Processing archaeological sensor data recorded with drones using machine learning
Archaeological remote sensing is of paramount importance to face challenges of climate change, agricultural intensification, and landscape development that are threatening buried archaeological remains. Drones using state-of-the-art sensor techniques strongly impact the possibilities of archaeologists by enabling them to trace ground and subsurface human activity in the past. This innovative technology however also results in huge high-resolution and multimodal datasets. Therefore, there is an urgent need for computer-aided inspection of these complex datasets. DroneML aims to develop software that can rapidly screen multiple feature types and multiple input layers simultaneously, to enable rapid processing of large datasets for subsequent manual assessment of identified features. The research software that would be the result of DroneML will hugely facilitate the work of archaeologists, as well as widen research possibilities, both within the field of heritage as well as any other discipline making use of remote sensing.
The CoeusAI QGIS plugin is designed for exploration of multiband geospatial datasets. It lets the user iteratively train and retrain segmentation models in seconds. A combination of Deep learning and traditional machine learning is used, leveraging the best of both methods.
Pycoeus is a command-line tool and python package designed for exploration of multiband geospatial datasets. It lets the user iteratively train and retrain segmentation models in seconds. A combination of Deep learning and traditional machine learning is used, leveraging the best of both methods.