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Triaging deforestation alerts: Clustering alerts for review

机译：对毁林警报进行分类：将警报聚类以进行审查

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Advances in the automatic detection of deforestation and forest disturbance now provides alerts on disturbances as small as 30×30m pixels on an 8 to 16 day update schedule. Global Land Analysis & Discovery (GLAD) Alerts, developed by the University of Maryland are publicly available from Global Forest Watch. These provide valuable new insights into near real-time forest canopy loss. For agencies tracking deforestation, this introduces a new challenge to deal with the large volume of alerts. A technique is presented to triage deforestation alerts. Neighbouring alerts are processed into clusters. Combining many adjacent and nearby disturbances into clusters results in fewer cases to investigate. Cases can be forwarded to a crowd of volunteers for a quick visual check before being sent to locals to investigate. Both the original alert coordinates and their cluster perimeters may be encoded in the same GeoJSON Feature Collection. This aids both visualization and coordinating the response. Clustered alerts add value to the original alerts to aid local decision support activities. A prototype is demonstrated using Bunjil Forest Watch, using alert data from Global Forest Watch and processing by Google Earth Engine.
机译：现在，在自动检测森林砍伐和森林干扰方面取得的进展可在8到16天的更新时间表上提供小至30×30m像素的干扰警报。由马里兰大学开发的全球土地分析与发现（GLAD）警报可从全球森林观察网公开获得。这些为近乎实时的林冠损失提供了有价值的新见解。对于跟踪森林砍伐的机构而言，这带来了应对大量警报的新挑战。提出了一种对毁林警报进行分类的技术。相邻警报被处理为群集。将许多相邻和附近的干扰合并为一组，可以减少调查的案例。可以将案例转发给大量志愿者进行快速的目视检查，然后再发送给当地人进行调查。原始警报坐标及其聚类边界都可以编码在同一GeoJSON Feature Collection中。这有助于可视化和协调响应。群集警报为原始警报增加了价值，以帮助本地决策支持活动。使用Bunjil Forest Watch，来自Global Forest Watch的警报数据以及Google Earth Engine的处理演示了一个原型。

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