Forestry Management

Solve today’s most pressing challenges while building competitive advantage for tomorrow.



The laborious task of manually inventorying extensive land areas often involves significant time and organization. Quite often, the precision and quality of these counts fall short of the standards foresters aim to achieve. The escalating issue of land monitoring for foresters is becoming more pronounced as labor costs rise and resources become increasingly scarce, restricting their ability to effectively count and monitor their forestry inventory. The challenge does not only lie in resource scarcity, but also in the daunting task of accurate enumeration and quality evaluation of the inventory over expansive land parcels, introducing a plethora of other complications.


Leveraging geospatial machine learning, tasks that previously necessitated foresters' extensive work hours, such as tree enumeration and inventory quality evaluation, can now be executed without consuming scarce resources or necessitating expensive organization. With the incorporation of drones, capable of mapping extensive terrains within a few days, coupled with the utilization of artificial intelligence, we have developed a model that can enumerate, locate, and appraise the quality of diverse types of trees . This innovation has facilitated foresters to supervise their inventory with diminished effort, time, and financial commitment.



The achieved results have been outstanding, not solely in terms of precision, demonstrating a 96% accuracy, but also a 90% reduction in labor hours. Furthermore, these results have facilitated an innovative approach for foresters to chart their terrains and identify individual tree species. Our techniques have proven to be remarkably beneficial to foresters, enabling tree counting and inventory quality assessment to be conducted at a large scale, and vastly simplifying the task of identifying the appropriate product for the consumer.

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251 Little Falls Drive
Wilmington, New Castle County
Delaware 19808

Science Park 608, Unit K10
1098 XH Amsterdam