It is a project designed for the reconnaissance and monitoring of areas potentially affected by the invasive species Ailanthus altissima, making extensive use of freely available optical satellite imagery and cloud-based processing. The workflow integrates multispectral information from Sentinel-2, surface temperature from Landsat 8/9, and terrain descriptors derived from digital terrain models. Aalto provides the labeled training and control dataset, while Alvar is the Google Earth Engine script implementing the full detection and analysis pipeline.
Ciolfi, M., Badalamenti, E., Chiocchini, F., Lauteri, M., Pollegioni, P. (2025). Alvar Aalto, i.e. AiLanthus Visual Analysis and Recognition based on Ailanthus Altissima Labeled Training Objects [Software] [10.5281/zenodo.17672766].
Alvar Aalto, i.e. AiLanthus Visual Analysis and Recognition based on Ailanthus Altissima Labeled Training Objects
Emilio Badalamenti;
2025-11-01
Abstract
It is a project designed for the reconnaissance and monitoring of areas potentially affected by the invasive species Ailanthus altissima, making extensive use of freely available optical satellite imagery and cloud-based processing. The workflow integrates multispectral information from Sentinel-2, surface temperature from Landsat 8/9, and terrain descriptors derived from digital terrain models. Aalto provides the labeled training and control dataset, while Alvar is the Google Earth Engine script implementing the full detection and analysis pipeline.
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