Page 687 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

REQ-05: Machine Learning and Deep Learning Infrastructure
Must include infrastructure for unsupervised classification (e.g., ISODATA, K-means++) and
deep learning (e.g., U-Net) for semantic segmentation, supported by high-performance
computing (e.g., GPU clusters). cities 4.8: Smart home/

REQ-06: Multisensor and Multitemporal Data Handling
Must process and fuse data from multiple sensors and across different time periods to enhance
robustness and accuracy.

REQ-07: Field Validation and Ground-Truth Integration
Must include mechanisms to collect and integrate ground-truth data (e.g., GPS surveys,
excavation records) to validate and refine predictions.

REQ-08: Data Storage, Processing, and Management
Must support scalable data storage and management of large volumes of remote sensing and
derived datasets.

REQ-09: Use of Established Software Tools
Should use established platforms such as ArcGIS Pro and Environment for Visualizing Images
(ENVI)/Interactive Data Language (IDL)for processing, modeling, and visualization.

REQ-10: Scalability and Adaptability
Should be scalable to larger study areas and adaptable to other desert or arid environments.

REQ-11: Multidisciplinary Expertise
Must involve a team with expertise in archaeology, geospatial analysis, and artificial intelligence
for system development and interpretation.

REQ-12: User Interface and Visualization Tools
Should include user-friendly visualization tools to support archaeologists and decision-makers
in interpreting results and guiding fieldwork.
REQ-13: Geophysical Validation Integration
Must support the incorporation of ground-truth geophysical data (e.g., GPR, magnetic survey)
to validate and enhance remote sensing-based predictions.

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