Detecting Wildfires Using CNN Model with 95% Accuracy

Preventing wildfires by using a CNN Model. An algorithm that detects wildfires via daylight images of a particular region.

Omdena

April 28, 2025

6 minutes read

The year 2019 was marred by a series of devastating fires that captured global attention. In addition to tragic blazes at iconic landmarks such as Paris’s Notre‑Dame Cathedral and Brazil’s National Museum, vast ecosystems like the Amazon forest Wildfires and, more recently, areas of Australia suffered widespread destruction. These catastrophes underscore the urgency of developing sophisticated tools for early detection and intervention. This article recounts how a community‑driven initiative harnessed Convolutional Neural Networks (CNNs) to build a cnn wildfires detection system that achieves 95 percent accuracy while maintaining a comprehensive recall.

Why Wildfires Spread

Understanding the causes and consequences of wildfires provides critical context. Ignition sources can be natural—most commonly lightning strikes leading to spontaneous combustion—or human‑caused: smoking, recreational mishaps or land preparation for agriculture. While people start more fires overall, natural events tend to burn larger areas because they often go unnoticed for longer. Once a forest like the Amazon ignites, flames can race at 23 km/h and reach temperatures of 800 °C (1 470 °F), destroying plant and animal life within a few hours. In addition to the immediate ecological toll, fires release vast amounts of CO₂, contributing to global warming and degrading air quality in distant urban centers such as São Paulo.

One striking example of this impact occurred on 19 August 2019. Around three o’clock that afternoon, residents of Brazil’s largest city watched the sky turn black as a cold front collided with smoke drifting from the Amazon and the Midwest. The eerie daytime darkness provoked fears of a biblical‑scale plague and spawned waves of misinformation. A scientific explanation surfaced when NASA published a high‑resolution satellite image showing smoke plumes spreading toward Brazil’s southeast.

Leveraging Technology to Fight Wildfires

Sintecsys, a Brazilian company devoted to protecting farms and forests, tackles the problem of early detection head‑on. The company installs cameras atop communication towers to monitor vast tracts of land. Images are transmitted to a central monitoring facility, and when smoke or fire is detected, an alert triggers a rapid firefighting response. By 2019 Sintecsys had deployed 50 towers across Brazil, but to extend its customer reach and scale the business model to thousands of cameras capable of quickly spotting wildfire outbreaks, the company partnered with Omdena. Omdena is a global platform where organizations collaborate with a diverse AI community to build solutions for real problems in a faster and more effective way.

Collaborative Problem‑Solving: From Scoping to Data Collection

Scoping the Challenge

From the outset, Omdena and Sintecsys agreed to tackle daytime images first and then, in a subsequent phase, address nighttime images. Daytime pictures typically reveal smoke, whereas nighttime images display live flames. Dawn and dusk represent challenging boundary conditions where both smoke and flames may appear simultaneously. By clearly defining these parameters, the team set realistic goals for the initial cnn wildfires detection model.

Building and Enriching the Dataset

The dataset assembled for this challenge was substantial, comprising footage and still images from various cameras both with and without wildfire outbreaks. The team started with roughly 7 600 images at 1 920 × 1 080 pixels, including day images without fires, day images with fires and a smaller subset of night images (about 16 percent of the total). To enrich the data further, Gary Diana devised an algorithm to extract additional frames from video footage while avoiding duplicates of the same landscape. His efforts yielded 1 150 extra images at 1 280 × 720 pixels, substantially expanding the training material.

Labeling and Data Management

With images in hand, the team turned to labeling. Approximately 20 volunteers organized into dedicated roles within the Labelbox platform, which the team regarded as the premier tool for computer vision projects because it streamlines data labeling, quality management and pipeline operations. Alyona Galyeva, who supported and reviewed everyone’s work, described the process:

It always starts with a mess when a group of people collaborates on a labeling project. In our case, Labelbox saved us a lot of time and effort by not allowing multiple users to label the same data. On top of that, it made our lives easier by proposing 4 roles: Labeler, Reviewer, Team Manager and Admin. So, nobody was able to mess with data sources, data formats and, of course, the labels made by other people.

Thanks to this structured workflow, the team produced high‑quality labels. The data pipeline team then split the labeled images into training, validation and test sets, laying the foundation for robust model development.

