System and Method for Identification of Archaeological Features Using Remotely Sensed Data
Summary
The USPTO published patent application US20260092777A1 for an AI-driven system designed to identify gravesites and archaeological features through remote sensing and machine learning. Invented by Nicholas A. Kuncewicz, the technology integrates multimodal data including spectral imagery and LiDAR to detect and classify features. No compliance obligations arise from this patent application publication.
What changed
The USPTO published patent application US20260092777A1 (filed October 1, 2025) for a system and method to detect gravesites and archaeological features non-invasively using multimodal remote sensing and machine learning. The system processes RGB, multispectral, hyperspectral, LiDAR, and thermal imagery through orthorectification and tiling, then trains computer vision models (YOLO-based detectors) alongside tabular models derived from spectral indices (NDVI, NDRE), LiDAR elevation derivatives, and thermal anomalies. Inferences are cross-validated against thresholded evidence layers to reject false positives, with validated detections exported as GIS-compatible layers with confidence scores and metadata.
This patent application does not impose compliance obligations on any party. Technology companies developing AI systems for archaeological, cultural resource management, or humanitarian search applications may be interested in the disclosed methodology. Academic institutions and nonprofits conducting cemetery investigations or unmarked grave searches may reference this application for prior art purposes.
Archived snapshot
Apr 2, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
SYSTEM AND METHOD FOR IDENTIFICATION OF ARCHEOLOGICAL FEATURES USING REMOTELY SENSED DATA
Application US20260092777A1 Kind: A1 Apr 02, 2026
Inventors
Nicholas A. Kuncewicz
Abstract
This invention relates to a system and method for non-invasive detection of gravesites and archaeological features using multimodal remote sensing and machine learning. Remotely sensed datasets, including RGB, multispectral, hyperspectral, LiDAR, and thermal imagery, are orthorectified, mosaicked, and subdivided into tiled image segments. Features are labeled through manual annotation of visible markers and environmental signatures and expanded via iterative augmentation. A supervised pipeline trains computer vision models, such as YOLO-based detectors, in parallel with tabular models derived from spectral indices (NDVI, NDRE), LiDAR elevation derivatives, and thermal anomalies. Inference outputs are cross-validated against thresholded evidence layers to reject false positives and upgraded when spectral, spatial, and thermal evidence align. Validated detections are exported as GIS-compatible layers with confidence scores and metadata. The system provides a scalable, replicable tool supporting archaeologists, Indigenous communities, and planners in cemetery investigations, cultural resource management, and humanitarian searches for unmarked or clandestine graves.
CPC Classifications
G01C 15/00 G06N 20/00
Filing Date
2025-10-01
Application No.
19347382
Named provisions
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