Opendata, web and dolomites

forecast

The next generation of forest inventory

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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 forecast project word cloud

Explore the words cloud of the forecast project. It provides you a very rough idea of what is the project "forecast" about.

managers    service    provides    forecast    plots    limited    intensive    paper    calibration    fora    quality    plantations    wood    sensors    efficiency    minimum    sampling    optimal    timber    forestry    rely    double    schemes    completing    techniques    satellite    resolution    sustainable    companies    disadvantages    technologies    costly    huge    maintaining    assignments    estimating    inventory    airborne    stand    innovation    labour    conservation    paramount    local    species    inherent    organisation    combine    safety    concerned    tree    plans    bunch    learning    woods    sustainability    return    deep    alone    inventories    operations    productivity    geospatial    forest    algorithms    remote    cons    recreation    heavily    combining    basis    forests    accurate    sensing    volume    estimation    reducing    ai    plays    optical    mainly    proprietary    generation    models    services    operate    individual    biomass    mapping    optimize    cover    mills    attributes    bottleneck    imagery    lidar    harnessed    radar    data    crews    time    forefront    pros    policie    area    ground    pioneered    solution   

Project "forecast" data sheet

The following table provides information about the project.

Coordinator
FORA FOREST TECHNOLOGIES SLL 

Organization address
address: C/ORESTE CAMARCA, 4 4B
city: SORIA
postcode: 42004
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Spain [ES]
 Project website https://forecast.fora.es
 Total cost 71˙429 €
 EC max contribution 50˙000 € (70%)
 Programme 1. H2020-EU.3. (PRIORITY 'Societal challenges)
2. H2020-EU.2.3. (INDUSTRIAL LEADERSHIP - Innovation In SMEs)
3. H2020-EU.2.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies)
 Code Call H2020-SMEInst-2018-2020-1
 Funding Scheme SME-1
 Starting year 2019
 Duration (year-month-day) from 2019-05-01   to  2019-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    FORA FOREST TECHNOLOGIES SLL ES (SORIA) coordinator 50˙000.00

Map

 Project 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

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The information about "FORECAST" are provided by the European Opendata Portal: CORDIS opendata.

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