Triaging Deforestation Alerts – Clustering alerts for review
Goodman, Chris. Triaging Deforestation Alerts – Clustering alerts for review, IEEE Global Humanitarian Technology Conference (GHTC), Seattle, October 2016.
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Advances in the automatic detection of deforestation and forest disturbance now provides pan-tropical data down to 30x30m pixels on an 8 to 16-day update schedule. Global Land Analysis & Discovery (GLAD) Alerts, available from Global Forest Watch provide valuable new insights into near real-time forest canopy change. This introduces a new challenge for agencies to deal with the large volume of alerts. A technique is presented to triage deforestation alerts. Initial processing uses clustering algorithms from data mining and pattern recognition applications to group the alerts. By combining many adjacent and nearby disturbances into clusters leaving fewer cases to investigate. The cases are forwarded to a crowd of volunteers for a quick visual check before being sent to locals. Both the original alert coordinates and their cluster perimeter may be encoded in the same GeoJson feature collection. This aids both visualization and coordinating and recording the response. A prototype is demonstrated based on Google Earth Engine and real-time data from Global Forest Watch.
Note: At the time of publication Earth Engine did not have support for clustering algorithms. This paper describes a technique that used vector-to-raster conversion to achieve clustering indirectly. Earth Engine now directly supports clustering algorithms including DBSCAN. Using DBSCAN would simplify the computation.
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