Virtual forest adviser
Virtual forest adviser
Track 02: Artificial intelligence, analytics, and value from data
Tags: artificial intelligence, mobile devices, augmented reality, visualization, GIS.
Sponsor: Metsä Group
Challenge
The objective is to develop a concept of a virtual forest adviser that informs the user in a forest of the next operation that should be carried out in a particular stand.
Background
The structure of forest ownership is changing rapidly. Forest owners are urbanising, their education levels are higher, they are less inclined to act on their own initiative and the need for services is growing.
In the future, an increasingly small number of forest owners will have the time, will and skill to manage their forests. Forestry competence will also decline. Not all forest owners have a forest plan for their forests that acts as a forestry handbook.
Build an example of a virtual forest adviser concept that works on a mobile device. The virtual forest adviser should be able to measure the trees with photography, and then inform the user of the required actions and their financial impact, as well as displaying an image of the scenery after the action using augmented reality.
Approaches and considerations
Consider how the application knows that it is in a forest, and what information is available regarding that particular forest stand delineation.
Next, consider how to measure the forest visually, i.e. with mobile phone’s camera and use machine vision to analyze the photo.
Finally, the recommendations and supporting information should be presented. How does that look? How does the process work from end to end? Where all the data is coming from?
As an example of measuring standing timber take a look at Trestima https://www.trestima.com/products_en/
The result or the process could possible be linked and/or supported by GIS tools (such as ArcGIS). Note that GIS tools operate both 2D and 3D.
Data
Collected data package
https://drive.google.com/drive/folders/1-oa5A6Mj87zPMEFMviSMr9DVzEGWPebX?usp=sharing
Technology
You can consider and try out the below examples and technologies. However, you’re not limited to use them at all, or only them. You should explore different approaches and find technology that supports your idea.
IBM DSX / Machine Learning
http://heidloff.net/article/watson-machine-learning-sample
https://datascience.ibm.com/community
Watson APIs
https://www.ibm.com/watson/developercloud/doc/index.html
https://www.ibm.com/watson/developercloud/starter-kits.html
Watson Visual Recognition
https://github.com/IBM-Bluemix/Visual-Recognition-Tile-Localization
ArcGIS Online of Xamk
Hackathon groups are granted an account for ArcGIS Online GIS platform. Your account has professional map authoring, publishing and analysis capabilities and rights.
A username and a password for the first login are provided per group by the staff.
Do NOT try to sign in here: http://www.arcgis.com/
The SIGN IN address is https://xamk.maps.arcgis.com/
Esri ArcGIS Desktop
You can use a trial desktop installation.
http://www.esri.com/arcgis/about-arcgis
Esri developer docs