DeepTrees Symposium: Deep Learning and Remote Sensing in Tree Monitoring

Session

Results from the community

2 Dec 2024, 10:45

Halle 1 CD (KUBUS)

Description

This session will highlight community-driven results, with a particular focus on how researchers, city planners, and environmentalists are their own work. We encourage submissions for:

• Case studies in urban forestry, biodiversity, or ecosystem management.
• Results of studies involving remote sesing and or machine learning for tree/forest monitoring.
• Comparative analyses of different deep learning models (e.g., CNN, UNet) used for tree monitoring.

1. deadtrees.earth - A Database of Centimeter-Scale Aerial Imagery as Reference Data for Mapping Global Tree Mortality

Session 2

Today, excessive tree mortality is a global concern. Accurate monitoring for ecological insights and environmental predictions is urgently required. Earth observation data, combined with supervised machine learning, offer a promising approach to map tree mortality over time.
However, global-scale machine learning requires broad training data covering a wide range of conditions and forest...

5. Integrating UAV Imagery and LiDAR Data for Tree Structural Properties

Session 2

Forests cover approximately 31% of the global land area and are essential for biodiversity, supporting 80% of terrestrial species. However, these ecosystems are vulnerable to human-driven climate change, necessitating automated systems to monitor structural changes at the individual tree level and assess forest responses to climate anomalies. UAV-based tree canopy detection is valuable for...

2. SELF-SUPERVISED BACKBONES FOR FOREST REMOTE SENSING

Session 2

Self-Supervised Learning (SSL) presents an opportunity to leverage large volumes of unlabeled data to improve outcomes for tasks for which training data is limited. This is the case in forest remote sensing where high quality annotated datasets are often too small to fully take advantage of data hungry deep learning models. Recognizing this, we have pre-trained two classes of neural network...

10. The UFZ forest monitor

Session 2

The UFZ Forest Condition Monitor is a project funded by the Helmholtz Association within the [Land Cover & Dynamics Working Group][1] of the [Remote Sensing Department][2] at the [Helmholtz Centre for Environmental Research - UFZ][3] in Leipzig. The UFZ Forest Condition Monitor provides (remote sensing-based) comprehensive annual/seasonal derivations of forest condition continuously since...