Project SLOPE is developing a forest information system to optimize timber harvesting and supply chain operations. The system will integrate real-time data on tree sizes, product distributions, and harvesting machine positions. It aims to develop modules for inventory data, real-time supply chain control, online purchasing and invoicing, and short and long-term optimization. Partners will utilize existing solutions like MHG Biomass Manager and develop new applications to track harvesting data, manage transportation logistics, and facilitate online commerce between producers and buyers. The system seeks to strengthen industry linkages and competitiveness through information sharing.
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Image Mapping for Encroachment Details
Geolocation Information embedded in model allows to have accurate mapping of Image in Terrain orSurface
Digital représentation of Forest geographywith Latitude and Longitude Information
Detailed Mapping of Encroached properties with Information and its cover
Time bound Encrochement display anddashboarding for future reference
Forest Structure and Catégorisation
Ground Elévation Comparison andcanopy Comparison
Project SLOPE is developing a forest information system to optimize timber harvesting and supply chain operations. The system will integrate real-time data on tree sizes, product distributions, and harvesting machine positions. It aims to develop modules for inventory data, real-time supply chain control, online purchasing and invoicing, and short and long-term optimization. Partners will utilize existing solutions like MHG Biomass Manager and develop new applications to track harvesting data, manage transportation logistics, and facilitate online commerce between producers and buyers. The system seeks to strengthen industry linkages and competitiveness through information sharing.
The document outlines Vision: Efficient Wood Supply 2025, which aims to make wood supply 30% more cost-efficient by 2025. It discusses opportunities and threats in wood supply, as well as success factors. Key areas of focus for R&D include increasing wood production profitably, cost-efficient forest management, improving logistics efficiency, and utilizing digital technologies like big data, automation, and decision support systems.
Image Mapping for Encroachment Details
Geolocation Information embedded in model allows to have accurate mapping of Image in Terrain orSurface
Digital représentation of Forest geographywith Latitude and Longitude Information
Detailed Mapping of Encroached properties with Information and its cover
Time bound Encrochement display anddashboarding for future reference
Forest Structure and Catégorisation
Ground Elévation Comparison andcanopy Comparison
The document describes a student project to develop an autonomous agricultural vehicle for weed removal. Key aspects of the project include designing the vehicle's mechanics, electronics, and software to allow for navigation between crop rows using GPS and autonomous weed detection and removal without pesticides. Experimental results showed the vehicle could navigate accurately between crop rows as narrow as 250mm using in-wheel motors and an RTK GPS system for positioning. The project aimed to create a flexible platform that could be used to test new sensor and control technologies for precision agriculture.
This document discusses how IoT can enable smart farming and food systems. It describes an ecosystem of apps that can push data between farmers, equipment, weather services, and government agencies to optimize activities like pesticide spraying. The IoF2020 project aims to demonstrate IoT business cases across the food sector through large-scale trials. It will integrate available technologies, address user needs, and establish an IoT ecosystem to facilitate large-scale adoption of IoT in European agriculture and food.
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BigDataEurope: Project Introduction @ Year #1 WorkshopsBigData_Europe
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The document discusses the development of a forest information system called Project SLOPE. It involves 5 work packages, including the development of a database to support novel inventory data (WP5.1), a platform for near real-time control of forest operations (WP5.2), and an online purchasing/invoicing system for industrial timber and biomass (WP5.3). It will also include the development of modules for short-term optimization (WP5.4) and mid-long term optimization/strategic planning (WP5.5). The system will integrate data on timber quality, quantities, and origin to optimize procedures and avoid delays. It will also facilitate long-term forest planning, simulations, optimization, and
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The document outlines the tasks and timeline for the development of a forest information system from August 2014 to January 2016. It involves 6 tasks: 1) developing a database to support novel inventory data, 2) a platform for near real-time control of operations, 3) online purchasing and invoicing of timber and biomass, 4) short-term optimization of operational planning, 5) mid-long term optimization of strategic and tactical planning, and 6) communication and risk management. Key deliverables are due between months 8 to 28 with bi-monthly project meetings to ensure the strict schedule is maintained through open communication and information sharing.
