Name of organisation
Ready for implementation
About the project
Business intelligence platform for the utilization of satellite images for surveillance and management purposes. We process and analyze satellite imagery using machine learning models to provide added value for decision management systems. We offer possible adaptation for different use cases (agriculture, forestry, water resources etc.) combining in-situ IoT and custom (user) data.
Pairing satellite data with sensor data eliminates the risk of false positive results and improves the accuracy of classification and prediction results. Data from both sources is fed into the machine learning models which will consult the users through their computers or mobile phones of certain actions that need to be performed. Our project aims to develop an innovative, scalable and accurate solution to produce value-added maps for planning of agricultural, forestry and other activities, using EO, sensors, in-situ data and specific machine learning algorithms.
A main technical advantage of our solution is the machine learning multi-criteria analysis based on data provided by sensors and earth observation data. Correlating the mentioned data sources will provide a powerful and stable analytical tool, even capable of automating the decision-making processes in the future. The application is being developed using the following technologies: Python with Django framework and GeoDjango extension (for handling server-side code and serving templates), PostgreSQL database with PostGIS extension for handling spatial data, and React.js (with OpenLayers or Leaflet.js for web maps) for client-side rendering. Aspects of technical interoperability of our solution include interface specifications, interconnection services, data integration services, data presentation and exchange, and secure communication protocols. Since we are developing a Software as a service (SaaS) solution, end-users will be able to purchase a subscription plan to use an app for monitoring their land and receiving notifications about potential risks.