Model Development and Experimentation

Participants surveyed several cutting‑edge research papers to explore different strategies for wildfire detection. Multiple sub‑teams worked in parallel on complementary approaches. They tested mobile net architectures for lightweight deployments, semantic segmentation for pixel‑level fire identification and a range of Convolutional Neural Network (CNN) designs—from straightforward models to more sophisticated architectures—seeking the optimal balance between performance and efficiency.

Danielle Paes Barretto reflected on this phase:

It was inspiring to see people eager to achieve great results. I tried to help in all tasks; from labeling the data to building CNN models and testing them on our dataset. We also had frequent discussions which in my opinion is one of the greatest ways of learning. All in all, it was an amazing opportunity to learn and to use my knowledge for the good while meeting great people!

To boost performance, the team applied different techniques such as creating patches of various sizes from the original images and training on those patches, performing data augmentation (including horizontal and vertical flips) and denoising images. These refinements helped the models generalize better and provided resilience against variations in lighting, smoke density and terrain.

Results and Next Steps

The final solutions achieved impressive metrics: they captured between 95 and 97 percent of actual wildfire incidents (recall) while limiting the false positive rate to 20 – 33 percent. This means the system was highly adept at identifying fires quickly, even though some false alarms persisted. Sintecsys, the challenge partner, expressed that it is extremely happy with these outcomes. The next stage of the collaboration aims to incorporate nighttime imagery to further improve accuracy and reliability under all conditions. A related initiative also pushed the boundaries of AI-driven prevention Dryad’s early-wildfire detection solution, built with Omdena’s Top Talent team, demonstrates how intelligent sensing and AI models can work together for even faster intervention.

Key Outcomes

High recall: Captured between 95 % and 97 % of actual wildfire incidents.
Moderate false positives: Limited false alarms to 20–33 %.
Future growth: Plans to integrate nighttime images and expand capability.

Conclusion

This project demonstrates how a collaborative, community‑driven approach can address real‑world challenges with advanced machine learning techniques. By meticulously curating a diverse dataset, deploying a robust labeling strategy and iteratively refining CNN architectures, the team developed a wildfire detection model that performs with remarkable accuracy. The success of this cnn wildfires initiative shows that technology, when coupled with collective effort, can play a decisive role in safeguarding our ecosystems and communities.

Modernize your wildfire monitoring with CNN models designed for accuracy in the field by connecting with Omdena for a hands-on innovation partnership.

FAQs

How does a CNN model detect wildfires from camera images?

CNN models detect wildfires by analyzing patterns in camera images—such as smoke texture, color shifts, or movement. In cnn wildfires projects like the Sintecsys–Omdena challenge, the model learns these patterns from thousands of labeled images, enabling it to spot early smoke with 95% accuracy.

Why are CNNs effective for wildfire smoke detection?

CNNs excel at image recognition. They extract features like edges, shapes, and gradients, making them ideal for distinguishing real wildfire smoke from clouds, fog, glare, or boiler emissions. This makes cnn wildfires systems more reliable than manual observation.

What kind of dataset is needed to train a cnn wildfires model?

A cnn wildfires model requires a diverse dataset with images showing smoke, non-smoke scenes, partial smoke, glare, fog, clouds, and various landscapes. In the project, the dataset included 7,600+ images, enhanced with 1,150 additional video frames, and carefully labeled using Labelbox.

How accurate was the wildfire detection model developed in the article?

The cnn wildfires model achieved 95–97% recall, meaning it detected nearly all real smoke events. It kept false positives between 20% and 33%, which is strong performance considering the visual complexity of outdoor scenes.

What challenges arise when training CNN models for wildfire detection?

Training cnn wildfires models is challenging because smoke can resemble clouds, fog, dust, or glare. Low-resolution images also reduce clarity. The team addressed this using label smoothing, upsampling for better image quality, and data augmentation to improve model generalization.

Why did the team focus on daytime wildfire detection first?

Daytime images primarily show smoke, making them ideal for training early versions of the cnn wildfires system. Nighttime detection requires models trained on flames and infrared features, which was planned for a later phase of the project.

What impact can CNN-based wildfire detection systems have on communities?

CNN wildfire systems enable faster detection—helping firefighters respond before flames spread. This reduces burned acreage, protects farms and forests, cuts CO₂ emissions, and prevents smoke-related health issues. Faster response literally saves land, lives, and money.

What are the next improvements planned for the cnn wildfires system?

The next steps include nighttime flame detection, integration of satellite imagery, and predictive algorithms to forecast where fires may start — making the system proactive rather than just reactive.