This document summarizes the key findings of the EFFORTE Business Forum held in Helsinki on June 19th, 2019. The EFFORTE project involved 23 partners from 5 countries working to increase the efficiency and sustainability of forestry operations through research in 3 areas: 1) soil mechanics and trafficability, 2) efficient silviculture, and 3) applications of big data. The project ran from 2016-2019 with a total budget of 4.15 million euros. Research activities included experimental studies of forest soil types in Finland and France, as well as developing models and applications to optimize operations related to soil assessment, planting, young stand management, and harnessing various data sources like remote sensing. Overall the collaboration between researchers and
BigDataPilotDemoDays - I BiDaaS Application to the Manufacturing Sector WebinarBig Data Value Association
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Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
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1. ForestTECH 2021/22, 24/Feb/2022
Stora Enso experience with
precision forestry and mechanized
solutions for silviculture
Marcos Wichert
VP Forest Division
2. Planting mechanization level
Increase the mechanization level in silviculture with new
equipment with onboard sensors and technologies
Example of a Forestry Research Institute (IPEF) survey with 9 main
Brazilian forestry companies, with 8 levels of mechanization.
Example of planting operation mechanization level
Overall silviculture mech. level of the companies
MANUAL
MANUAL
Level 1 Level 2
Level 1 Level 2
COMPANY
COMPANY
Average
Average
SEMI-MECH.
SEMI-MECH.
Level 3 Level 4
Level 3 Level 4
MECHANIZED
MECHANIZED
AUTOMATED
AUTOMATED
Level 5 Level 6
Level 5 Level 6 Level 7 Level 8
Level 7 Level 8
Source: Guerra, S.P.S, IPEF, 2020
2 Marcos Wichert – ForestTECH 2021/22, 24/Feb/2022
• Finland: < 5% planting mech. level (around 31 mec. planters: Risutec, Bracke, M-planter)
• Sweden: < 1% planting mech. level (around 10 mec. planters: Bracke)
https://www.ipef.br/publicacoes/nivel-de-mecanizacao-2020-
2021/LevantamentoDoNivelDeMecanizacaoNaSilvicultura2020-2021BaixaResolucao.pdf
3. Mechanized planting
Innovation from Sweden with Plantma-X planter machine
3
Marcos Wichert – ForestTECH 2021/22, 24/Feb/2022
https://www.youtube.com/watch?v=HVXWnBXzxmw
https://www.youtube.com/watch?v=0TSW5Ove4OE
For more information you can contact the Plantma team
from their website https://plantmaforestry.com/
Planter main features and operation:
1. Soil preparation (3 options)
• Continuous mound
• Scraping lightly soil surface
• Intermitent mounding
2. Mound compaction with rear boggie
3. Planting seedlings with planting unit
and lateral compaction
• Spacing between the lines can
vary from 1,76m to 2,74m
• Spacing between the
seeglings can also vary
• In Finland and Sweden
planting density is typically
around 1.800 to 1.900
seedlings/ha
Optimum operating speed 1,8-2,0 km/h
Productivity in optimum ground
conditions: 2.500 seed./h (spacing 1,7m)
or 2.700 seed./h (spacing 1,4m)
5. Stora Enso – our ambition
We add value to our clients offering renewable materials
supporting their journey into the circular-economy
5
Everything that’s made with fossil-based materials
today can be made from a tree tomorrow
Marcos Wichert – ForestTECH 2021/22, 24/Feb/2022
6. Serving markets
around the world
Stora Enso in Brief
6
Present in Australia with
our Wood Products
Division
Marcos Wichert – ForestTECH 2021/22, 24/Feb/2022
7. Wood Products presence in Australia
Stora Enso in Brief
7 Marcos Wichert – ForestTECH 2021/22, 24/Feb/2022
2021 Operations:
• 4 terminals in main
South-Eastern
population hubs
• 6.000 containers
unpacked at Stora
Enso sites, stored
and redelivered to
customer
Products available:
• Structural Framing
• LVL (laminated veneer
lumber) *
• GLT Studs (Glue
Laminated Timber or
Glulam) *
• Weatherboards
• T&G Flooring, Linings
• Dressed Boards
• F/J Components
• CLT (cross laminated
