DIGIFOREST - Digital Analytics and Robotics for Sustainable Forestry

如果我们能够在空间数据获取、组织和分析方面创造一场革命，并为林业经营者和企业提供有关其森林状况的最新、切实的信息，甚至是有关每棵树的信息，那会怎么样？我们相信，这将改善他们的监督，允许更准确的林分生长模型和木材产量的精确预测。它将消除何时需要间伐作业或何处有准备收割的树木的不确定性。它还可以使操作员自动计划他们的工作人员或设备应该部署在哪里。有了强大的（半）自主采伐能力，运营商最终实现了整个过程的自动化。它还可以更好地量化森林的碳固存--每棵树的碳估计不确定性很低。对树冠体积和树木直径的精确测量将提高碳信用计划的粒度。这可以为各国政府和政策制定者在决定碳抵消和碳农业等举措的政策时提供信息。在DIGIFOREST中，我们建议通过开发一个异构机器人团队来创建这样一个生态系统，以收集和更新这种原始的3D空间表示，构建大规模的森林地图，并将其提供给机器学习和空间AI，以语义分割和标记树木和地形。我们的机器人团队将是多样化的：我们将使用坚固的野外机器人以及更多的实验车辆。最雄心勃勃的是打算（半）自动化一个轻量级采伐可持续选择性采伐。该项目的进展将通过一系列雄心勃勃的实地试验来证明。随着林业和工业公司的明确参与，商业途径是现成的。一个1：15的视频总结了整个项目的野心和财团可以在这里查看：https://tinyurl.com/digiforest

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 theirstaff or equipmentshould 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