DeepTrees Symposium: Deep Learning and Remote Sensing in Tree Monitoring

Session

Deeptrees updates

2 Dec 2024, 09:30

Halle 1 CD (KUBUS)

Description

This session will focus on recent advancements in DeepTrees, including updates on workflows, tools, and methodology improvements. Talks that highlight:

• Innovations in deep learning models for tree detection and monitoring.
• Workflow standardization and methods for aggregating tree data.
• New findings in tree allometry and tree class analysis using DeepTrees tools.

7. Challenges in Crown Spread and DBH Estimation from Segmented Tree Crown Polygons

Session 1

Tree traits such as crown area (CA), crown radius (CR), and diameter at breast height (DBH) provide insight into a tree’s growth dynamics, biomass, and the overall biophysical and biochemical processes of ecosystems. Accurately measuring these traits is essential for carbon estimation, urban tree management, and biodiversity assessments. The allometric relationships between CA, CR, and DBH are...

6. DeepTrees: Deep-Learning based spatiotemporal tree inventorying from public orthoimages

Session 1

Field-based methods for tree inventorying are time-consuming, labour-intensive, and often expensive. With the advent of remote sensing technologies and deep learning, it is now possible to leverage digital public orthoimages and artificial intelligence to automate and enhance the tree inventorying process [1].

The DeepTrees project aims to develop and implement deep learning models for...

3. Fine-tuning and evaluating deep learning models for tree crown segmentation

Session 2

The DeepTree project uses deep learning models to segment tree crowns in orthoimages. Building on pretrained models [1], we fine tune them with labeled orthoimages from the city of Halle (Saale), Germany. We present a deep learning model that is optimized for segmenting tree crowns in orthoimages from this region.
The deep learning model is evaluated based on the city’s registry of trees [2]...