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zeekayandHanzo Dev dced1dcfb9 Publish under the org, not a person
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Co-authored-by: Hanzo Dev <[email protected]>
2026-09-01 08:55:40 -07:00

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\section{Conclusion}
\label{sec:conclusion}
We have presented ZooSat, a deep learning pipeline for continuous satellite-based ecological monitoring.
The Temporal Siamese Network achieves 94.2\% change detection accuracy and 87.8\% change type classification by leveraging temporal sequence modeling to suppress phenological and atmospheric confounders.
The Habitat Fragmentation Index provides a real-time, field-validated measure of habitat connectivity degradation ($r = 0.87$ with species richness change).
The Conservation Alert System delivers priority-ranked alerts 11.2 days faster than existing global monitoring systems.
An 18-month operational deployment across 47 protected areas in 14 countries generated 12,847 alerts (89.3\% true positive rate), supported 47 enforcement actions, and contributed to an estimated 4,200 hectares of prevented forest loss.
ZooSat demonstrates that deep learning applied to freely available Sentinel-2 imagery can provide conservation-grade ecological monitoring at a cost of \$0.003 per km$^2$ per month.
The system is deployed as open infrastructure on the Zoo Network and specified as Zoo Improvement Proposal ZIP-010.
Future work will extend the pipeline to incorporate radar imagery (Sentinel-1) for all-weather detection, integrate species distribution models for species-specific fragmentation assessment, and develop predictive models that forecast future deforestation fronts based on spatial and temporal patterns.
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% References
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