2026-08-28 04:00 UTCOriginal source2 min readUpdated: 2026-08-28 09:01 UTC

Learning Woody Clearing With Loss Alignment for Zero-Shot Regrowth and Woody Segmentation

arXiv:2608.26489v1 Announce Type: new Abstract: Detecting woody clearing is vital for managing biodiversity. Deep learning models can detect change in woody vegetation from bitemporal remote sensing imagery, however generated products may not meet end-user specifications due to unaligned loss definitions. Further limitations of deep learning models are the reliance on large datasets which can be difficult to attain for spatially rare and ambiguous events such as regrowth detection. In this work we train a model to detect woody change using bitemporal Sentinel-2 imagery consisting of 7 years' worth of annual imagery across the state of New South Wales, Australia. To align the objective of the model with end-user metrics, we introduce the loss scaling coefficient $\alpha$ which transforms the objective to optimize for specific $F_{\beta}$ scores. Introducing $\alpha$ was found to increase precision by 1.85x or recall by 1.12x. We propose input imagery augmentation and generation techniques that allow the woody change detection model to zero-shot transfer to regrowth and woody segmentation tasks. For woody segmentation, image generation techniques using activation maximization with low $\alpha$ values for stability and image generation techniques derived from handcrafted features utilizing a mosaic of clearing patches and artificial trees for contextual grounding were found to outperform prior woody segmentation works of the study area, reducing the overall error by up to 18.2%. For zero-shot woody regrowth, creating pseudo-post and prior images resulted in the model achieving an F1 score of 0.845, creating a foundation for future regrowth detection work.

SourcearXiv Computer VisionAuthor: Kal Backman, Jared Wood, Adam Roff

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[Submitted on 27 Aug 2026]

Title:Learning Woody Clearing With Loss Alignment for Zero-Shot Regrowth and Woody Segmentation

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Abstract:Detecting woody clearing is vital for managing biodiversity. Deep learning models can detect change in woody vegetation from bitemporal remote sensing imagery, however generated products may not meet end-user specifications due to unaligned loss definitions. Further limitations of deep learning models are the reliance on large datasets which can be difficult to attain for spatially rare and ambiguous events such as regrowth detection. In this work we train a model to detect woody change using bitemporal Sentinel-2 imagery consisting of 7 years' worth of annual imagery across the state of New South Wales, Australia. To align the objective of the model with end-user metrics, we introduce the loss scaling coefficient $\alpha$ which transforms the objective to optimize for specific $F_{\beta}$ scores. Introducing $\alpha$ was found to increase precision by 1.85x or recall by 1.12x. We propose input imagery augmentation and generation techniques that allow the woody change detection model to zero-shot transfer to regrowth and woody segmentation tasks. For woody segmentation, image generation techniques using activation maximization with low $\alpha$ values for stability and image generation techniques derived from handcrafted features utilizing a mosaic of clearing patches and artificial trees for contextual grounding were found to outperform prior woody segmentation works of the study area, reducing the overall error by up to 18.2%. For zero-shot woody regrowth, creating pseudo-post and prior images resulted in the model achieving an F1 score of 0.845, creating a foundation for future regrowth detection work.

Comments: Published in IEEE Transactions on Geoscience and Remote Sensing

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2608.26489 [cs.CV]

(or arXiv:2608.26489v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2608.26489

arXiv-issued DOI via DataCite (pending registration)

Journal reference: IEEE Transactions on Geoscience and Remote Sensing, vol. 64, pp. 1-18, 2026, Art no. 4406318

Related DOI:

https://doi.org/10.1109/TGRS.2026.3676347

DOI(s) linking to related resources

Submission history

From: Kal Backman [view email] [v1] Thu, 27 Aug 2026 00:24:19 UTC (28,156 KB)

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