Objective
Accurate mapping of tree species and estimation of wood volume and biomass are important assignments of any forest inventory. However, forestry operations currently rely heavily on field data as a basis for estimating its attributes. This labour-intensive approach provides limited information and has become a costly bottleneck in completing operations. Today, remote sensing data plays a key role to characterize forests. Generation of accurate models combining a huge bunch of data requires the use of advance AI techniques that provides real time information about woods and its resources.
fora has pioneered high-resolution and timely forest inventory services which combine state-of-the-art remote sensing technologies and deep learning to produce operational forest inventories that help improving the efficiency of forest management activities. Whether LiDAR, RADAR, and/or optical imagery, airborne or satellite, these sensors able to cover a large area for intensive sampling without the disadvantages inherent to labour-intensive ground sampling schemes done by field crews. However, each remote sensing solution has its own pros and cons, mainly to operate as stand-alone service.
FORECAST is at the forefront of how geospatial and remote-sensing data can be harnessed to optimize safety, efficiency and productivity of forest operations. Key to FORECAST innovation is the fora proprietary calibration systems based on a double application of AI algorithms.
FORECAST is the solution for forest managers and wood and paper companies, reducing the field plots to a minimum, while maintaining a high quality of information about the state of the forest at the (local) scale of individual plantations. Whether an organisation is concerned with timber, access to mills, recreation or conservation, achieving long term sustainability with an optimal return is of paramount importance for the design and implementation of effective sustainable forest management plans and forest-related policie
Field of science
- /agricultural sciences/agriculture, forestry, and fisheries/forestry
- /agricultural sciences/agricultural biotechnology/biomass
- /natural sciences/biological sciences/botany
- /engineering and technology/materials engineering/paper and wood
- /engineering and technology/environmental engineering/remote sensing
- /natural sciences/computer and information sciences/artificial intelligence/machine learning/deep learning
Programme(s)
Call for proposal
H2020-SMEInst-2018-2020-1
See other projects for this call
Funding Scheme
SME-1 - SME instrument phase 1Coordinator
42004 Soria
Spain
