Opendata, web and dolomites

forecast

The next generation of forest inventory

Total Cost €

0

EC-Contrib. €

0

Partnership

0

Views

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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.

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

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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