HELP4i
FROM GEO4i

A stand-alone decision support system for the identification of military equipment in satellite images

Advancing equipment indentification

Highly Compatible

Syncs seamlessly with the GeoSpace platform or via plugin with ArcGIS and QGIS

Extensive reference library

Ever-growing and updated equipment database with over 6,000 references

Intuitive Interface

Quickly search the database and drag reference images directly over your imagery

Help4i is a stand-alone automatic object detection tool developed by Geo4i.  With over 6,000 references and plugin compatibility with ArcGIS and QGIS, as well as Geo4i’s GeoSpace platform, the tool provides identification of military equipment from satellite imagery, with manual inputs such as length and width. Each image suggestion comes with a top view that can be dynamically compared with the image for 100% verification confidence.

EXPERIENCE HELP4i YOURSELF

Contact our sales team for a live demonstration of this powerful tool.

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COMBINE HELP4i WITH GEOSPACE

Geo4i’s GeoSpace is a dedicated GEOINT & IMINT platform for processing and analysing geospatial imagery and Big Data. The platform is NATO STANAG 3569 compliant featuring automated detection workflows and other value-added tools that allows for reporting and analysis. 

 

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Architecture of ResNet34-UNet model

UNet architecture for semantic segmentation with ResNet34 as encoder or feature extraction part. ResNet34 is used as an encoder or feature extractor in the contracting path and the corresponding symmetric expanding path predicts the dense segmentation output.

Architecture of VGG16-UNet model

UNet architecture for semantic segmentation with VGG16 as the encoder or feature extractor. VGG16 is used as an encoder or feature extractor in the contracting path and the corresponding symmetric expanding path predicts the dense segmentation output.

Architecture of ResNet34-FCN model

In this model, ResNet34 is used for feature extraction and the FCN operation remains as is. The feature of ResNet architecture is exploited where just like VGG, as the number of filters double, the feature map size gets halved. This gives a similarity to VGG and ResNet architecture while supporting deeper architecture and addressing the issue of vanishing gradients while also being faster. The fully connected layer at the output of ResNet34 is not used and instead converted to fully convolutional layer by means of 1×1 convolution.

Architecture of VGG16-FCN model

In this model, VGG16 is used for feature extraction which also performs the function of an encoder. The fully connected layer of the VGG16 is not used and instead converted to fully convolutional layer by means of 1×1 convolution.

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