First, we have these satellites called Sentinel-2 that take pictures of the earth from space with a resolution of 10 meters per pixel! These images are really detailed which is great because it means we can see all sorts of things like wildfires and stuff. But here’s where deep learning comes in instead of manually looking at these images to find fires, we use an algorithm called ResNet-50 to do the heavy lifting for us. This algorithm basically learns how to identify patterns in the data (in this case, satellite imagery) that are indicative of wildfires. And the best part? It gets better and better at identifying these patterns over time as it processes more and more images!

So essentially what we’re doing is training a computer model to recognize wildfires based on their unique characteristics in satellite data. Pretty cool, right?!

Now let me give you an example of how this works in practice. Let’s say we have two images one that shows a normal forest and another that shows the same forest after it has been burned by a wildfire. If we feed these images into our ResNet-50 model, it will be able to identify the differences between them based on things like changes in color or texture. And because this algorithm is so good at recognizing patterns, it can even detect small fires that might not be visible to the naked eye!

In terms of context, we’ve created a big dataset for Turkeys wildfires using Sentinel-2 multiband images to aid the development of remote sensing or computer vision-based models for image segmentation, object detection, and classification concerning wildfires. Binary classification for burned-area and non-burned-area detections is supported in this dataset. Various experiments were conducted in this work to evaluate the performance of wildfire monitoring and detection using our dataset, and they achieved great results with the DL segmentation models. We compared 14 deep-learning models based on combinations between five architectures (U-Net, U-Net++, Attention ResU-Net, LinkNet, and DeepLabV3+) and four encoders (ResNet101, ResNet50, ResNet152, and MobileNet) for U-Net and LinkNet. U-Net with a ResNet50 encoder, Attention ResU-Net, and U-Net with a ResNet101 encoder models achieved the best results in IoU and F1-score metrics. We developed a

However, it should be noted that while deep learning has shown great promise in this area, there are still some limitations to consider. For instance, the superiority of deep learning for NIDS is not yet proven, as demonstrated by studies such as References [134] and [166]. These studies show conflicting results regarding the effectiveness of shallow versus deep ML methods on the same dataset (CICIDS17). Therefore, it’s essential to continue researching and refining these techniques in order to improve their accuracy and reliability.