epochs

    • Default 100
    • Any int value

  • Batch-size --batch_size

    • Default 8
    • Any int value

  • cutoff --cutoff

    • Default 0.3
    • Any float value

  • learning rate --lr

    • Default 0.001
    • Any float value

  • Print loss values every loginterval epochs `--loginterval`

    • Default 1
    • Any int value

  • Transform data during training mode --transform_mode

    • Default Basic basic
    • Transform transform

  • Transform type --transform_types

    • Default Crop crop
    • Horizontal Flip hflip
    • Vertical Flip vflip

  • Set training seed --seed

    • Default 10
    • Any int value.

Alternative fast setup

  • After putting the project in the root directory in the google drive + mounting.
  • Go to Google colab and go File > Upload notebook > (change to the Upload tab) > Choose File.
  • Choose the Main_train.ipynb or Main_Train_AUC.ipynb.
  • Make sure the Session runtime is set to GPU (see above).
  • Run all cells.

Adding augmented images

  • Edit aug.py and add desired transforms at the top of the code. This utilizes the Albumentations package
  • Determine file names during the saving stage located at the bottom of the code
  • Run aug.py
  • Copy and paste generated images located at augmentedimages onto trainimages
  • Do the same for generated masks, from augmentedmasks onto trainmasks

Fire Prediction

  • Open Wild_Fire.py with text editor to select image, image path and initial conditions
  • Run Wild_Fire.py when satified.

Owner

  • Name: AJ
  • Login: jaina1008
  • Kind: user

Data Scientist. ML Researcher. UoA MDataSci Graduate.

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Jain
    given-names: Anshul
  - family-names: Abiad
    given-names: Jonathan
  - family-names: Yu
    given-names: He
  - family-names: Chang
    given-names: Carl
title: "Bushfire Detection and Mapping"
date-released: 2021-10-22
url: "https://github.com/FoundingTitan/Fire_Mapping_Project"

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