Deep Learning applied to early detection of forest fires

La Paz. Second Edition. 2021

In the world, forest fires are the most important cause of forest destruction. In a forest fire, not only trees and bushes are lost, but also houses, animals, sources of work and even human lives.

As can be seen in Fig. 1 in Bolivia, in the last seven months this phenomenon has alarmingly multiplied the affected area due to multiple factors. Fig. 2 shows some of the causes studied for forest fires. Another important factor identified is climate change, which due to the increase in temperature in the affected places, increases the affected areas alarmingly.

Fig. 2 Identified causes of forest fires (Vélez)

Due to the consequences of uncontrolled or unplanned forest fires, different strategies must be taken to mitigate their occurrence in protected or prohibited areas for this activity.

Problem description

Based on what was mentioned above, the following problem arises:

How to identify uncontrolled forest fires in a timely manner to minimize their impact?

General objective

The general objective is to implement a model of Deep Learning for early detection of forest fires by recognizing smoke in flat/forested areas using images of the scene.

The following graphic shows the central idea of the project.

Fig. 3 Project proposal

Information collection

The dataset to train the models was created by downloading images classified as “forest fire” (images taken in perspective with the presence of smoke in forest areas) and “non-forest fire” (images taken in perspective in forest areas without the presence of smoke or fire).

The downloaded images (4 groups of images) were taken to a repository github for easy import into Google Colab.

Print of the first 8 training images and first 8 validation images:

Model selection

The selection of models is based on the following considerations:

• Ease of implementation.
• Model performance.
• Calculation limitation.

These considerations are due to the resources available to achieve the general objective. The models to be developed are:

• CCN Custom
• VGG-16

Techniques implemented

The main technique used to compensate for the limited dataset size due to the limitation of Google image search was “data augmentation” made by the bookstore keras “ImageDataGenerator”.

Fig 5. Data Augmentation

Fig. 6 ImageDataGenerator in Keras

Model evaluation

1. Custom CNN

2. VGG-16

Analysis of results

Below are the results of the model Custom CNN and VGG-16.

1. Precision and ROC curve of cnn custom:

Custom CNN ROC Curve

2. Precision and ROC curve of VGG-16:

Accuracy of VGG-16.

ROC curve of VGG-16.

Model predictions below:

1. Custom CNN

2. VGG-16

Conclusions

The model cnn customized has adequate performance for the detection of forest fires. The model VGG-16 with the pre-workout element requires more elaboration to obtain more accurate results. In this way a model Deep Learning It does not always require high complexity to perform classification efficiently.

The developed model obtains very good forecasts for the problem posed and is a complementary solution to the problem of forest fires.

The use of _AI Deep Learning_g models can be better exploited as a complement to the solution of current problems.

Members

• Daniel Blanco

Project presentation: DemoDay

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