Data Fusion and Compilation

Data Fusion and Compilation

We specialize in data fusion and the compilation of information from diverse sources, including satellites, aerial surveys, and more. Our expertise lies in bringing together data from multiple sources and enriching it with our features to create comprehensive and integrated datasets. Currently, we provide data on a grid of 1km x 1km, with future models working with predictions on a 100m x 100m grid for any region. This allows us to provide a holistic view of geographical and weather-related information.

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Data Platform with Modern Technologies

Data Platform with Modern Technologies

GeoMapVision provides access to a cutting-edge data platform. Currently, we provide data on a grid of 1km x 1km, with future models working with predictions on a 100m x 100m grid for any region. This platform offers:

• API Access: Users can access our enriched data and visualizations through modern APIs, making it easy to integrate our insights into their applications and systems. We utilize QGIS Server to serve our geospatial data, which works seamlessly with PostGIS for spatial database management and MapProxy for efficient data caching and transformation.
• Daily Updates: Our data platform is updated daily, ensuring that you have access to the most current information for your decision-making. By leveraging QGIS Server, we can provide dynamic and interactive maps that are always up-to-date.
• Risk Calculations: We provide risk calculations based on the data received on a daily level. This includes assessing risks such as fire, floods, and other environmental factors, helping you make proactive decisions to mitigate these risks. Our platform’s integration with QGIS Server and PostGIS ensures that these calculations are accurate and based on the latest data. Additionally, we offer machine learning models and consulting services to help you enrich your data and make the most informed decisions possible. Machine learning is utilized in most of our processes to enhance the accuracy and predictive power of our analyses.
• Pest Mapping for Agriculture: Our platform offers pest mapping for agriculture, enabling farmers to monitor how insects spread through their fields. We analyze factors like foliage in the area to provide insights that support pest control strategies. The use of QGIS Server allows us to create detailed and interactive pest maps that are easy to understand and act upon.

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Geographical Information Visualization

Geographical Information Visualization

We specialize in the visualization of geographical information. Our team is dedicated to turning geographic data into engaging and informative visuals. Whether it’s mapping terrain or analyzing land use patterns, we enrich data with our features to make geography come alive.

Currently, we provide data on a grid of 1km x 1km, with future models working with predictions on a 100m x 100m grid for any region.

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Machine Learning and IoT Integration

Machine Learning and IoT Integration

GeoMapVision harnesses the power of machine learning (ML) to analyze and interpret data, making our visualizations more intelligent and predictive. We integrate data from IoT sensors that collect all kinds of weather information in real-time. Currently, we provide data on a grid of 1km x 1km, with future models working with predictions on a 100m x 100m grid for any region. Based on our extensive experience in monitoring fires and analyzing fire-related data, we create machine learning models that enhance our predictive capabilities. By combining ML algorithms with IoT data, we enrich data with our features to offer advanced weather insights and forecasts that are accurate and up-to-date. Machine learning models are a core component of our processes, ensuring that our analyses are both robust and scalable.

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Weather-Related Data Visualization

Weather-Related Data Visualization

Weather plays a significant role in various aspects of our lives. GeoMapVision is at the forefront of visualizing weather-related data, especially when it’s linked to geographic regions. Our primary focus is on predicting fires. Based on our extensive experience in monitoring fires and analyzing fire-related data, we create machine learning models that enhance our predictive capabilities.

Currently, we provide data on a grid of 1km x 1km, with future models working with predictions on a 100m x 100m grid for any region. We take into account many parameters that go into predicting the risk of fire and the Fire Weather Index, such as:

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