timber) *
If you like building construction with wood, check
more information on our online catalog below:
Wood Products Flagship Brochure 2020
(storaenso.com)
* main products used for mass timber construction
8. Main Wood Products mass timber projects in Australia
Library at the Dock, Melbourne
2013
International House
Sydney, 2016
Jordan Springs, Sydney
2017
25 King St, Brisbane
2018
Australian National University, Canberra
2018
Daramu House Sydney
2019
Melbourne Connect
2020
Stora Enso in Brief
9. Our approach to digital innovation in our processes
9
Innovation culture Operating model Technology access
https://youtu.be/0mcP_kJqbRE
Dedicated IT team of 258 professionals
developing applications and systems to
support the operations and processes
Marcos Wichert – ForestTECH 2021/22, 24/Feb/2022
10. Stora Enso New Technologies & Innovations
Eco-RFID for packaging tracking – divested to CCRR Oct/21
10
Radio-Frequency Identification (RFID)
Marcos Wichert – ForestTECH 2021/22, 24/Feb/2022
Eco-RFID for Intelligent cabinets
Unmanned stores
11. Stora Enso New Technologies & Innovations
Focus on supporting digital solutions for new retail
11 Marcos Wichert – ForestTECH 2021/22, 24/Feb/2022
12. Stora Enso Packaging Automation
Automatic plant packer solution for tree nurseries
12 Marcos Wichert – ForestTECH 2021/22, 24/Feb/2022
https://www.storaenso.com/en/newsroom/news/2020/4/eff
ektiv-och-hallbar-forpackningsprocess-med-plantor-i-well
This automated packaging solution is available for tree
nurseries globally (not possible for bare rooted seedlings), for
more details contact Thomas Sundling, our Head of
Packaging Automation
thomas.sundling@storaenso.com
In the Nordic countries most
seedlings (mainly Scots Pine
and Spruce) are disptached to
the field on special corrugated
cardboard boxes.
13. Stora Enso New Technologies & Innovations
Digital wood trade platform – example digital technologies
13
• Around 72% of our annual wood supply comes from purchased wood.
• We are introducing a digital wood trade platform to make it easier and more transparent the
wood purchase negotiations and deals with timberland owners in Europe.
2021 wood procurement by region,
total 37.6 million m3
Marcos Wichert – ForestTECH 2021/22, 24/Feb/2022
14. Stora Enso forests geographical area
Stora Enso’s productive forest land areas
Source: Stora Enso 2021 annual report
14
Second largest private forest owner globally, with 2,01 M ha of productive forest
land. Annual harvested volume of 10,4 M m3. Total land and biological asset value
in balance sheet EUR 8.0 billion.
Marcos Wichert – ForestTECH 2021/22, 24/Feb/2022
99 %
15. Satellite
Virtual Forest
Mobile solutions
Forest resource data applications
Condition information
Harvesting
Wood quality
Trestima
Digitalization used to generate a “digital twin” of the forests
Drone
Forest resource data
Public open data
LiDAR
Satellite forest
monitoring service
Forest Data Platform
Drone tree health
detection service
Stem diameter
distribution
16. Mechanized harvesting:
Better planning and
monitoring, increased
efficiency and alignment with
end use
Geospatial optimization:
Accessibility and conditions
data; utilisation of sensor
technology in data collection
Site specific management:
Nutrition and maintenance
planning within each stand
based on site specific conditions
Disturbance monitoring:
Use remote sensing to
identify instances of
pest, diseases, fire, etc.
Predictive analytics for yield
maximization: Harness the
power of big data collected
through IoT sensors, UAVs and
traditional data sources to
predict field interventions for
maximizing yield
Forest planning model:
Integrated planning from company-wide
strategic to daily within stand operational
planning
Mechanized silviculture:
Mechanization and automation of soil
preparation, planting and maintenance
for higher efficiency and yield
Digital inventory measurement:
Tree-level digital inventory
measurement using remote
sensing
Value chain optimization:
Production optimization (e.g.
