3 papers
cs.CV2026
Multispectral Blind Image Super-Resolution for Standing Dead Tree Segmentation
Mete Ahishali, Anis Ur Rahman, Einari Heinaro +2
Mapping standing dead trees is crucial for acquiring information on the effects of climate change on forests and forest biodiversity. However, leveraging high-quality aerial imager…
cs.CV2025
ADA-Net: Attention-Guided Domain Adaptation Network with Contrastive Learning for Standing Dead Tree Segmentation Using Aerial Imagery
Mete Ahishali, Anis Ur Rahman, Einari Heinaro +1
Information on standing dead trees is important for understanding forest ecosystem functioning and resilience but has been lacking over large geographic regions. Climate change has…
cs.CV2025
Dual-Task Learning for Dead Tree Detection and Segmentation with Hybrid Self-Attention U-Nets in Aerial Imagery
Anis Ur Rahman, Einari Heinaro, Mete Ahishali +1
Mapping standing dead trees is critical for assessing forest health, monitoring biodiversity, and mitigating wildfire risks, for which aerial imagery has proven useful. However, de…