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Cross-Domain Dead Tree Detection via Knowledge Distillation in Aerial Imagery
Anis Ur Rahman, Mete Ahishali, Einari Heinaro +1
Detecting dead trees in aerial imagery is vital for assessing forest health, especially as tree mortality increases globally due to climate change, but domain variability and scarc…
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…
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…
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…