in sawmills) based on tree-
level resource data
Forest infra:
Advanced optimisation to
minimise wood cost in the
long term
16
Precision Forestry Areas Overview
Vision and strategy of precision forestry
Next 4-5 years road map is to have 100% digital data collection from the field, with centralized
database and utilization of automated analysis and reports, with the use of “big data” and “predictive
analytics”, optimizing the forest value chain process
ÅF PÖYRY AB / TOWARDS PRECISION FORESTRY STORA ENSO PROJECT
Marcos Wichert – ForestTECH 2021/22, 24/Feb/2022
17. ÅF PÖYRY AB / TOWARDS PRECISION FORESTRY STORA ENSO PROJECT
17
Improving performance with optimization and analytics
Common forestry data centralized platform, more use of
big-data and analytics (automated reports)
UNITS
Stora Enso
Precision forestry
data
Operational management
Digital purchasing and forest
owner services
Harvester data,
silviculture data, ops
data feedback
AI engine
Simulation and
optimisation platform
Analytics platform
(predictive analytics)
Satellite, Lidar,
photogrammetric and
manual inventory data
Public/national data
sources
Harvest site planning
on the field
Accessibility
DATA
SOURCES
SOLUTIONS
Soil, topography,
weather
Customer portal
Tactical and strategic planning
Decision support in value
maximisation
Ongoing development (forest data service):
- Drone data processing
- Diameter distribution modelling
- Remote sensing data processing
Data ownership. And data
monetization opportunity
in the future
Marcos Wichert – ForestTECH 2021/22, 24/Feb/2022
18. ÅF PÖYRY AB / TOWARDS PRECISION FORESTRY STORA ENSO PROJECT
18
Data flow and architecture
The precision forestry platform creates the capacity to process
large volumes of data expected across all units in an efficient way
Marcos Wichert – ForestTECH 2021/22, 24/Feb/2022
19. Precision Forestry - Challenges and future needs
Critical components to successfully implement new
technologies
• Right skills and competencies
• Capability building
• Change management
• Transform more data available into intelligence, improving decision making
• Critical thinking and complex problem solving capacity
• After sales technical support and solutions development (new tech. provider)
20%
30%
50%
Precision technologies (digitalization)
Processes
People
• Integration in current business processes and IT database systems (cloud)
• Data quality, availability and updating schedule
• Data analytics (algorithms, big data, machine learning, AI)
• Data and computational platform (data collection and transmission)
• Connectivity to send data
• Visualization, analysis and restitution tools
• Data engineering
19 Marcos Wichert – ForestTECH 2021/22, 24/Feb/2022
20. Precision Forestry - Challenges and future needs
New technologies increase exponentially and new skills are
needed
Source: Dominguez, J., Sep 2018. Engineering Education for XXI.
1. Complex problem solving
2. Coordinating with others
3. People management
4. Critical thinking
5. Negotiation
6. Quality control
7. Service orientation
8. Judgement and decision making
9. Active listening
10. Creativity
World Economic Forum (2015) World Economic Forum (2020)
Difficult for Universities to catch up with the
speed that new technologies are introduced.
As the world becomes more complex, new skills and competencies are required from the
workforce for the successful performance of the companies in the future.
20
Covid19 impact: Adaptability and Resilience!
Marcos Wichert – ForestTECH 2021/22, 24/Feb/2022
21. Precision Forestry - Challenges and future needs
Main technology drivers for the future of precision forestry
Robots &
automation
Increased autonomous machine operation in the field, remote
teleoperation of machines, and robots in the nurseries
Cloud
systems
Real time communication between operational
systems. Big databases in open systems
Augmented
reality
Support from augmented reality for
machine maintenance, and display of
operational standards/manuals
Cyber-security
Guarantee data safety in open systems
and integrated networks, and also data
transfer from machines
IoT and
connected
sensors
Network of smart forestry sensors with
machines. Multi-directional communication
between networked objects
Optimization &
simulation
Optimization systems utilizing real-time data updates from database.
Simulation of best solution considering the impact in the whole value chain
Big-data &
analytics
Real time decision making support and
optimization based on full evaluation of
available data from all systems (e.g ERP, SCM,
CRM, etc.) and machine data
Source: Adapted from McKinsey & Company, June 2018. Precision forestry a revolution in the woods.
Data
integration &
monetization
Horizontal and vertical data integration based
on data transfer standards. Needed before a
fully automated value chain implementation is
done and also monetization of databases in
the value chain will be possible
21 Marcos Wichert – ForestTECH 2021/22, 24/Feb/2022