The participation of our company in the European project Quick Urban Forest shows that there is real potential for the creation of green areas and recovery of urban soils without undertaking costly irrigation infrastructures by means of using advanced techniques (retainers, mycorrhizae and the mix of both treatments) that leads to sustainable growth and the survival of the plants without water addition. To prove this hypothesis were analysed more than 2 million humidity and temperature of the roots observations obtained with a sensor network for 2 years. Internet of Things (IoT) techniques, supervisory analysis, Ordinary Least Squares (OLS) and mixed models were used.
Project to evaluate the information contained in thousands of historical forestry files in different formats to obtain knowledge for helping improve future plantings. Data mining, handling and data cleaning techniques from several data sources, GIS (Geographic Information System) integration to obtain geo-spatial data, descriptive modelling and supervised learning techniques are implemented.
The customer needed technical assistance for route optimisation of its emergency forestry vehicles. 38,264 forestry routes were estimated for different kind of vehicles using 695,218 speed observations obtained from the Global Positioning System (GPS) devices installed in those vehicles. The estimations were integrated into GPS devices and installed in the customer vehicles. Hierarchical clustering techniques and supervised learning models combining multiple models were used.