To increase surveillance frequency.
To detect visible and invisible wildfires signals.
For uninterrupted service and a more efficient setup.
For continuous learning, more accuracy and speed to detect smoke plumes.
To calculate the event location and reduce the time of the initial fire strike.
In recent years, the number of scientific studies that propose innovative technologies to be used in the fight against forest fires has grown exponentially.
One of the most important takeaways from these research is that when more detection systems are deployed, the probability of detecting fire sources in their early stages increases.
Due to the environmental, social, and economic benefits of timely detection, our team has developed Firecatch, a web application that integrates three technologies:
1. Smart Teledetection: AI detection based on the monitoring of optical sensors.
2. Wildfire Simulator: an AI application that allows users to forecast the behavior of forest fires based on the circumstances of the occurrence.
3. Satellite information: hotspots and smoke columns detection in near real-time satellite images.
A detection tool that optimizes the monitoring frequency and minimizes the average detection time of forest fires is obtained using the newest technology of high-definition sensors, thermal and optical, with 360° programmed movement.
In addition to the above, optical sensors' live view is processed in real-time by our Deep Learning model trained to identify smoke plumes in this context. This Artificial Intelligence resource accelerates and precises the detection of this forest fire signs.
When a smoke plume is detected, the incident is instantly geolocated in order to send an alert to the associated Firecatch users. This platform allows you to review incoming and previous notifications, as well as their corresponding image and geographic location.
* Ideal circumstances
Smoke column detection using AI
We can use Firecatch to analyze live video feeds for wildfire detection and monitoring. Regardless of the model, if the camera has a high enough resolution, the footage can be analyzed by our AI model to detect smoke plumes and notify the appropriate users. As a result, we can locate and alert fires anywhere in the world using our Smart Teledetection service.
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In addition to Smart Teledetection system notifications, fire alerts found in satellite data will be sent on Firecatch Web via an image and their corresponding geolocations.
Geostationary satellites send near real-time images of the earth and what is happening on it. The National Office of Oceanic and Atmospheric Administration (NOAA) allows access to this information so that we can detect the presence of forest fires at a regional, national and even continental level.
This information must be prepared before being analyzed by our model, and due the huge volume of data received per unit of time, proper technologies are necessary to provide timely and reliable results.
AI, and particularly machine learning algorithms, have been demonstrated to be effective tools for processing this type and volume of data. This is why we created a new machine learning model that specializes in spotting forest fires in satellite images that show us what is happening on Earth right now.
Simulator of Forest Fire Behavior
The spread of a wildfire over time will be visualized in an interactive map featured in Firecatch Web, which will display the propagation of the fire over time, depending on topographic and climatic conditions, as well as the fuel models involved. These factors can be modified to examine multiple scenarios and combat strategies.
Forest fires are difficult to model when all of the variables involved are included. The behavior forecast tells us where and when changes will occur in the region as a result of the fire, allowing us to choose more effective plans and tactics for extinction.
Forest fire emergency management is critical for any organization and requires a methodology that allows for successful and efficient event management. As a result, Artificial Intelligence has been chosen as a tool that learns from pre-existing fires to forecast the behavior of recently discovered or yet-to-occur wildfires.
Artificial Intelligence techniques can make predictions on a specific topic with high precision once they receive a series of inputs and have been previously trained on it. However, to visualize the results, a GIS platform is necessary to generate the geospatial inputs for the simulation model and to consider the effect of topographic variables.
By detecting a fire in its early stages, control can begin valuable minutes before it spreads. A major disaster can be avoided if it is detected at this point.
Advantages of early detection:
Fire sources are easier to control.
Decrease in the affected area.
Reduced expenses for combat and restoration.
Reduced pollutant emissions.
Greater protection for nearby communities.
We are a professional team committed to the research and development of models for the detection and control of forest fires. We understand the industry's needs and the importance of a fast response, which is why we developed Firecatch, a fast and accurate early detection system for fires, combining renewable energy, automation, sensors, artificial intelligence, and messaging services.
We believe that proper application of technology can improve the conditions under which certain operations are carried out. We see Chile and the rest of the globe considering about ways to detect forest fires early, minimize large-scale destruction, and reduce pollution and greenhouse gas emissions.
Based on our respect for the environment, our constant search for comprehensive solutions, with a deep dedication to wildfire detection and the forestry industry, we are developing the best system for early forest fire detection using the best technologies and the tireless effort of our entire team.