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GEOMATRIX UAB has developed a series of automated processing work-flows for practical applications: (1) change detection (based on entropy differencing) to detect presence or movements of small objects within a time series of very high resolution optical imagery; (2) automated processing of LiDAR data, originally developed for the forest monitoring projects and does point-cloud filtering, classification, creation of DTM and DSM, detection  of objects and change detection of surface objects on different statistical scales; (3) automated production of large amounts of location-based digital maps with pre-defined information layers and scales, including geostatistical processing and statistical filtering capabilities, to produce thousands of maps for operational monitoring of certain hot-spots. The company has perfected it's skills in “extreme” geo-computing on a limited hardware. Automated pre-processing of large amount of satellite imagery is a heavy computing task, which are automated and boosted up very significantly by automated processing algorithms developed by our company. Another heavy processing task critical for very high resolution imagery is object-based classification (segmentation). Computing and analysis of a time series of vegetation or geophysical indexes, surface water masks, etc. can provide useful information on rapidly changing environmental conditions, as well as availability of nutrition basis and life support components – or emerging natural hazards – over large areas on the operational basis. The current technological developments are mostly related to the new generation of ESA satellites – Sentinels 1/2/3, Landsat 8 and new generation SPOT 7. We are actively testing automated land cover  classification based on Sentinel-1 SAR interferometric datasets, working on very dense time series on pan-European scale. With advanced image fusion and statistical trending techniques we try to complement high-density SAR time series with biophysical information from various optical imagery. Change detection within SAR time series will be another focus for the technological development in the near future with potential applications in agriculture, forestry, climate change, but also intelligence gathering.