Description: Wälder sind für die Bekämpfung des Klimawandels und die Erhaltung unseres Planeten von entscheidender Bedeutung. Forstwirtschaftliche Unternehmen und Organisationen überwachen die Wälder und unterstützen deren Wachstum, Holzproduktion und Pflege. Leider kann die Überwachung großer Waldflächen schwierig und ungenau sein, was für die ordnungsgemäße Erhaltung der Waldgesundheit gefährlich ist. Das EU-finanzierte Projekt DIGIFOREST stellt eine innovative Lösung vor, die die Überwachung und das Datenmanagement für Wälder mithilfe von Robotern und künstlicher Intelligenz verbessert. Durch den Einsatz verschiedener Roboter zur Erfassung von 3D-Raumdaten über Wälder und einzelne Bäume und die anschließende KI-Dokumentation und KI-Darstellung bietet es Forstunternehmen und Verantwortlichen der Politik ein unvergleichliches Instrument.
Types:
SupportProgram
Origins:
/Bund/UBA/UFORDAT
Tags:
Baum
?
Waldfläche
?
Holzproduktion
?
Künstliche Intelligenz
?
Klimaschutz
?
Waldpflege
?
Waldzustand
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Europa
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Datenmanagement
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Sozialwissenschaft
?
Wald
?
political sciences
?
political transitions
?
revolutions
?
Region:
Bavaria
Bounding boxes:
11.5° .. 11.5° x 49° .. 49°
License: cc-by-nc-nd/4.0
Language: Deutsch
Organisations
Time ranges:
2022-09-01 - 2026-02-28
Alternatives
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Language: Englisch/English
Title: Digital Analytics and Robotics for Sustainable Forestry
Description: What if we could create a revolution in spatial data acquisition, organization and analysis and give forestry operators and enterprises up-to-date, tangible information about the status of their forests down to the individual tree? We believe this would improve their oversight by allowing more accurate growth modelling of forest stands and precise predictions of timber yields. It would remove the uncertainty of when thinning operations are needed or where there are trees which are ready for harvest. It could also enable operators to automatically plan where their staff or equipment should be deployed. With capable (semi-)autonomous harvesting, operators eventually automating the full process. It could also better quantify a forest's carbon sequestration - with low uncertainty per-tree carbon estimates. Precise measures of crown volume and tree diameters would improve the granularity of carbon credit schemes. This could inform national governments and policy makers when deciding policy on initiatives such as carbon offsets and carbon farming.In DIGIFOREST we propose to create such an ecosystem by developing a team of heterogeneous robots to collect and update this raw 3D spatial representations, building large scale forest maps and feeding them to machine learning and spatial AI to semantically segment and label the trees and also the terrain. Our robot team will be diverse: we will use both rugged field robots as well as more experimental vehicles. Most ambitious of all is the intention to (semi-)automate a lightweight harvester for sustainable selective logging.Progress in this project will be demonstrated with an ambitious series of field trials. With the clear engagement of forestry and industrial companies, commercial pathways are readily available.A 1:15 video summarizing the overall project ambitions and consortium can be viewed here:https://tinyurl.com/digiforest
https://ufordat.uba.de/UFORDAT/pages/PublicRedirect.aspx?TYP=PR&DSNR=1139034
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