Transform complex forest polygons into a digital rainforest twin with Open AreaSeals. This Amazon demo compares forest coverage across 2000, 2013, 2016 and 2020 to reveal where and how intact forest areas change.
For each time interval, thousands of polygons—including their internal holes—are converted using Polygon to GRID N0, with adaptive cell sizes from 100 m to 100 km. A shared spatial hierarchy makes the resulting information comparable across time and analytical scales.
Explore changes in forest extent, identify concentrations of loss and analyse spatial patterns through geoKPIs. New observations can be integrated into the existing framework without repeatedly rebuilding complex polygon overlays.
Turn environmental datasets into reusable spatial and temporal intelligence for monitoring, conservation and impact assessment—ready for BI, machine learning and AI.
overlay year 2020 to 2020 multi-polygons
Task
Tracking and determination of environmental influences on intact forest via geo-spacial KPIs.
Input
OLD GIS multi-polygon shape-files for each time period. (2000, 2013,2016, 2020)
Challenge
Complex local geo-spacial joins of the given polygons.
year 2000 multi-polygon
year 2013 multi-polygon
year 2016 multi-polygon
year 2020 multi-polygon
Solution
„one-Click“ conversion of OLD GIS polygons to NEW GIS unique geo-Clusters, Open AreaSeals (OAS) für instant BI, ML and AI processing.
year 2000 adaptive OAS GRID
year 2013 adaptive OAS GRID
year 2013 adaptive OAS GRID
year 2020 adaptive OAS GRID
Result
Easy, advanced data analytics with standard BI tools, e.g. MS Power BI, due to simple unique geo-spacial joins via Open AreaSeal CODES as unique geo-spacial identifiers. All as on-the-fly and closed-loop system, means any new input-data is up-dating the data framework instantly with out the need of pre-processing already given information once again.
Benefit - up-to-date geo-spacial KPIs
- -easier, time saving, recources saving
- more efficient and agile
- real-time capability for up-dating information